2019 Theses Doctoral

A Three-Paper Dissertation on Longitudinal Data Analysis in Education and Psychology

Ahmadi, Hedyeh

In longitudinal settings, modeling the covariance structure of repeated measure data is essential for proper analysis. The first paper in this three-paper dissertation presents a survey of four journals in the fields of Education and Psychology to identify the most commonly used methods for analyzing longitudinal data. It provides literature reviews and statistical details for each identified method. This paper also offers a summary table giving the benefits and drawbacks of all the surveyed methods in order to help researchers choose the optimal model according to the structure of their data. Finally, this paper highlights that even when scholars do use more advanced methods for analyzing repeated measure data, they very rarely report (or explore in their discussions) the covariance structure implemented in their choice of modeling. This suggests that, at least in some cases, researchers may not be taking advantage of the optimal covariance patterns. This paper identifies a gap in the standard statistical practices of the fields of Education and Psychology, namely that researchers are not modeling the covariance structure as an extension of fixed/random effects modeling. The second paper introduces the General Serial Covariance (GSC) approach, an extension of the Linear Mixed Modeling (LMM) or Hierarchical Linear Model (HLM) techniques that models the covariance structure using spatial correlation functions such as Gaussian, Exponential, and other patterns. These spatial correlations model the covariance structure in a continuous manner and therefore can deal with missingness and imbalanced data in a straightforward way. A simulation study in the second paper reveals that when data are consistent with the GSC model, using basic HLMs is not optimal for the estimation and testing of the fixed effects. The third paper is a tutorial that uses a real-world data set from a drug abuse prevention intervention to demonstrate the use of the GSC and basic HLM models in R programming language. This paper utilizes variograms (a visualization tool borrowed from geostatistics) among other exploratory tools to determine the covariance structure of the repeated measure data. This paper aims to introduce the GSC model and variogram plots to Education and Psychology, where, according to the survey in the first paper, they are not in use. This paper can also help scholars seeking guidance for interpreting the fixed effect-parameters.

  • Longitudinal method--Statistical methods
  • Analysis of covariance
  • Education--Research

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Article Contents

Questions on conceptual issues, questions on research design, questions on statistical techniques, acknowledgments, longitudinal research: a panel discussion on conceptual issues, research design, and statistical techniques.

All authors contributed equally to this article and the order of authorship is arranged arbitrarily. Correspondence concerning this article should be addressed to Mo Wang, Warrington College of Business, Department of Management, University of Florida, Gainesville, FL 32611. E-mail: [email protected]

Decision Editor: Donald Truxillo, PhD

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Mo Wang, Daniel J. Beal, David Chan, Daniel A. Newman, Jeffrey B. Vancouver, Robert J. Vandenberg, Longitudinal Research: A Panel Discussion on Conceptual Issues, Research Design, and Statistical Techniques, Work, Aging and Retirement , Volume 3, Issue 1, 1 January 2017, Pages 1–24, https://doi.org/10.1093/workar/waw033

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The goal of this article is to clarify the conceptual, methodological, and practical issues that frequently emerge when conducting longitudinal research, as well as in the journal review process. Using a panel discussion format, the current authors address 13 questions associated with 3 aspects of longitudinal research: conceptual issues, research design, and statistical techniques. These questions are intentionally framed at a general level so that the authors could address them from their diverse perspectives. The authors’ perspectives and recommendations provide a useful guide for conducting and reviewing longitudinal studies in work, aging, and retirement research.

An important meta-trend in work, aging, and retirement research is the heightened appreciation of the temporal nature of the phenomena under investigation and the important role that longitudinal study designs play in understanding them (e.g., Heybroek, Haynes, & Baxter, 2015 ; Madero-Cabib, Gauthier, & Le Goff, 2016 ; Wang, 2007 ; Warren, 2015 ; Weikamp & Göritz, 2015 ). This echoes the trend in more general research on work and organizational phenomena, where the discussion of time and longitudinal designs has evolved from explicating conceptual and methodological issues involved in the assessment of changes over time (e.g., McGrath & Rotchford, 1983 ) to the development and application of data analytic techniques (e.g., Chan, 1998 ; Chan & Schmitt, 2000 ; DeShon, 2012 ; Liu, Mo, Song, & Wang, 2016 ; Wang & Bodner, 2007 ; Wang & Chan, 2011 ; Wang, Zhou, & Zhang, 2016 ), theory rendering (e.g., Ancona et al. , 2001 ; Mitchell & James, 2001 ; Vancouver, Tamanini, & Yoder, 2010 ; Wang et al. , 2016 ), and methodological decisions in conducting longitudinal research (e.g., Beal, 2015 ; Bolger, Davis, & Rafaeli, 2003 ; Ployhart & Vandenberg, 2010 ). Given the importance of and the repeated call for longitudinal studies to investigate work, aging, and retirement-related phenomena (e.g., Fisher, Chaffee, & Sonnega, 2016 ; Wang, Henkens, & van Solinge, 2011 ), there is a need for more nontechnical discussions of the relevant conceptual and methodological issues. Such discussions would help researchers to make more informed decisions about longitudinal research and to conduct studies that would both strengthen the validity of inferences and avoid misleading interpretations.

In this article, using a panel discussion format, the authors address 13 questions associated with three aspects of longitudinal research: conceptual issues, research design, and statistical techniques. These questions, as summarized in Table 1 , are intentionally framed at a general level (i.e., not solely in aging-related research), so that the authors could address them from diverse perspectives. The goal of this article is to clarify the conceptual, methodological, and practical issues that frequently emerge in the process of conducting longitudinal research, as well as in the related journal review process. Thus, the authors’ perspectives and recommendations provide a useful guide for conducting and reviewing longitudinal studies—not only those dealing with aging and retirement, but also in the broader fields of work and organizational research.

Questions Regarding Longitudinal Research Addressed in This Article

Conceptual issues1. Conceptually, what is the essence of longitudinal research?
2. What is the status of “time” in longitudinal research? Is “time” a general notion of the temporal dynamics in phenomena, or is “time” a substantive variable similar to other focal variables in the longitudinal study?
3. What are the procedures, if any, for developing a theory of changes over time in longitudinal research? Given that longitudinal research purportedly addresses the limitations of cross-sectional research, can findings from cross-sectional studies be useful for the development of a theory of change?
Research design1. What are some of the major considerations that one should take into account before deciding to employ a longitudinal study design?
2. Are there any design advantages of cross-sectional research that might make it preferable to longitudinal research? That is, what would be lost and what might be gained if a moratorium were placed on cross- sectional research?
3. In a longitudinal study, how do we decide on the length of the interval between two adjacent time points?
4. As events occur in our daily life, our mental representations of these events may change as time passes. How can we determine the point(s) in time at which the representation of an event is appropriate? How can these issues be addressed through design and measurement in a study?
5. What are the biggest practical hurdles to conducting longitudinal research? What are the ways to overcome them?
Statistical techniques1. With respect to assessing changes over time in a latent growth modeling framework, how can a researcher address different conceptual questions by coding the slope variable differently?
2. In longitudinal research, are there additional issues of measurement error that we need to pay attention to, which are over and above those that are applicable to cross-sectional research?
3. When analyzing longitudinal data, how should we handle missing values?
4. Most of existing longitudinal research focuses on studying quantitative change over time. What if the variable of interest is categorical or if the changes over time are qualitative in nature?
5. Could you speculate on the “next big thing” in conceptual or methodological advances in longitudinal research? Specifically, describe a novel idea or specific data analytic model that is rarely used in longitudinal studies in our literature, but could serve as a useful conceptual or methodological tool for future science in work, aging and retirement.
Conceptual issues1. Conceptually, what is the essence of longitudinal research?
2. What is the status of “time” in longitudinal research? Is “time” a general notion of the temporal dynamics in phenomena, or is “time” a substantive variable similar to other focal variables in the longitudinal study?
3. What are the procedures, if any, for developing a theory of changes over time in longitudinal research? Given that longitudinal research purportedly addresses the limitations of cross-sectional research, can findings from cross-sectional studies be useful for the development of a theory of change?
Research design1. What are some of the major considerations that one should take into account before deciding to employ a longitudinal study design?
2. Are there any design advantages of cross-sectional research that might make it preferable to longitudinal research? That is, what would be lost and what might be gained if a moratorium were placed on cross- sectional research?
3. In a longitudinal study, how do we decide on the length of the interval between two adjacent time points?
4. As events occur in our daily life, our mental representations of these events may change as time passes. How can we determine the point(s) in time at which the representation of an event is appropriate? How can these issues be addressed through design and measurement in a study?
5. What are the biggest practical hurdles to conducting longitudinal research? What are the ways to overcome them?
Statistical techniques1. With respect to assessing changes over time in a latent growth modeling framework, how can a researcher address different conceptual questions by coding the slope variable differently?
2. In longitudinal research, are there additional issues of measurement error that we need to pay attention to, which are over and above those that are applicable to cross-sectional research?
3. When analyzing longitudinal data, how should we handle missing values?
4. Most of existing longitudinal research focuses on studying quantitative change over time. What if the variable of interest is categorical or if the changes over time are qualitative in nature?
5. Could you speculate on the “next big thing” in conceptual or methodological advances in longitudinal research? Specifically, describe a novel idea or specific data analytic model that is rarely used in longitudinal studies in our literature, but could serve as a useful conceptual or methodological tool for future science in work, aging and retirement.

Conceptual Issue Question 1: Conceptually, what is the essence of longitudinal research?

This is a fundamental question to ask given the confusion in the literature. It is common to see authors attribute their high confidence in their causal inferences to the longitudinal design they use. It is also common to see authors attribute greater confidence in their measurement because of using a longitudinal design. Less common, but with increasing frequency, authors claim to be examining the role of time in their theoretical models via the use of longitudinal designs. These different assumptions by authors illustrate the need for clarifying when specific attributions about longitudinal research are appropriate. Hence, a discussion of the essence of longitudinal research and what it provides is in order.

Oddly, definitions of longitudinal research are rare. One exception is a definition by Taris (2000) , who explained that longitudinal “data are collected for the same set of research units (which might differ from the sampling units/respondents) for (but not necessarily at) two or more occasions, in principle allowing for intra-individual comparison across time” (pp. 1–2). Perhaps more directly relevant for the current discussion of longitudinal research related to work and aging phenomena, Ployhart and Vandenberg (2010) defined “ longitudinal research as research emphasizing the study of change and containing at minimum three repeated observations (although more than three is better) on at least one of the substantive constructs of interest” (p. 97; italics in original). Compared to Taris (2000) , Ployhart and Vandenberg’s (2010) definition explicitly emphasizes change and encourages the collection of many waves of repeated measures. However, Ployhart and Vandenberg’s definition may be overly restrictive. For example, it precludes designs often classified as longitudinal such as the prospective design. In a prospective design, some criterion (i.e., presumed effect) is measured at Times 1 and 2, so that one can examine change in the criterion as a function of events (i.e., presumed causes) happening (or not) between the waves of data collection. For example, a researcher can use this design to assess the psychological and behavioral effects of retirement that occur before and after retirement. That is, psychological and behavioral variables are measured before and after retirement. Though not as internally valid as an experiment (which is not possible because we cannot randomly assign participants into retirement and non-retirement conditions), this prospective design is a substantial improvement over the typical design where the criteria are only measured at one time. This is because it allows one to more directly examine change in a criterion as a function of differences between events or person variables. Otherwise, one must draw inferences based on retrospective accounts of the change in criterion along with the retrospective accounts of the events; further, one may worry that the covariance between the criterion and person variables is due to changes in the criterion that are also changing the person. Of course, this design does not eliminate the possibility that changes in criterion may cause differences in events (e.g., changes observed in psychological and behavioral variables lead people to decide to retire).

In addition to longitudinal designs potentially having only two waves of data collection for a variable, there are certain kinds of criterion variables that need only one explicit measure at Time 2 in a 2-wave study. Retirement (or similarly, turnover) is an example. I say “explicit” because retirement is implicitly measured at Time 1. That is, if the units are in the working sample at Time 1, they have not retired. Thus, retirement at Time 2 represents change in working status. On the other hand, if retirement intentions is the criterion variable, repeated measures of this variable are important for assessing change. Repeated measures also enable the simultaneous assessment of change in retirement intentions and its alleged precursors; it could be that a variable like job satisfaction (a presumed cause of retirement intentions) is actually lowered after the retirement intentions are formed, perhaps in a rationalization process. That is, individuals first intend to retire and then evaluate over time their attitudes toward their present job. This kind of reverse causality process would not be detected in a design measuring job satisfaction at Time 1 and retirement intentions at Time 2.

Given the above, I opt for a much more straightforward definition of longitudinal research. Specifically, longitudinal research is simply research where data are collected over a meaningful span of time. A difference between this definition and the one by Taris (2000) is that this definition does not include the clause about examining intra-individual comparisons. Such designs can examine intra-individual comparisons, but again, this seems overly restrictive. That said, I do add a restriction to this definition, which is that the time span should be “meaningful.” This term is needed because time will always pass—that is, it takes time to complete questionnaires, do tasks, or observe behavior, even in cross-sectional designs. Yet, this passage of time likely provides no validity benefit. On the other hand, the measurement interval could last only a few seconds and still be meaningful. To be meaningful it has to support the inferences being made (i.e., improve the research’s validity). Thus, the essence of longitudinal research is to improve the validity of one’s inferences that cannot otherwise be achieved using cross-sectional research ( Shadish, Cook, & Campbell, 2002 ). The inferences that longitudinal research can potentially improve include those related to measurement (i.e., construct validity), causality (i.e., internal validity), generalizability (i.e., external validity), and quality of effect size estimates and hypothesis tests (i.e., statistical conclusion validity). However, the ability of longitudinal research to improve these inferences will depend heavily on many other factors, some of which might make the inferences less valid when using a longitudinal design. Increased inferential validity, particularly of any specific kind (e.g., internal validity), is not an inherent quality of the longitudinal design; it is a goal of the design. And it is important to know how some forms of the longitudinal design fall short of that goal for some inferences.

For example, consider a case where a measure of a presumed cause precedes a measure of a presumed effect, but over a time period across which one of the constructs in question does not likely change. Indeed, it is often questionable as to whether a gap of several months between the observations of many variables examined in research would change meaningfully over the interim, much less that the change in one preceded the change in the other (e.g., intention to retire is an example of this, as people can maintain a stable intention to retire for years). Thus, the design typically provides no real improvement in terms of internal validity. On the other hand, it does likely improve construct and statistical conclusion validity because it likely reduces common method bias effects found between the two variables ( Podsakoff et al., 2003 ).

Further, consider the case of the predictive validity design, where a selection instrument is measured from a sample of job applicants and performance is assessed some time later. In this case, common method bias is not generally the issue; external validity is. The longitudinal design improves external validity because the Time 1 measure is taken during the application process, which is the context in which the selection instrument will be used, and the Time 2 measure is taken after a meaningful time interval (i.e., after enough time has passed for performance to have stabilized for the new job holders). Again, however, internal validity is not much improved, which is fine given that prediction, not cause, is the primary concern in the selection context.

Another clear construct validity improvement gained by using longitudinal research is when one is interested in measuring change. A precise version of change measurement is assessing rate of change. When assessing the rate, time is a key variable in the analysis. To assess a rate one needs only two repeated measures of the variable of interest, though these measures should be taken from several units (e.g., individuals, groups, organizations) if measurement and sampling errors are present and perhaps under various conditions if systematic measurement error is possible (e.g., testing effect). Moreover, Ployhart and Vandenberg (2010) advocate at least three repeated measures because most change rates are not constant; thus, more than two observations will be needed to assess whether and how the rate changes (i.e., the shape of the growth curves). Indeed, three is hardly enough given noise in measurement and the commonality of complex processes (i.e., consider the opponent process example below).

Longitudinal research designs can, with certain precautions, improve one’s confidence in inferences about causality. When this is the purpose, time does not need to be measured or included as a variable in the analysis, though the interval between measurements should be reported because rate of change and cause are related. For example, intervals can be too short, such that given the rate of an effect, the cause might not have had sufficient time to register on the effect. Alternatively, if intervals are too long, an effect might have triggered a compensating process that overshoots the original level, inverting the sign of the cause’s effect. An example of this latter process is opponent process ( Solomon & Corbit, 1974 ). Figure 1 depicts this process, which refers to the response to an emotional stimulus. Specifically, the emotional response elicits an opponent process that, at its peak, returns the emotion back toward the baseline and beyond. If the emotional response is collected when peak opponent response occurs, it will look like the stimulus is having the opposite effect than it actually is having.

The opponent process effect demonstrated by Solomon and Corbit (1974).

The opponent process effect demonstrated by Solomon and Corbit (1974) .

Most of the longitudinal research designs that improve internal validity are quasi-experimental ( Shadish et al. , 2002 ). For example, interrupted time series designs use repeated observations to assess trends before and after some manipulation or “natural experiment” to model possible maturation or maturation-by-selection effects ( Shadish et al. , 2002 ; Stone-Romero, 2010 ). Likewise, regression discontinuous designs (RDD) use a pre-test to assign participants to the conditions prior to the manipulation and thus can use the pre-test value to model selection effects ( Shadish et al. , 2002 ; Stone-Romero, 2010 ). Interestingly, the RDD design is not assessing change explicitly and thus is not susceptible to maturations threats, but it uses the timing of measurement in a meaningful way.

Panel (i.e., cohort) designs are also typically considered longitudinal. These designs measure all the variables of interest during each wave of data collection. I believe it was these kinds of designs that Ployhart and Vandenberg (2010) had in mind when they created their definition of longitudinal research. In particular, these designs can be used to assess rates of change and can improve causal inferences if done well. In particular, to improve causal inferences with panel designs, researchers nearly always need at least three repeated measures of the hypothesized causes and effects. Consider the case of job satisfaction and intent to retire. If a researcher measures job satisfaction and intent to retire at Times 1 and 2 and finds that the Time 2 measures of job satisfaction and intent to retire are negatively related when the Time 1 states of the variables are controlled, the researcher still cannot tell which changed first (or if some third variable causes both to change in the interim). Unfortunately, three observations of each variable is only a slight improvement because it might be a difficult thing to get enough variance in changing attitudes and changing intentions with just three waves to find anything significant. Indeed, the researcher might have better luck looking at actual retirement, which as mentioned, only needs one observation. Still, two observations of job satisfaction are needed prior to the retirement to determine if changes in job satisfaction influence the probability of retirement.

Finally, on this point I would add that meaningful variance in time will often mean case-intensive designs (i.e., lots of observations of lots of variables over time per case; Bolger & Laurenceau, 2013 ; Wang et al. , 2016 ) because we will be more and more interested in assessing feedback and other compensatory processes, reciprocal relationships, and how dynamic variables change. In these cases, within-unit covariance will be much more interesting than between-unit covariance.

It is important to point out that true experimental designs are also a type of longitudinal research design by nature. This is because in experimental design, an independent variable is manipulated before the measure of the dependent variable occurs. This time precedence (or lag) is critical for using experimental designs to achieve stronger causal inferences. Specifically, given that random assignment is used to generate experimental and control groups, researchers can assume that prior to the manipulation, the mean levels of the dependent variables are the same across experimental and control groups, as well as the mean levels of the independent variables. Thus, by measuring the dependent variable after manipulation, an experimental design reveals the change in the dependent variable as a function of change in the independent variable as a result of manipulation. As such, the time lag between the manipulation and the measure of the dependent variable is indeed meaningful in the sense of achieving causal inference.

Conceptual Issue Question 2: What is the status of “time” in longitudinal research? Is “time” a general notion of the temporal dynamics in phenomena, or is “time” a substantive variable similar to other focal variables in the longitudinal study?

In longitudinal research, we are concerned with conceptualizing and assessing the changes over time that may occur in one or more substantive variables. A substantive variable refers to a measure of an intended construct of interest in the study. For example, in a study of newcomer adaptation (e.g., Chan & Schmitt, 2000 ), the substantive variables, whose changes over time we are interested in tracking, could be frequency of information seeking, job performance, and social integration. We could examine the functional form of the substantive variable’s change trajectory (e.g., linear or quadratic). We could also examine the extent to which individual differences in a growth parameter of the trajectory (e.g., the individual slopes of a linear trajectory) could be predicted from the initial (i.e., at Time 1 of the repeated measurement) values on the substantive variable, the values on a time-invariant predictor (e.g., personality trait), or the values on another time-varying variable (e.g., individual slopes of the linear trajectory of a second substantive variable in the study). The substantive variables are measures used to represent the study constructs. As measures of constructs, they have specific substantive content. We can assess the construct validity of the measure by obtaining relevant validity evidence. The evidence could be the extent to which the measure’s content represents the conceptual content of the construct (i.e., content validity) or the extent to which the measure is correlated with another established criterion measure representing a criterion construct that, theoretically, is expected to be associated with the measure (i.e., criterion-related validity).

“Time,” on the other hand, has a different ontological status from the substantive variables in the longitudinal study. There are at least three ways to describe how time is not a substantive variable similar to other focal variables in the longitudinal study. First, when a substantive construct is tracked in a longitudinal study for changes over time, time is not a substantive measure of a study construct. In the above example of newcomer adaptation study by Chan and Schmitt, it is not meaningful to speak of assessing the construct validity of time, at least not in the same way we can speak of assessing the construct validity of job performance or social integration measures. Second, in a longitudinal study, a time point in the observation period represents one temporal instance of measurement. The time point per se, therefore, is simply the temporal marker of the state of the substantive variable at the point of measurement. The time point is not the state or value of the substantive variable that we are interested in for tracking changes over time. Changes over time occur when the state of a substantive variable changes over different points of measurement. Finally, in a longitudinal study of changes over time, “time” is distinct from the substantive process that underlies the change over time. Consider a hypothetical study that repeatedly measured the levels of job performance and social integration of a group of newcomers for six time points, at 1-month intervals between adjacent time points over a 6-month period. Let us assume that the study found that the observed change over time in their job performance levels was best described by a monotonically increasing trajectory at a decreasing rate of change. The observed functional form of the performance trajectory could serve as empirical evidence for the theory that a learning process underlies the performance level changes over time. Let us further assume that, for the same group of newcomers, the observed change over time in their social integration levels was best described by a positive linear trajectory. This observed functional form of the social integration trajectory could serve as empirical evidence for a theory of social adjustment process that underlies the integration level changes over time. In this example, there are two distinct substantive processes of change (learning and social adjustment) that may underlie the changes in levels on the two respective study constructs (performance and social integration). There are six time points at which each substantive variable was measured over the same time period. Time, in this longitudinal study, was simply the medium through which the two substantive processes occur. Time was not an explanation. Time did not cause the occurrence of the different substantive processes and there was nothing in the conceptual content of the time construct that could, nor was expected to, explain the functional form or nature of the two different substantive processes. The substantive processes occur or unfold through time but they did not cause time to exist.

The way that growth modeling techniques analyze longitudinal data is consistent with the above conceptualization of time. For example, in latent growth modeling, time per se is not represented as a substantive variable in the analysis. Instead, a specific time point is coded as a temporal marker of the substantive variable (e.g., as basis coefficients in a latent growth model to indicate the time points in the sequence of repeated measurement at which the substantive variable was measured). The time-varying nature of the substantive variable is represented either at the individual level as the individual slopes or at the group level as the variance of the slope factor. It is the slopes and variance of slopes of the substantive variable that are being analyzed, and not time per se. The nature of the trajectory of change in the substantive variable is descriptively represented by the specific functional form of the trajectory that is observed within the time period of study. We may also include in the latent growth model other substantive variables, such as time-invariant predictors or time-varying correlates, to assess the strength of their associations with variance of the individual slopes of trajectory. These associations serve as validation and explanation of the substantive process of change in the focal variable that is occurring over time.

Many theories of change require the articulation of a change construct (e.g., learning, social adjustment—inferred from a slope parameter in a growth model). When specifying a change construct, the “time” variable is only used as a marker to track a substantive growth or change process. For example, when we say, “Extraversion × time interaction effect” on newcomer social integration, we really mean that Extraversion relates to the change construct of social adjustment (i.e., where social adjustment is operationalized as the slope parameter from a growth model of individuals’ social integration over time). Likewise, when we say, “Conscientiousness × time2 quadratic interaction effect” on newcomer task performance, we really mean that Conscientiousness relates to the change construct of learning (where learning is operationalized as the nonlinear slope of task performance over time).

This view of time brings up a host of issues with scaling and calibration of the time variable to adequately assess the underlying substantive change construct. For example, should work experience be measured in number of years in the job versus number of assignments completed ( Tesluk & Jacobs, 1998 )? Should the change construct be thought of as a developmental age effect, historical period effect, or birth cohort effect ( Schaie, 1965 )? Should the study of time in teams reflect developmental time rather than clock time, and thus be calibrated to each team’s lifespan ( Gersick, 1988 )? As such, although time is not a substantive variable itself in longitudinal research, it is important to make sure that the measurement of time matches the theory that specifies the change construct that is under study (e.g., aging, learning, adaptation, social adjustment).

I agree that time is typically not a substantive variable, but that it can serve as a proxy for substantive variables if the process is well-known. The example about learning by Chan is a case in point. Of course, well-known temporal processes are rare and I have often seen substantive power mistakenly given to time: For example, it is the process of oxidation, not the passage of time that is responsible for rust. However, there are instances where time plays a substantive role. For example, temporal discounting ( Ainslie & Haslam, 1992 ) is a theory of behavior that is dependent on time. Likewise, Vancouver, Weinhardt, and Schmidt’s (2010) theory of multiple goal pursuit involves time as a key substantive variable. To be sure, in that latter case the perception of time is a key mediator between time and its hypothetical effects on behavior, but time has an explicit role in the theory and thus should be considered a substantive variable in tests of the theory.

I was referring to objective time when explaining that time is not a substantive variable in longitudinal research and that it is instead the temporal medium through which a substantive process unfolds or a substantive variable changes its state. When we discuss theories of substantive phenomena or processes involving temporal constructs, such as temporal discounting, time urgency, or polychronicity related to multitasking or multiple goal pursuits, we are in fact referring to subjective time, which is the individual’s psychological experience of time. Subjective time constructs are clearly substantive variables. The distinction between objective time and subjective time is important because it provides conceptual clarity to the nature of the temporal phenomena and guides methodological choices in the study of time (for details, see Chan, 2014 ).

Conceptual Issue Question 3: What are the procedures, if any, for developing a theory of changes over time in longitudinal research? Given that longitudinal research purportedly addresses the limitations of cross-sectional research, can findings from cross-sectional studies be useful for the development of a theory of change?

To address this question, what follows is largely an application of some of the ideas presented by Mitchell and James (2001) and by Ployhart and Vandenberg (2010) in their respective publications. Thus, credit for the following should be given to those authors, and consultation of their articles as to specifics is highly encouraged.

Before we specifically address this question, it is important to understand our motive for asking it. Namely, as most succinctly stated by Mitchell and James (2001) , and repeated by, among others, Bentein and colleagues (2005) , Chan (2002 , 2010 ), and Ployhart and Vandenberg (2010) , there is an abundance of published research in the major applied psychology and organizational science journals in which the authors are not operationalizing through their research designs the causal relations among their focal independent, dependent, moderator, and mediator variables even though the introduction and discussion sections imply such causality. Mitchell and James (2001) used the published pieces in the most recent issues (at that time) of the Academy of Management Journal and Administrative Science Quarterly to anchor this point. At the crux of the problem is using designs in which time is not a consideration. As they stated so succinctly:

“At the simplest level, in examining whether an X causes a Y, we need to know when X occurs and when Y occurs. Without theoretical or empirical guides about when to measure X and Y, we run the risk of inappropriate measurement, analysis, and, ultimately, inferences about the strength, order, and direction of causal relationships (italics added, Mitchell & James, 2001 , p. 530).”

When is key because it is at the heart of causality in its simplest form, as in the “cause must precede the effect” ( James, Mulaik, & Brett, 1982 ; Condition 3 of 10 for inferring causality, p. 36). Our casual glance at the published literature over the decade since Mitchell and James (2001) indicates that not much has changed in this respect. Thus, our motive for asking the current question is quite simple—“perhaps it’s ‘time’ to put these issues in front of us once more (pun intended), particularly given the increasing criticisms as to the meaningfulness of published findings from studies with weak methods and statistics” (e.g., statistical myths and urban legends, Lance & Vandenberg, 2009 ).

The first part of the question asks, “what are the procedures, if any, for developing a theory of change over time in longitudinal research?” Before addressing procedures per se, it is necessary first to understand some of the issues when incorporating change into research. Doing so provides a context for the procedures. Ployhart and Vandenberg (2010) noted four theoretical issues that should be addressed when incorporating change in the variables of interest across time. These were:

“To the extent possible, specify a theory of change by noting the specific form and duration of change and predictors of change.

Clearly articulate or graph the hypothesized form of change relative to the observed form of change.

Clarify the level of change of interest: group average change, intraunit change, or interunit differences in intraunit change.

Realize that cross-sectional theory and research may be insufficient for developing theory about change. You need to focus on explaining why the change occurs” (p. 103).

The interested reader is encouraged to consult Ployhart and Vandenberg (2010) as to the specifics underlying the four issues, but they were heavily informed by Mitchell and James (2001) . Please note that, as one means of operationalizing time, Mitchell and James (2001) focused on time very broadly in the context of strengthening causal inferences about change across time in the focal variables. Thus, Ployhart and Vandenberg’s (2010) argument, with its sole emphasis on change, is nested within the Mitchell and James (2001) perspective. I raise this point because it is in this vein that the four theoretical issues presented above have as their foundation the five theoretical issues addressed by Mitchell and James (2001) . Specifically, first, we need to know the time lag between X and Y . How long after X occurs does Y occur? Second, X and Y have durations. Not all variables occur instantaneously. Third, X and Y may change over time. We need to know the rate of change. Fourth, in some cases we have dynamic relationships in which X and Y both change. The rate of change for both variables should be known, as well as how the X – Y relationship changes. Fifth, in some cases we have reciprocal causation: X causes Y and Y causes X . This situation requires an understanding of two sets of lags, durations, and possibly rates. The major point of both sets of authors is that these theoretical issues need to be addressed first in that they should be the key determinants in designing the overall study; that is, deciding upon the procedures to use.

Although Mitchell and James (2001 , see p. 543) focused on informing procedures through theory in the broader context of time (e.g., draw upon studies and research that may not be in our specific area of interest; going to the workplace and actually observing the causal sequence, etc.), our specific question focuses on change across time. In this respect, Ployhart and Vandenberg (2010 , Table 1 in p. 103) identified five methodological and five analytical procedural issues that should be informed by the nature of the change. These are:

“Methodological issues

1. Determine the optimal number of measurement occasions and their intervals to appropriately model the hypothesized form of change.

2. Whenever possible, choose samples most likely to exhibit the hypothesized form of change, and try to avoid convenience samples.

3. Determine the optimal number of observations, which in turn means addressing the attrition issue before conducting the study. Prepare for the worst (e.g., up to a 50% drop from the first to the last measurement occasion). In addition, whenever possible, try to model the hypothesized “cause” of missing data (ideally theorized and measured a priori) and consider planned missingness approaches to data collection.

4. Introduce time lags between intervals to address issues of causality, but ensure the lags are neither too long nor too short.

5. Evaluate the measurement properties of the variable for invariance (e.g., configural, metric) before testing whether change has occurred.

Analytical issues

1. Be aware of potential violations in statistical assumptions inherent in longitudinal designs (e.g., correlated residuals, nonindependence).

2. Describe how time is coded (e.g., polynomials, orthogonal polynomials) and why.

3. Report why you use a particular analytical method and its strengths and weaknesses for the particular study.

4. Report all relevant effect sizes and fit indices to sufficiently evaluate the form of change.

5. It is easy to ‘overfit’ the data; strive to develop a parsimonious representation of change.”

In summary, the major point from the above is to encourage researchers to develop a thorough conceptual understanding of time as it relates to defining the causal relationships between the focal variables of interest. We acknowledge that researchers are generally good at conceptualizing why their x -variables cause some impact on their y -variables. What is called for here goes beyond just understanding why, but forcing ourselves to be very specific about the timing between the variables. Doing so will result in stronger studies and ones in which our inferences from the findings can confidently include statements about causality—a level of confidence that is sorely lacking in most published studies today. As succinctly stated by Mitchell and James (2001) , “With impoverished theory about issues such as when events occur, when they change, or how quickly they change, the empirical researcher is in a quandary. Decisions about when to measure and how frequently to measure critical variables are left to intuition, chance, convenience, or tradition. None of these are particularly reliable guides (p. 533).”

The latter quote serves as a segue to address the second part of our question, “Given that longitudinal research purportedly addresses the limitations of cross-sectional research, can findings from cross-sectional studies be useful for the development of a theory of change?” Obviously, the answer here is “it depends.” In particular, it depends on the design contexts around which the cross-sectional study was developed. For example, if the study was developed strictly following many of the principles for designing quasi-experiments in field settings spelled out by Shadish, Cook, and Campbell (2002) , then it would be very useful for developing a theory of change on the phenomenon of interest. Findings from such studies could inform decisions as to how much change needs to occur across time in the independent variable to see measurable change in the dependent variable. Similarly, it would help inform decisions as to what the baseline on the independent variable needs to be, and what amount of change from this baseline is required to impact the dependent variable. Another useful set of cross-sectional studies would be those developed for the purpose of verifying within field settings the findings from a series of well-designed laboratory experiments. Again, knowing issues such as thresholds, minimal/maximal values, and intervals or timing of the x -variable onset would be very useful for informing a theory of change. A design context that would be of little use for developing a theory of change is the case where a single cross-sectional study was completed to evaluate the conceptual premises of interest. The theory underlying the study may be useful, but the findings themselves would be of little use.

Few theories are not theories of change. Most, however, are not sufficiently specified. That is, they leave much to the imagination. Moreover, they often leave to the imagination the implications of the theory on behavior. My personal bias is that theories of change should generally be computationally rendered to reduce vagueness, provide a test of internal coherence, and support the development of predictions. One immediately obvious conclusion one will draw when attempting to create a formal computational theoretical model is that we have little empirical data on rates of change.

The procedures for developing a computational model are the following ( Vancouver & Weinhardt, 2012 ; also see Wang et al. , 2016 ). First, take variables from (a) existing theory (verbal or static mathematical theory), (b) qualitative studies, (c) deductive reasoning, or (d) some combination of these. Second, determine which variables are dynamic. Dynamic variables have “memory” in that they retain their value over time, changing only as a function of processes that move the value in one direction or another at some rate or some changing rate. Third, describe processes that would affect these dynamic variables (if using existing theory, this likely involves other variables in the theory) or the rates and direction of change to the dynamic variables if the processes that affect the rates are beyond the theory. Fourth, represent formally (e.g., mathematically) the effect of the variables on each other. Fifth, simulate the model to see if it (a) works (e.g., no out-of-bounds values generated), (b) produces phenomena the theory is presumed to explain, (c) produces patterns of data over time (trajectories; relationships) that match (or could be matched to) data, and (d) determine if variance in exogenous variables (i.e., ones not presumably affected by other variables in the model) affect trajectories/relationships (called sensitivity analysis). For example, if we build a computational model to understand retirement timing, it will be critical to simulate the model to make sure that it generates predictions in a realistic way (e.g., the simulation should not generate too many cases where retirement happens after the person is a 90-year old). It will also be important to see whether the predictions generated from the model match the actual empirical data (e.g., the average retirement age based on simulation should match the average retirement age in the target population) and whether the predictions are robust when the model’s input factors take on a wide range of values.

As mentioned above, many theories of change require the articulation of a change construct (e.g., learning, aging, social adjustment—inferred from a slope parameter in a growth model). A change construct must be specified in terms of its: (a) theoretical content (e.g., what is changing, when we say “learning” or “aging”?), (b) form of change (linear vs. quadratic vs. cyclical), and (c) rate of change (does the change process meaningfully occur over minutes vs. weeks?). One salient problem is how to develop theory about the form of change (linear vs. nonlinear/quadratic) and the rate of change (how fast?) For instance, a quadratic/nonlinear time effect can be due to a substantive process of diminishing returns to time (e.g., a learning curve), or to ceiling (or floor) effects (i.e., hitting the high end of a measurement instrument, past which it becomes impossible to see continued growth in the latent construct). Indeed, only a small fraction of the processes we study would turn out to be linear if we used more extended time frames in the longitudinal design. That is, most apparently linear processes result from the researcher zooming in on a nonlinear process in a way that truncates the time frame. This issue is directly linked to the presumed rate of change of a phenomenon (e.g., a process that looks nonlinear in a 3-month study might look linear in a 3-week study). So when we are called upon to theoretically justify why we hypothesize a linear effect instead of a nonlinear effect, we must derive a theory of what the passage of time means. This would involve three steps: (a) naming the substantive process for which time is a marker (e.g., see answers to Question #2 above), (b) theorizing the rate of this process (e.g., over weeks vs. months), which will be more fruitful if it hinges on related past empirical longitudinal research, than if it hinges on armchair speculation about time (i.e., the appropriate theory development sequence here is: “past data → theory → new data,” and not simply, “theory → new data”; the empirical origins of theory are an essential step), and (c) disavowing nonlinear forces (e.g., diminishing returns to time, periodicity), within the chosen time frame of the study.

Research Design Question 1: What are some of the major considerations that one should take into account before deciding to employ a longitudinal study design?

As with all research, the design needs to allow the researcher to address the research question. For example, if one is seeking to assess a change rate, one needs to ask if it is safe to assume that the form of change is linear. If not, one will need more than two waves or will need to use continuous sampling. One might also use a computational model to assess whether violations of the linearity assumption are important. The researcher needs to also have an understanding of the likely time frame across which the processes being examined occur. Alternatively, if the time frame is unclear, the researcher should sample continuously or use short intervals. If knowing the form of the change is desired, then one will need enough waves of data collection in which to comprehensively capture the changes.

If one is interested in assessing causal processes, more issues need to be considered. For example, what are the processes of interest? What are the factors affecting the processes or the rates of the processes? What is the form of the effect of these factors? And perhaps most important, what alternative process could be responsible for effects observed?

For example, consider proactive socialization ( Morrison, 2002 ). The processes of interest are those involved in determining proactive information seeking. One observation is that the rate of proactive information seeking drops with the tenure of an employee ( Chan & Schmitt, 2000 ). Moreover, the form of the drop is asymptotic to a floor (Vancouver, Tamanini et al. , 2010 ). The uncertainty reduction model predicts that proactive information seeking will drop over time because knowledge increases (i.e., uncertainty decreases). An alternative explanation is that ego costs grow over time: One feels that they will look more foolish asking for information the longer one’s tenure ( Ashford, 1986 ). To distinguish these explanations for a drop in information seeking over time, one might want to look at whether the transparency of the reason to seek information would moderate the negative change trend of information seeking. For the uncertainty reduction model, transparency should not matter, but for the ego-based model, transparency and legitimacy of reason should matter. Of course, it might be that both processes are at work. As such, the researcher may need a computational model or two to help think through the effects of the various processes and whether the forms of the relationships depend on the processes hypothesized (e.g., Vancouver, Tamanini et al. , 2010 ).

Research Design Question 2: Are there any design advantages of cross-sectional research that might make it preferable to longitudinal research? That is, what would be lost and what might be gained if a moratorium were placed on cross-sectional research?

Cross-sectional research is easier to conduct than longitudinal research, but it often estimates the wrong parameters. Interestingly, researchers typically overemphasize/talk too much about the first fact (ease of cross-sectional research), and underemphasize/talk too little about the latter fact (that cross-sectional studies estimate the wrong thing). Cross-sectional research has the advantages of allowing broader sampling of participants, due to faster and cheaper studies that involve less participant burden; and broader sampling of constructs, due to the possibility of participant anonymity in cross-sectional designs, which permits more honest and complete measurement of sensitive concepts, like counterproductive work behavior.

Also, when the theoretical process at hand has a very short time frame (e.g., minutes or seconds), then cross-sectional designs can be entirely appropriate (e.g., for factor analysis/measurement modeling, because it might only take a moment for a latent construct to be reflected in a survey response). Also, first-stage descriptive models of group differences (e.g., sex differences in pay; cross-cultural differences in attitudes; and other “black box” models that do not specify a psychological process) can be suggestive even with cross-sectional designs. Cross-sectional research can also be condoned in the case of a 2-study design wherein cross-sectional data are supplemented with lagged/longitudinal data.

But in the end, almost all psychological theories are theories of change (at least implicitly) [Contrary to Ployhart and Vandenberg (2010) , I tend to believe that “cross-sectional theory” does not actually exist— theories are inherently longitudinal, whereas models and evidence can be cross-sectional.]. Thus, longitudinal and time-lagged designs are indispensable, because they allow researchers to begin answering four types of questions: (a) causal priority, (b) future prediction, (c) change, and (d) temporal external validity. To define and compare cross-sectional against longitudinal and time-lagged designs, I refer to Figure 2 . Figure 2 displays three categories of discrete-time designs: cross-sectional ( X and Y measured at same time; Figure 2a ), lagged ( Y measured after X by a delay of duration t ; Figure 2b ), and longitudinal ( Y measured at three or more points in time; Figure 2c ) designs. First note that, across all time designs, a 1 denotes the cross-sectional parameter (i.e., the correlation between X 1 and Y 1 ) . In other words, if X is job satisfaction and Y is retirement intentions, a 1 denotes the cross-sectional correlation between these two variables at t 1 . To understand the value (and limitations) of cross-sectional research, we will look at the role of the cross-sectional parameter ( a 1 ) in each of the Figure 2 models.

Time-based designs for two constructs, X and Y. (a) cross-sectional design (b) lagged designs (c) longitudinal designs.

Time-based designs for two constructs, X and Y . (a) cross-sectional design (b) lagged designs (c) longitudinal designs.

For assessing causal priority , the lagged models and panel model are most relevant. The time-lagged b 1 parameter (i.e., correlation between X 1 and Y 2 ; e.g., predictive validity) aids in future prediction, but tells us little about causal priority. In contrast, the panel regression b 1 ' parameter from the cross-lagged panel regression (in Figure 2b ) and the cross-lagged panel model (in Figure 2c ) tells us more about causal priority from X to Y ( Kessler & Greenberg, 1981 ; Shingles, 1985 ), and is a function of the b 1 parameter and the cross-sectional a 1 parameter [ b 1 ' = ( b 1 − a 1 r Y 1 , Y 2 ) / 1 − a 1 2 ] . For testing theories that X begets Y (i.e., X → Y ), the lagged parameter b 1 ' can be extremely useful, whereas the cross-sectional parameter a 1 is the wrong parameter (indeed, a 1 is often negatively related to b 1 ' ) . That is, a 1 does not estimate X → Y , but it is usually negatively related to that estimate (via the above formula for b 1 ' ) . Using the example of job satisfaction and retirement intentions, if we would like to know about the causal priority from job satisfaction to retirement intentions, we should at least measure both job satisfaction and retirement intentions at t 1 and then measure retirement intentions at t 2 . Deriving the estimate for b 1 ' involves regressing retirement intentions at t 2 on job satisfaction at t 1 , while controlling for the effect of retirement intentions at t 1 .

For future prediction , the autoregressive model and growth model in Figure 2c are most relevant. One illustrative empirical phenomenon is validity degradation, which means the X – Y correlation tends to shrink as the time interval between X and Y increases ( Keil & Cortina, 2001 ). Validity degradation and patterns of stability have been explained via simplex autoregressive models ( Hulin, Henry, & Noon, 1990 ; Humphreys, 1968 ; Fraley, 2002 ), which express the X – Y correlation as r X 1 , Y 1 + k = a 1 g k , where k is the number of time intervals separating X and Y . Notice the cross-sectional parameter a 1 in this formula serves as a multiplicative constant in the time-lagged X – Y correlation, but is typically quite different from the time-lagged X – Y correlation itself. Using the example of extraversion and retirement intentions, validity degradation means that the effect of extraversion at t 1 on the measure of retirement intentions is likely to decrease over time, depending on how stable retirement intentions are. Therefore, relying on a 1 to gauge how well extraversion can predict future retirement intentions is likely to overestimate the predictive effect of extraversion.

Another pertinent model is the latent growth model ( Chan, 1998 ; Ployhart & Hakel, 1998 ), which explains longitudinal data using a time intercept and slope. In the linear growth model in Figure 2 , the cross-sectional a 1 parameter is equal to the relationship between X 1 and the Y intercept, when t 1 = 0. I also note that from the perspective of the growth model, the validity degradation phenomenon (e.g., Hulin et al. , 1990 ) simply means that X 1 has a negative relationship with the Y slope. Thus, again, the cross-sectional a 1 parameter merely indicates the initial state of the X and Y relationship in a longitudinal system, and will only offer a reasonable estimate of future prediction of Y under the rare conditions when g ≈ 1.0 in the autoregressive model (i.e., Y is extremely stable), or when i ≈ 0 in the growth model (i.e., X does not predict the Y -slope; Figure 2c ).

For studying change , I refer to the growth model (where both X and the Y -intercept explain change in Y [or Y -slope]) and the coupled growth model (where X -intercept, Y -intercept, change in X , and change in Y all interrelate) in Figure 2c . Again, in these models the cross-sectional a 1 parameter is the relationship between the X and Y intercepts, when the slopes are specified with time centered at t 1 = 0 (where t 1 refers arbitrarily to any time point when the cross-sectional data were collected). In the same way that intercepts tell us very little about slopes (ceiling and floor effects notwithstanding), the cross-sectional X 1 parameter tells us almost nothing about change parameters. Again, using the example of the job satisfaction and retirement intentions relationship, to understand change in retirement intentions over time, it is important to gauge the effects of initial status of job satisfaction (i.e., job satisfaction intercept) and change in job satisfaction (i.e., job satisfaction slope) on change in retirement intentions (i.e., slope of retirement intentions).

Finally, temporal external validity refers to the extent to which an effect observed at one point in time generalizes across other occasions. This includes longitudinal measurement equivalence (e.g., whether the measurement metric of the concept or the meaning of the concept may change over time; Schmitt, 1982 ), stability of bivariate relationships over time (e.g., job satisfaction relates more weakly to turnover when the economy is bad; Carsten & Spector, 1987 ), the stationarity of cross-lagged parameters across measurement occasions ( b 1 ' = b 2 ' , see cross-lagged panel model in Figure 2c ; e.g., Cole & Maxwell, 2003 ), and the ability to identify change as an effect of participant age/tenure/development—not an effect of birth/hire cohort or historical period ( Schaie, 1965 ). Obviously, cross-sectional data have nothing to say about temporal external validity.

Should there be a moratorium on cross-sectional research? Because any single wave of a longitudinal design is itself cross-sectional data, a moratorium is not technically possible. However, there should be (a) an explicit acknowledgement of the different theoretical parameters in Figure 2 , and (b) a general moratorium on treating the cross-sectional a 1 parameter as though it implies causal priority (cf. panel regression parameter b 1 ' ) , future prediction (cf. panel regression, autoregressive, and growth models), change (cf. growth models), or temporal external validity. This recommendation is tantamount to a moratorium on cross-sectional research papers, because almost all theories imply the lagged and/or longitudinal parameters in Figure 2 . As noted earlier, cross-sectional data are easier to get, but they estimate the wrong parameter.

I agree with Newman that most theories are about change or should be (i.e., we are interested in understanding processes and, of course, processes occur over time). I am also in agreement that cross-sectional designs are of almost no value for assessing theories of change. Therefore, I am interested in getting to a place where most research is longitudinal, and where top journals rarely publish papers with only a cross-sectional design. However, as Newman points out, some research questions can still be addressed using cross-sectional designs. Therefore, I would not support a moratorium on cross-sectional research papers.

Research Design Question 3: In a longitudinal study, how do we decide on the length of the interval between two adjacent time points?

This question needs to be addressed together with the question on how many time points of measurement to administer in a longitudinal study. It is well established that intra-individual changes cannot be adequately assessed with only two time points because (a) a two-point measurement by necessity produces a linear trajectory and therefore is unable to empirically detect the functional form of the true change trajectory and (b) time-related (random or correlated) measurement error and true change over time are confounded in the observed change in a two-point measurement situation (for details, see Chan, 1998 ; Rogosa, 1995 ; Singer & Willett, 2003 ). Hence, the minimum number of time points for assessing intra-individual change is three, but more than three is better to obtain a more reliable and valid assessment of the change trajectory ( Chan, 1998 ). However, it does not mean that a larger number of time points is always better or more accurate than a smaller number of time points. Given that the total time period of study captures the change process of interest, the number of time points should be determined by the appropriate location of the time point. This then brings us to the current practical question on the choice regarding the appropriate length of the interval between adjacent time points.

The correct length of the time interval between adjacent time points in a longitudinal study is critical because it directly affects the observed functional form of the change trajectory and in turn the inference we make about the true pattern of change over time ( Chan, 1998 ). What then should be the correct length of the time interval between adjacent time points in a longitudinal study? Put simply, the correct or optimal length of the time interval will depend on the specific substantive change phenomenon of interest. This means it is dependent on the nature of the substantive construct, its underlying process of change over time, and the context in which the change process is occurring which includes the presence of variables that influence the nature and rate of the change. In theory, the time interval for data collection is optimal when the time points are appropriately spaced in such a way that it allows the true pattern of change over time to be observed during the period of study. When the observed time interval is too short or too long as compared to the optimal time interval, true patterns of change will get masked or false patterns of change will get observed.

The problem is we almost never know what this optimal time interval is, even if we have a relatively sound theory of the change phenomenon. This is because our theories of research phenomena are often static in nature. Even when our theories are dynamic and focus on change processes, they are almost always silent on the specific length of the temporal dimension through which the substantive processes occur over time ( Chan, 2014 ).

In practice, researchers determine their choice of the length of the time interval in conjunction with the choice of number of time points and the choice of the length of the total time period of study. Based on my experiences as an author, reviewer, and editor, I suspect that these three choices are influenced by the specific resource constraints and opportunities faced by the researchers when designing and conducting the longitudinal study. Deviation from optimal time intervals probably occurs more frequently than we would like, since decisions on time intervals between measures in a study are often pragmatic and atheoretical. When we interpret findings from longitudinal studies, we should consider the possibility that the study may have produced patterns of results that led to wrong inferences because the study did not reflect the true changes over time.

Given that our theories of phenomena are not at the stage where we could specify the optimal time intervals, the best we could do now is to explicate the nature of the change processes and the effects of the influencing factors to serve as guides for decisions on time intervals, number of time points, and total time period of study. For example, in research on sense-making processes in newcomer adaptation, the total period of study often ranged from 6 months to 1 year, with 6 to 12 time points, equally spaced at time intervals of 1 or 2 months between adjacent time points. A much longer time interval and total time period, ranging from several months to several years, would be more appropriate for a change process that should take a longer time to manifest itself, such as development of cognitive processes or skill acquisition requiring extensive practice or accumulation of experiences over time. On the other extreme, a much shorter time interval and total time period, ranging from several hours to several days, will be appropriate for a change process that should take a short time to manifest itself such as activation or inhibition of mood states primed by experimentally manipulated events.

Research Design Question 4: As events occur in our daily life, our mental representations of these events may change as time passes. How can we determine the point(s) in time at which the representation of an event is appropriate? How can these issues be addressed through design and measurement in a study?

In some cases, longitudinal researchers will wish to know the nature and dynamics of one’s immediate experiences. In these cases, the items included at each point in time will simply ask participants to report on states, events, or behaviors that are relatively immediate in nature. For example, one might be interested in an employee’s immediate affective experiences, task performance, or helping behavior. This approach is particularly common for intensive, short-term longitudinal designs such as experience sampling methods (ESM; Beal & Weiss, 2003 ). Indeed, the primary objective of ESM is to capture a representative sample of points within one’s day to help understand the dynamic nature of immediate experience ( Beal, 2015 ; Csikszentmihalyi & Larson, 1987 ). Longitudinal designs that have longer measurement intervals may also capture immediate experiences, but more often will ask participants to provide some form of summary of these experiences, typically across the entire interval between each measurement occasion. For example, a panel design with a 6-month interval may ask participants to report on affective states, but include a time frame such as “since the last survey” or “over the past 6 months”, requiring participants to mentally aggregate their own experiences.

As one might imagine, there also are various designs and approaches that range between the end points of immediate experience and experiences aggregated over the entire interval. For example, an ESM study might examine one’s experiences since the last survey. These intervals obviously are close together in time, and therefore are conceptually similar to one’s immediate state; nevertheless, they do require both increased levels of recall and some degree of mental aggregation. Similarly, studies with a longer time interval (e.g., 6-months) might nevertheless ask about one’s relatively recent experiences (e.g., affect over the past week), requiring less in terms of recall and mental aggregation, but only partially covering the events of the entire intervening interval. As a consequence, these two approaches and the many variations in between form a continuum of abstraction containing a number of differences that are worth considering.

Differences in Stability

Perhaps the most obvious difference across this continuum of abstraction is that different degrees of aggregation are captured. As a result, items will reflect more or less stable estimates of the phenomenon of interest. Consider the hypothetical temporal break-down of helping behavior depicted in Figure 3 . No matter how unstable the most disaggregated level of helping behavior may appear, aggregations of these behaviors will always produce greater stability. So, asking about helping behavior over the last hour will produce greater observed variability (i.e., over the entire scale) than averages of helping behavior over the last day, week, month, or one’s overall general level. Although it is well-known that individuals do not follow a strict averaging process when asked directly about a higher level of aggregation (e.g., helping this week; see below), it is very unlikely that such deviations from a straight average will result in less stability at higher levels of aggregation.

Hypothetical variability of helping behavior at different levels of aggregation.

Hypothetical variability of helping behavior at different levels of aggregation.

The reason why this increase in stability is likely to occur regardless of the actual process of mental aggregation is that presumably, as you move from shorter to longer time frames, you are estimating either increasingly stable aspects of an individual’s dispositional level of the construct, or increasingly stable features of the context (e.g., a consistent workplace environment). As you move from longer to shorter time frames you are increasingly estimating immediate instances of the construct or context that are influenced not only by more stable predictors, but also dynamic trends, cycles, and intervening events ( Beal & Ghandour, 2011 ). Notably, this stabilizing effect exists independently of the differences in memory and mental aggregation that are described below.

Differences in Memory

Fundamental in determining how people will respond to these different forms of questions is the nature of memory. Robinson and Clore (2002) provided an in-depth discussion of how we rely on different forms of memory when answering questions over different time frames. Although these authors focus on reports of emotion experiences, their conclusions are likely applicable to a much wider variety of self-reports. At one end of the continuum, reports of immediate experiences are direct, requiring only one’s interpretation of what is occurring and minimizing mental processes of recall.

Moving slightly down the continuum, we encounter items that ask about very recent episodes (e.g., “since the last survey” or “in the past 2 hours” in ESM studies). Here, Robinson and Clore (2002) note that we rely on what cognitive psychologists refer to as episodic memory. Although recall is involved, specific details of the episode in question are easily recalled with a high degree of accuracy. As items move further down the continuum toward summaries of experiences over longer periods of time (e.g., “since the last survey” in a longitudinal panel design), the details of particular relevant episodes are harder to recall and so responses are tinged to an increasing degree by semantic memory. This form of memory is based on individual characteristics (e.g., neurotic individuals might offer more negative reports) as well as well-learned situation-based knowledge (e.g., “my coworkers are generally nice people, so I’m sure that I’ve been satisfied with my interactions over this period of time”). Consequently, as the time frame over which people report increases, the nature of the information provided changes. Specifically, it is increasingly informed by semantic memory (i.e., trait and situation-based knowledge) and decreasingly informed by episodic memory (i.e., particular details of one’s experiences). Thus, researchers should be aware of the memory-related implications when they choose the time frame for their measures.

Differences in the Process of Summarizing

Aside from the role of memory in determining the content of these reports, individuals also summarize their experiences in a complex manner. For example, psychologists have demonstrated that even over a single episode, people tend not to base subjective summaries of the episode on its typical or average features. Instead, we focus on particular notable moments during the experience, such as its peak or its end state, and pay little attention to some aspects of the experience, such as its duration ( Fredrickson, 2000 ; Redelmeier & Kahneman, 1996 ). The result is that a mental summary of a given episode is unlikely to reflect actual averages of the experiences and events that make up the episode. Furthermore, when considering reports that span multiple episodes (e.g., over the last month or the interval between two measurements in a longitudinal panel study), summaries become even more complex. For example, recent evidence suggests that people naturally organize ongoing streams of experience into more coherent episodes largely on the basis of goal relevance ( Beal, Weiss, Barros, & MacDermid, 2005 ; Beal & Weiss, 2013 ; Zacks, Speer, Swallow, Braver, & Reynolds, 2007 ). Thus, how we interpret and parse what is going on around us connects strongly to our goals at the time. Presumably, this process helps us to impart meaning to our experiences and predict what might happen next, but it also influences the type of information we take with us from the episode, thereby affecting how we might report on this period of time.

Practical Differences

What then, can researchers take away from this information to help in deciding what sorts of items to include in longitudinal studies? One theme that emerges from the above discussion is that summaries over longer periods of time will tend to reflect more about the individual and the meanings he or she may have imparted to the experiences, events, and behaviors that have occurred during this time period, whereas shorter-term summaries or reports of more immediate occurrences are less likely to have been processed through this sort of interpretive filter. Of course, this is not to say that the more immediate end of this continuum is completely objective, as immediate perceptions are still host to many potential biases (e.g., attributional biases typically occur immediately); rather, immediate reports are more likely to reflect one’s immediate interpretation of events rather than an interpretation that has been mulled over and considered in light of an individual’s short- and long-term goals, dispositions, and broader worldview.

The particular choice of item type (i.e., immediate vs. aggregated experiences) that will be of interest to a researcher designing a longitudinal study should of course be determined by the nature of the research question. For example, if a researcher is interested in what Weiss and Cropanzano (1996) referred to as judgment-driven behaviors (e.g., a calculated decision to leave the organization), then capturing the manner in which individuals make sense of relevant work events is likely more appropriate, and so items that ask one to aggregate experiences over time may provide a better conceptual match than items asking about immediate states. In contrast, affect-driven behaviors or other immediate reactions to an event will likely be better served by reports that ask participants for minimal mental aggregations of their experiences (e.g., immediate or over small spans of time).

The issue of mental representations of events at particular points in time should always be discussed and evaluated within the research context of the conceptual questions on the underlying substantive constructs and change processes that may account for patterns of responses over time. Many of these conceptual questions are likely to relate to construct-oriented issues such as the location of the substantive construct on the state-trait continuum and the timeframe through which short-term or long-term effects on the temporal changes in the substantive construct are likely to be manifested (e.g., effects of stressors on changes in health). On the issue of aggregation of observations across time, I see it as part of a more basic question on whether an individual’s subjective experience on a substantive construct (e.g., emotional well-being) should be assessed using momentary measures (e.g., assessing the individual’s current emotional state, measured daily over the past 1 week) or retrospective global reports (e.g., asking the individual to report an overall assessment of his or her emotional state over the past 1 week). Each of the two measurement perspectives (i.e., momentary and global retrospective) has both strengths and limitations. For example, momentary measures are less prone to recall biases compared to global retrospective measures ( Kahneman, 1999 ). Global retrospective measures, on the other hand, are widely used in diverse studies for the assessment of many subjective experience constructs with a large database of evidence concerning the measure’s reliability and validity ( Diener, Inglehart, & Tay, 2013 ). In a recent article ( Tay, Chan, & Diener, 2014 ), my colleagues and I reviewed the conceptual, methodological, and practical issues in the debate between the momentary and global retrospective perspectives as applied to the research on subjective well-being. We concluded that both perspectives could offer useful insights and suggested a multiple-method approach that is sensitive to the nature of the substantive construct and specific context of use, but also called for more research on the use of momentary measures to obtain more evidence for their psychometric properties and practical value.

Research Design Question 5: What are the biggest practical hurdles to conducting longitudinal research? What are the ways to overcome them?

As noted earlier, practical hurdles are perhaps one of the main reasons why researchers choose cross-sectional rather than longitudinal designs. Although we have already discussed a number of these issues that must be faced when conducting longitudinal research, the following discussion emphasizes two hurdles that are ubiquitous, often difficult to overcome, and are particularly relevant to longitudinal designs.

Encouraging Continued Participation

Encouraging participation is a practical issue that likely faces all studies, irrespective of design; however, longitudinal studies raise special considerations given that participants must complete measurements on multiple occasions. Although there is a small literature that has examined this issue specifically (e.g., Fumagalli, Laurie, & Lynn, 2013 ; Groves et al. , 2006 ; Laurie, Smith, & Scott, 1999 ), it appears that the relevant factors are fairly similar to those noted for cross-sectional surveys. In particular, providing monetary incentives prior to completing the survey is a recommended strategy (though nonmonetary gifts can also be effective), with increased amounts resulting in increased participation rates, particularly as the burden of the survey increases ( Laurie & Lynn, 2008 ).

The impact of participant burden relates directly to the special considerations of longitudinal designs, as they are generally more burdensome. In addition, with longitudinal designs, the nature of the incentives used can vary over time, and can be tailored toward reducing attrition rates across the entire span of the survey ( Fumagalli et al. , 2013 ). For example, if the total monetary incentive is distributed across survey waves such that later waves have greater incentive amounts, and if this information is provided to participants at the outset of the study, then attrition rates may be reduced more effectively ( Martin & Loes, 2010 ); however, some research suggests that a larger initial payment is particularly effective at reducing attrition throughout the study ( Singer & Kulka, 2002 ).

In addition, the fact that longitudinal designs reflect an implicit relationship between the participant and the researchers over time suggests that incentive strategies that are considered less effective in cross-sectional designs (e.g., incentive contingent on completion) may be more effective in longitudinal designs, as the repeated assessments reflect a continuing reciprocal relationship. Indeed, there is some evidence that contingent incentives are effective in longitudinal designs ( Castiglioni, Pforr, & Krieger, 2008 ). Taken together, one potential strategy for incentivizing participants in longitudinal surveys would be to divide payment such that there is an initial relatively large incentive delivered prior to completing the first wave, followed by smaller, but increasing amounts that are contingent upon completion of each successive panel. Although this strategy is consistent with theory and evidence just discussed, it has yet to be tested explicitly.

Continued contact

One thing that does appear certain, particularly in longitudinal designs, is that incentives are only part of the picture. An additional factor that many researchers have emphasized is the need to maintain contact with participants throughout the duration of a longitudinal survey ( Laurie, 2008 ). Strategies here include obtaining multiple forms of contact information at the outset of the study and continually updating this information. From this information, researchers should make efforts to keep in touch with participants in-between measurement occasions (for panel studies) or some form of ongoing basis (for ESM or other intensive designs). Laurie (2008) referred to these efforts as Keeping In Touch Exercises (KITEs) and suggested that they serve to increase belongingness and perhaps a sense of commitment to the survey effort, and have the additional benefit of obtaining updated contact and other relevant information (e.g., change of job).

Mode of Data Collection

General considerations.

In panel designs, relative to intensive designs discussed below, only a limited number of surveys are sought, and the interval between assessments is relatively large. Consequently, there is likely to be greater flexibility as to the particular methods chosen for presenting and recording responses. Although the benefits, costs, and deficiencies associated with traditional paper-and-pencil surveys are well-known, the use of internet-based surveys has evolved rapidly and so the implications of using this method have also changed. For example, early survey design technologies for internet administration were often complex and potentially costly. Simply adding items was sometimes a difficult task, and custom-formatted response options (e.g., sliding scales with specific end points, ranges, and tick marks) were often unattainable. Currently available web-based design tools often are relatively inexpensive and increasingly customizable, yet have maintained or even improved the level of user-friendliness. Furthermore, a number of studies have noted that data collected using paper-and-pencil versus internet-based applications are often comparable if not indistinguishable (e.g., Cole, Bedeian, & Feild, 2006 ; Gosling et al. , 2004 ), though notable exceptions can occur ( Meade, Michels, & Lautenschlager, 2007 ).

One issue related to the use of internet-based survey methods that is likely to be of increasing relevance in the years to come is collection of survey data using a smartphone. As of this writing (this area changes rapidly), smartphone options are in a developing phase where some reasonably good options exist, but have yet to match the flexibility and standardized appearance that comes with most desktop or laptop web-based options just described. For example, it is possible to implement repeated surveys for a particular mobile operating system (OS; e.g., Apple’s iOS, Google’s Android OS), but unless a member of the research team is proficient in programming, there will be a non-negligible up-front cost for a software engineer ( Uy, Foo, & Aguinis, 2010 ). Furthermore, as market share for smartphones is currently divided across multiple mobile OSs, a comprehensive approach will require software development for each OS that the sample might use.

There are a few other options, however, but some of these options are not quite complete solutions. For example, survey administration tools such as Qualtrics now allow for testing of smartphone compatibility when creating web-based surveys. So, one could conceivably create a survey using this tool and have people respond to it on their smartphone with little or no loss of fidelity. Unfortunately, these tools (again, at this moment in time) do not offer elegant or flexible signaling capabilities. For example, intensive repeated measures designs will often try to signal reasonably large (e.g., N = 50–100) number of participants multiple random signals every day for multiple weeks. Accomplishing this task without the use of a built-in signaling function (e.g., one that generates this pattern of randomized signals and alerts each person’s smartphone at the appropriate time), is no small feat.

There are, however, several efforts underway to provide free or low-cost survey development applications for mobile devices. For example, PACO is a (currently) free Google app that is in the beta-testing stage and allows great flexibility in the design and implementation of repeated surveys on both Android OS and iOS smartphones. Another example that is currently being developed for both Android and iOS platforms is Expimetrics ( Tay, 2015 ), which promises flexible design and signaling functions that is of low cost for researchers collecting ESM data. Such applications offer the promise of highly accessible survey administration and signaling and have the added benefit of transmitting data quickly to servers accessible to the research team. Ideally, such advances in accessibility of survey administration will allow increased response rates throughout the duration of the longitudinal study.

Issues specific to intensive designs

All of the issues just discussed with respect to the mode of data collection are particularly relevant for short-term intensive longitudinal designs such as ESM. As the number of measurement occasions increases, so too do the necessities of increasing accessibility and reducing participant burden wherever possible. Of particular relevance is the emphasis ESM places on obtaining in situ assessments to increase the ecological validity of the study ( Beal, 2015 ). To maximize this benefit of the method, it is important to reduce the interruption introduced by the survey administration. If measurement frequency is relatively sparse (e.g., once a day), it is likely that simple paper-and-pencil or web-based modes of collection will be sufficient without creating too much interference ( Green et al. , 2006 ). In contrast, as measurements become increasingly intensive (e.g., four or five times/day or more), reliance on more accessible survey modes will become important. Thus, a format that allows for desktop, laptop, or smartphone administration should be of greatest utility in such intensive designs.

Statistical Techniques Question 1: With respect to assessing changes over time in a latent growth modeling framework, how can a researcher address different conceptual questions by coding the slope variable differently?

As with many questions in this article, an in-depth answer to this particular question is not possible in the available space. Hence, only a general treatment of different coding schemes of the slope or change variable is provided. Excellent detailed treatments of this topic may be found in Bollen and Curran (2006 , particularly chapters 3 & 4), and in Singer and Willett (2003 , particularly chapter 6). As noted by Ployhart and Vandenberg (2010) , specifying the form of change should be an a priori conceptual endeavor, not a post hoc data driven effort. This stance was also stated earlier by Singer and Willett (2003) when distinguishing between empirical (data driven) versus rational (theory driven) strategies. “Under rational strategies, on the other hand, you use theory to hypothesize a substantively meaningful functional form for the individual change trajectory. Although rational strategies generally yield clearer interpretations, their dependence on good theory makes them somewhat more difficult to develop and apply ( Singer & Willett, 2003 , p. 190).” The last statement in the quote simply reinforces the main theme throughout this article; that is, researchers need to undertake the difficult task of bringing in time (change being one form) within their conceptual frameworks in order to more adequately examine the causal structure among the focal variables within those frameworks.

In general, there are three sets of functional forms for which the slope or change variable may be coded or specified: (a) linear; (b) discontinuous; and (c) nonlinear. Sets emphasize that within each form there are different types that must be considered. The most commonly seen form in our literature is linear change (e.g., Bentein et al. , 2005 ; Vandenberg & Lance, 2000 ). Linear change means there is an expectation that the variable of interest should increase or decrease in a straight-line function during the intervals of the study. The simplest form of linear change occurs when there are equal measurement intervals across time and the units of observations were obtained at the same time in those intervals. Assuming, for example, that there were four occasions of measurement, the coding of the slope variable would be 0 (Time 1), 1 (Time 2), 2 (Time 3) and 3 (Time 4). Such coding fixes the intercept (starting value of the line) at the Time 1 interval, and thus, the conceptual interpretation of the linear change is made relative to this starting point. Reinforcing the notion that there is a set of considerations, one may have a conceptual reason for wanting to fix the intercept to the last measurement occasion. For example, there may be an extensive training program anchored with a “final exam” on the last occasion, and one wants to study the developmental process resulting in the final score. In this case, the coding scheme may be −3, −2, −1, and 0 going from Time 1 to Time 4, respectively ( Bollen & Curran, 2006 , p. 116; Singer & Willett, 2003 , p. 182). One may also have a conceptual reason to use the middle of the time intervals to anchor the intercept and look at the change above and below this point. Thus, the coding scheme in the current example may be −1.5, −0.5, 0.5, and 1.5 for Time 1 to Time 4, respectively ( Bollen & Curran, 2006 ; Singer & Willett, 2003 ). There are other considerations in the “linear set” such as the specification of linear change in cohort designs or other cases where there are individually-varying times of observation (i.e., not everyone started at the same time, at the same age, at the same intervals, etc.). The latter may need to make use of missing data procedures, or the use of time varying covariates that account for the differences as to when observations were collected. For example, to examine how retirement influences life satisfaction, Pinquart and Schindler (2007) modeled life satisfaction data from a representative sample of German retirees who retired between 1985 and 2003. Due to the retirement timing differences among the participants (not everyone retired at the same time or at the same age), different numbers of life satisfaction observations were collected for different retirees. Therefore, the missing observations on a yearly basis were modeled as latent variables to ensure that the analyses were able to cover the entire studied time span.

Discontinuous change is the second set of functional form with which one could theoretically describe the change in one’s substantive focal variables. Discontinuities are precipitous events that may cause the focal variable to rapidly accelerate (change in slope) or to dramatically increase/decrease in value (change in elevation) or both change in slope and elevation (see Ployhart & Vandenberg, 2010 , Figure 1 in p. 100; Singer & Willett, 2003 , pp. 190–208, see Table 6.2 in particular). For example, according to the stage theory ( Wang et al. , 2011 ), retirement may be such a precipitous event, because it can create an immediate “honeymoon effect” on retirees, dramatically increasing their energy-level and satisfaction with life as they pursue new activities and roles.

This set of discontinuous functional form has also been referred to as piecewise growth ( Bollen & Curran, 2006 ; Muthén & Muthén, 1998–2012 ), but in general, represents situations where all units of observation are collected at the same time during the time intervals and the discontinuity happens to all units at the same time. It is actually a variant of the linear set, and therefore, could have been presented above as well. To illustrate, assume we are tracking individual performance metrics that had been rising steadily across time, and suddenly the employer announces an upcoming across-the-board bonus based on those metrics. A sudden rise (as in a change in slope) in those metrics could be expected based purely on reinforcement theory. Assume, for example, we had six intervals of measurement, and the bonus announcement was made just after the Time 3 data collection. We could specify two slope or change variables and code the first one as 0, 1, 2, 2, 2, and 2, and code the second slope variable as 0, 0, 0, 1, 2, and 3. The latter specification would then independently examine the linear change in each slope variable. Conceptually, the first slope variable brings the trajectory of change up to the transition point (i.e., the last measurement before the announcement) while the second one captures the change after the transition ( Bollen & Curran, 2006 ). Regardless of whether the variables are latent or observed only, if this is modeled using software such as Mplus ( Muthén & Muthén, 1998–2012 ), the difference between the means of the slope variables may be statistically tested to evaluate whether the post-announcement slope is indeed greater than the pre-announcement slope. One may also predict that the announcement would cause an immediate sudden elevation in the performance metric as well. This can be examined by including a dummy variable which is zero at all time points prior to the announcement and one at all time points after the announcement ( Singer & Willett, 2003 , pp. 194–195). If the coefficient for this dummy variable is statistically significant and positive, then it indicates that there was a sudden increase (upward elevation) in value post-transition.

Another form of discontinuous change is one in which the discontinuous event occurs at varying times for the units of observation (indeed it may not occur at all for some) and the intervals for collecting data may not be evenly spaced. For example, assume again that individual performance metrics are monitored across time for individuals in high-demand occupations with the first one collected on the date of hire. Assume as well that these individuals are required to report when an external recruiter approaches them; that is, they are not prohibited from speaking with a recruiter but need to just report when it occurred. Due to some cognitive dissonance process, individuals may start to discount the current employer and reduce their inputs. Thus, a change in slope, elevation, or both may be expected in performance. With respect to testing a potential change in elevation, one uses the same dummy-coded variable as described above ( Singer & Willett, 2003 ). With respect to whether the slopes of the performance metrics differ pre- versus post-recruiter contact, however, requires the use of a time-varying covariate. How this operates specifically is beyond the scope here. Excellent treatments on the topic, however, are provided by Bollen and Curran (2006 , pp. 192–218), and Singer and Willett (2003 , pp. 190–208). In general, a time-varying covariate captures the intervals of measurement. In the current example, this may be the number of days (weeks, months, etc.) from date of hire (when baseline performance was obtained) to the next interval of measurement and all subsequent intervals. Person 1, for example, may have the values 1, 22, 67, 95, 115, and 133, and was contacted after Time 3 on Day 72 from the date of hire. Person 2 may have the values 1, 31, 56, 101, 141, and 160, and was contacted after Time 2 on Day 40 from date of hire. Referring the reader to the specifics starting on page 195 of Singer and Willett (2003) , one would then create a new variable from the latter in which all of the values on this new variable before the recruiting contact are set to zero, and values after that to the difference in days when contact was made to the interval of measurement. Thus, for Person 1, this new variable would have the values 0, 0, 0, 23, 43, and 61, and for Person 2, the values would be 0, 0, 16, 61, 101, and 120. The slope of this new variable represents the increment (up or down) to what the slope would have been had the individuals not been contacted by a recruiter. If it is statistically nonsignificant, then there is no change in slope pre- versus post-recruiter contact. If it is statistically significant, then the slope after contact differed from that before the contact. Finally, while much of the above is based upon a multilevel approach to operationalizing change, Muthén and Muthén (1998–2012 ) offer an SEM approach to time-varying covariates through their Mplus software package.

The final functional form to which the slope or change variable may be coded or specified is nonlinear. As with the other forms, there is a set of nonlinear forms. The simplest in the set is when theory states that the change in the focal variable may be quadratic (curve upward or downward). As such, in addition to the linear slope/change variable, a second change variable is specified in which the values of its slope are fixed to the squared values of the first or linear change variable. Assuming five equally spaced intervals of measurement coded as 0, 1, 2, 3, and 4 on the linear change variable. The values of the second quadratic change variable would be 0, 1, 4, 9, and 16. Theory could state that there is cubic change as well. In that case, a third cubic change variable is introduced with the values of 0, 1, 8, 27, and 64. One problem with the use of quadratic (or even linear change variables) or other polynomial forms as described above is that the trajectories are unbounded functions ( Bollen & Curran, 2006 ); that is, there is an assumption that they tend toward infinity. It is unlikely that most, if any, of the theoretical processes in the social sciences are truly unbounded. If a nonlinear form is expected, operationalizing change using an exponential trajectory is probably the most realistic choice. This is because exponential trajectories are bounded functions in the sense that they approach an asymptote (either growing and/or decaying to asymptote). There are three forms of exponential trajectories: (a) simple where there is explosive growth from asymptote; (b) negative where there is growth to an asymptote; and (c) logistic where this is asymptote at both ends ( Singer & Willett, 2003 ). Obviously, the values of the slope or change variable would be fixed to the exponents most closely representing the form of the curve (see Bollen & Curren, 2006, p. 108; and Singer & Willett, 2003 , Table 6.7, p. 234).

There are other nonlinear considerations as well that belong to this. For example, Bollen and Curran (2006 , p. 109) address the issue of cycles (recurring ups and downs but that follow a general upward or downward trend.) Once more the values of the change variable would be coded to reflect those cycles. Similarly, Singer and Willett (2003 , p. 208) address recoding when one wants to remove through transformations the nonlinearity in the change function to make it more linear. They provide an excellent heuristic on page 211 to guide one’s thinking on this issue.

Statistical Techniques Question 2: In longitudinal research, are there additional issues of measurement error that we need to pay attention to, which are over and above those that are applicable to cross-sectional research?

Longitudinal research should pay special attention to the measurement invariance issue. Chan (1998) and Schmitt (1982) introduced Golembiewski and colleagues’ (1976) notion of alpha, beta, and gamma change to explain why measurement invariance is a concern in longitudinal research. When the measurement of a particular concept retains the same structure (i.e., same number of observed items and latent factors, same value and pattern of factor loadings), change in the absolute levels of the latent factor is called alpha change. Only for this type of change can we draw the conclusion that there is a specific form of growth in a given variable. When the measurement of a concept has to be adjusted over time (i.e., different values or patterns of factor loadings), beta change happens. Although the conceptual meaning of the factor remains the same over measurements, the subjective metric of the concept has changed. When the meaning of a concept changes over time (e.g., having different number of factors or different correlations between factors), gamma change happens. It is not possible to compare difference in absolute levels of a latent factor when beta and gamma changes happen, because there is no longer a stable measurement model for the construct. The notions of beta and gamma changes are particularly important to consider when conducting longitudinal research on aging-related phenomena, especially when long time intervals are used in data collection. In such situations, the risk for encountering beta and gamma changes is higher and can seriously jeopardize the internal and external validity of the research.

Longitudinal analysis is often conducted to examine how changes happen in the same variable over time. In other words, it operates on the “alpha change” assumption. Thus, it is often important to explicitly test measurement invariance before proceeding to model the growth parameters. Without establishing measurement invariance, it is unknown whether we are testing meaningful changes or comparing apples and oranges. A number of references have discussed the procedures for testing measurement invariance in latent variable analysis framework (e.g., Chan, 1998 ; McArdle, 2007 ; Ployhart & Vandenberg, 2010 ). The basic idea is to specify and include the measurement models in the longitudinal model, with either continuous or categorical indicators (see answers to Statistical Techniques #4 below on categorical indicators). With the latent factor invariance assumption, factor loadings across measurement points should be constrained to be equal. Errors from different measurement occasions might correlate, especially when the measurement contexts are very similar over time ( Tisak & Tisak, 2000 ). Thus, the error variances for the same item over time can also be correlated to account for common influences at the item-level (i.e., autocorrelation between items). With the specification of the measurement structure, the absolute changes in the latent variables can then be modeled by the mean structure. It should be noted that a more stringent definition of measurement invariance also requires equal variance in latent factors. However, in longitudinal data this requirement becomes extremely difficult to satisfy, and factor variances can be sample specific. Thus, this requirement is often eased when testing measurement invariance in longitudinal analysis. Moreover, this requirement may even be invalid when the nature of the true change over time involves changes in the latent variance ( Chan, 1998 ).

It is important to note that the mean structure approach not only applies to longitudinal models with three or more measurement points, but also applies to simple repeated measures designs (e.g., pre–post design). Traditional paired sample t tests and within-subject repeated measures ANOVAs do not take into account measurement equivalence, which simply uses the summed scores at two measurement points to conduct a hypothesis test. The mean structure approach provides a more powerful way to test the changes/differences in a latent variable by taking measurement errors into consideration ( McArdle, 2009 ).

However, sometimes it is not possible to achieve measurement equivalence through using the same scales over time. For example, in research on development of cognitive intelligence in individuals from birth to late adulthood, different tests of cognitive intelligence are administrated at different ages (e.g., Bayley, 1956 ). In applied settings, different domain-knowledge or skill tests may be administrated to evaluate employee competence at different stages of their career. Another possible reason for changing measures is poor psychometric properties of scales used in earlier data collection. Previously, researchers have used transformed scores (e.g., scores standardized within each measurement point) before modeling growth curves over time. In response to critiques of these scaling methods, new procedures have been developed to model longitudinal data using changed measurement (e.g., rescoring methods, over-time prediction, and structural equation modeling with convergent factor patterns). Recently, McArdle and colleagues (2009) proposed a joint model approach that estimated an item response theory (IRT) model and latent curve model simultaneously. They provided a demonstration of how to effectively handle changing measurement in longitudinal studies by using this new proposed approach.

I am not sure these issues of measurement error are “over and above” cross-sectional issues as much as that cross-sectional data provide no mechanisms for dealing with these issues, so they are simply ignored at the analysis stage. Unfortunately, this creates problems at the interpretation stage. In particular, issues of random walk variables ( Kuljanin, Braun, & DeShon, 2011 ) are a potential problem for longitudinal data analysis and the interpretation of either cross-sectional or longitudinal designs. Random walk variables are dynamic variables that I mentioned earlier when describing the computational modeling approach. These variables have some value and are moved from that value. The random walk expression comes from the image of a highly inebriated individual, who is in some position, but who staggers and sways from the position to neighboring positions because the alcohol has disrupted the nerve system’s stabilizers. This inebriated individual might have an intended direction (called “the trend” if the individual can make any real progress), but there may be a lot of noise in that path. In the aging and retirement literature, one’s retirement savings can be viewed as a random walk variable. Although the general trend of retirement savings should be positive (i.e., the amount of retirement savings should grow over time), at any given point, the exact amount added/gained into the saving (or withdrawn/loss from the saving) depends on a number of situational factors (e.g., stock market performance) and cannot be consistently predicted. The random walks (i.e., dynamic variables) have a nonindependence among observations over time. Indeed, one way to know if one is measuring a dynamic variable is if one observes a simplex pattern among inter-correlations of the variable with itself over time. In a simplex pattern, observations of the variable are more highly correlated when they are measured closer in time (e.g., Time 1 observations correlate more highly with Time 2 than Time 3). Of course, this pattern can also occur if its proximal causes (rather than itself) is a dynamic variable.

As noted, dynamic or random walk variables can create problems for poorly designed longitudinal research because one may not realize that the level of the criterion ( Y ), say measured at Time 3, was largely near its level at Time 2, when the presumed cause ( X ) was measured. Moreover, at Time 1 the criterion ( Y ) might have been busy moving the level of the “causal” variable ( X ) to the place it is observed at Time 2. That is, the criterion variable ( Y ) at Time 1 is actually causing the presumed causal variable ( X ) at Time 2. For example, performances might affect self-efficacy beliefs such that self-efficacy beliefs end up aligning with performance levels. If one measures self-efficacy after it has largely been aligned, and then later measures the largely stable performance, a positive correlation between the two variables might be thought of as reflecting self-efficacy’s influence on performance because of the timing of measurement (i.e., measuring self-efficacy before performance). This is why the multiple wave measurement practice is so important in passive observational panel studies.

However, the multiple waves of measurement might still create problems for random walk variables, particularly if there are trends and reverse causality. Consider the self-efficacy to performance example again. If performance is trending over time and self-efficacy is following along behind, a within-person positive correlation between self-efficacy and subsequent performance is likely be observed (even if there is no or a weak negative causal effect) because self-efficacy will be relatively high when performance is relatively high and low when performance is low. In this case, controlling for trend or past performance will generally solve the problem ( Sitzmann & Yeo, 2013 ), unless the random walk has no trend. Meanwhile, there are other issues that random walk variables may raise for both cross-sectional and longitudinal research, which Kuljanin et al. (2011) do a very good job of articulating.

A related issue for longitudinal research is nonindependence of observations as a function of nesting within clusters. This issue has received a great deal of attention in the multilevel literature (e.g., Bliese & Ployhart, 2002 ; Singer & Willett, 2003 ), so I will not belabor the point. However, there is one more nonindependence issue that has not received much attention. Specifically, the issue can be seen when a variable is a lagged predictor of itself ( Vancouver, Gullekson, & Bliese, 2007 ). With just three repeated measures or observations, the correlation of the variable on itself will average −.33 across three time points, even if the observations are randomly generated. This is because there is a one-third chance the repeated observations are changing monotonically over the three time points, which results in a correlation of 1, and a two-thirds chance they are not changing monotonically, which results in a correlation of −1, which averages to −.33. Thus, on average it will appear the variable is negatively causing itself. Fortunately, this problem is quickly mitigated by more waves of observations and more cases (i.e., the bias is largely removed with 60 pairs of observations).

Statistical Techniques Question 3: When analyzing longitudinal data, how should we handle missing values?

As reviewed by Newman (2014 ; see in-depth discussions by Enders, 2001 , 2010 ; Little & Rubin, 1987 ; Newman, 2003 , 2009 ; Schafer & Graham, 2002 ), there are three levels of missing data (item level missingness, variable/construct-level missingness, and person-level missingness), two problems caused by missing data (parameter estimation bias and low statistical power), three mechanisms of missing data (missing completely at random/MCAR, missing at random/MAR, and missing not at random/MNAR), and a handful of common missing data techniques (listwise deletion, pairwise deletion, single imputation techniques, maximum likelihood, and multiple imputation). State-of-the-art advice is to use maximum likelihood (ML: EM algorithm, Full Information ML) or multiple imputation (MI) techniques, which are particularly superior to other missing data techniques under the MAR missingness mechanism, and perform as well as—or better than—other missing data techniques under MCAR and MNAR missingness mechanisms (MAR missingness is a form of systematic missingness in which the probability that data are missing on one variable [ Y ] is related to the observed data on another variable [ X ]).

Most of the controversy surrounding missing data techniques involves two misconceptions: (a) the misconception that listwise and pairwise deletion are somehow more natural techniques that involve fewer or less tenuous assumptions than ML and MI techniques do, with the false belief that a data analyst can draw safer inferences by avoiding the newer techniques, and (b) the misconception that multiple imputation simply entails “fabricating data that were not observed.” First, because all missing data techniques are based upon particular assumptions, none is perfect. Also, when it comes to selecting a missing data technique to analyze incomplete data, one of the above techniques (e.g., listwise, pairwise, ML, MI) must be chosen. One cannot safely avoid the decision altogether—that is, abstinence is not an option. One must select the least among evils.

Because listwise and pairwise deletion make the exceedingly unrealistic assumption that missing data are missing completely at random/MCAR (cf. Rogelberg et al. , 2003 ), they will almost always produce worse bias than ML and MI techniques, on average ( Newman & Cottrell, 2015 ). Listwise deletion can further lead to extreme reductions in statistical power. Next, single imputation techniques (e.g., mean substitution, stochastic regression imputation)—in which the missing data are filled in only once, and the resulting data matrix is analyzed as if the data had been complete—are seriously flawed because they overestimate sample size and underestimate standard errors and p -values.

Unfortunately, researchers often get confused into thinking that multiple imputation suffers from the same problems as single imputation; it does not. In multiple imputation, missing data are filled in several different times, and the multiple resulting imputed datasets are then aggregated in a way that accounts for the uncertainty in each imputation ( Rubin, 1987 ). Multiple imputation is not an exercise in “making up data”; it is an exercise in tracing the uncertainty of one’s parameter estimates, by looking at the degree of variability across several imprecise guesses (given the available information). The operative word in multiple imputation is multiple , not imputation.

Longitudinal modeling tends to involve a lot of construct- or variable-level missing data (i.e., omitting answers from an entire scale, an entire construct, or an entire wave of observation—e.g., attrition). Such conditions create many partial nonrespondents, or participants for whom some variables have been observed and some other variables have not been observed. Thus a great deal of missing data in longitudinal designs tends to be MAR (e.g., because missing data at Time 2 is related to observed data at Time 1). Because variable-level missingness under the MAR mechanism is the ideal condition for which ML and MI techniques were designed ( Schafer & Graham, 2002 ), both ML and MI techniques (in comparison to listwise deletion, pairwise deletion, and single imputation techniques) will typically produce much less biased estimates and more accurate hypothesis tests when used on longitudinal designs ( Newman, 2003 ). Indeed, ML missing data techniques are now the default techniques in LISREL, Mplus, HLM, and SAS Proc Mixed. It is thus no longer excusable to perform discrete-time longitudinal analyses ( Figure 2 ) without using either ML or MI missing data techniques ( Enders, 2010 ; Graham, 2009 ; Schafer & Graham, 2002 ).

Lastly, because these newer missing data techniques incorporate all of the available data, it is now increasingly important for longitudinal researchers to not give up on early nonrespondents. Attrition need not be a permanent condition. If a would-be respondent chooses not to reply to a survey request at Time 1, the researcher should still attempt to collect data from that person at Time 2 and Time 3. More data = more useful information that can reduce bias and increase statistical power. Applying this advice to longitudinal research on aging and retirement, it means that even when a participant fails to provide responses at some measurement points, continuing to make an effort to collect more data from the participant in subsequent waves may still be worthwhile. It will certainly help combat the issue of attrition and allow more usable data to emerge from the longitudinal data collection.

Statistical Techniques Question 4: Most of existing longitudinal research focuses on studying quantitative change over time. What if the variable of interest is categorical or if the changes over time are qualitative in nature?

I think there are two questions here: How to model longitudinal data of categorical variables, and how to model discontinuous change patterns of variables over time. In terms of longitudinal categorical data, there are two types of data that researchers typically encounter. One type of data comes from measuring a sample of participants on a categorical variable at a few time points (i.e., panel data). The research question that drives the data analyses is to understand the change of status from one time point to the next. For example, researchers might be interested in whether a population of older workers would stay employed or switch between employed and unemployed statuses (e.g., Wang & Chan, 2011 ). To answer this question, employment status (employed or unemployed) of a sample of older workers might be measured five or six times over several years. When transition between qualitative statuses is of theoretical interest, this type of panel data can be modeled via Markov chain models. The simplest form of Markov chain models is a simple Markov model with a single chain, which assumes (a) the observed status at time t depends on the observed status at time t –1, (b) the observed categories are free from measurement error, and (c) the whole population can be described by a single chain. The first assumption is held by most if not all Markov chain models. The other two assumptions can be released by using latent Markov chain modeling (see Langeheine & Van de Pol, 2002 for detailed explanation).

The basic idea of latent Markov chains is that observed categories reflect the “true” status on latent categorical variables to a certain extent (i.e., the latent categorical variable is the cause of the observed categorical variable). In addition, because the observations may contain measurement error, a number of different observed patterns over time could reflect the same underlying latent transition pattern in qualitative status. This way, a large number of observed patterns (e.g., a maximum of 256 patterns of a categorical variable with four categories measured four times) can be reduced into reflecting a small number of theoretically coherent patterns (e.g., a maximum of 16 patterns of a latent categorical variable with two latent statuses over four time points). It is also important to note that subpopulations in a larger population can follow qualitatively different transition patterns. This heterogeneity in latent Markov chains can be modeled by mixture latent Markov modeling, a technique integrating latent Markov modeling and latent class analysis (see Wang & Chan, 2011 for technical details). Given that mixture latent Markov modeling is a part of the general latent variable analysis framework ( Muthén, 2001 ), mixture latent Markov models can include different types of covariates and outcomes (latent or observed, categorical or continuous) of the subpopulation membership as well as the transition parameters of each subpopulation.

Another type of longitudinal categorical data comes from measuring one or a few study units on many occasions separated by the same time interval (e.g., every hour, day, month, or year). Studies examining this type of data mostly aim to understand the temporal trend or periodic tendency in a phenomenon. For example, one can examine the cyclical trend of daily stressful events (occurred or not) over several months among a few employees. The research goal could be to reveal multiple cyclical patterns within the repeated occurrences in stressful events, such as daily, weekly, and/or monthly cycles. Another example is the study of performance of a particular player or a sports team (i.e., win, lost, or tie) over hundreds of games. The research question could be to find out time-varying factors that could account for the cyclical patterns of game performance. The statistical techniques typically used to analyze this type of data belong to the family of categorical time series analyses . A detailed technical review is beyond the current scope, but interested readers can refer to Fokianos and Kedem (2003) for an extended overview.

In terms of modeling discontinuous change patterns of variables, Singer and Willett (2003) and Bollen and Curran (2006) provided guidance on modeling procedures using either the multilevel modeling or structural equation modeling framework. Here I briefly discuss two additional modeling techniques that can achieve similar research goals: spline regression and catastrophe models.

Spline regression is used to model a continuous variable that changes its trajectory at a particular time point (see Marsh & Cormier, 2001 for technical details). For example, newcomers’ satisfaction with coworkers might increase steadily immediately after they enter the organization. Then due to a critical organizational event (e.g., the downsizing of the company, a newly introduced policy to weed out poor performers in the newcomer cohort), newcomers’ coworker satisfaction may start to drop. A spline model can be used to capture the dramatic change in the trend of newcomer attitude as a response to the event (see Figure 4 for an illustration of this example). The time points at which the variable changes its trajectory are called spline knots. At the spline knots, two regression lines connect. Location of the spline knots may be known ahead of time. However, sometimes the location and the number of spline knots are unknown before data collection. Different spline models and estimation techniques have been developed to account for these different explorations of spline knots ( Marsh & Cormier, 2001 ). In general, spline models can be considered as dummy-variable based models with continuity constraints. Some forms of spline models are equivalent to piecewise linear regression models and are quite easy to implement ( Pindyck & Rubinfeld, 1998 ).

Hypothetical illustration of spline regression: The discontinuous change in newcomers’ satisfaction with coworkers over time.

Hypothetical illustration of spline regression: The discontinuous change in newcomers’ satisfaction with coworkers over time.

Catastrophe models can also be used to describe “sudden” (i.e., catastrophic) discontinuous change in a dynamic system. For example, some systems in organizations develop from one certain state to uncertainty, and then shift to another certain state (e.g., perception of performance; Hanges, Braverman, & Rentsch, 1991 ). This nonlinear dynamic change pattern can be described by a cusp model, one of the most popular catastrophe models in the social sciences. Researchers have applied catastrophe models to understand various types of behaviors at work and in organizations (see Guastello, 2013 for a summary). Estimation procedures are also readily available for fitting catastrophe models to empirical data (see technical introductions in Guastello, 2013 ).

Statistical Techniques Question 5: Could you speculate on the “next big thing” in conceptual or methodological advances in longitudinal research? Specifically, describe a novel idea or specific data analytic model that is rarely used in longitudinal studies in our literature, but could serve as a useful conceptual or methodological tool for future science in work, aging and retirement.

Generally, but mostly on the conceptual level, I think we will see an increased use of computational models to assess theory, design, and analysis. Indeed, I think this will be as big as multilevel analysis in future years, though the rate at which it will happen I cannot predict. The primary factors slowing the rate of adoption are knowledge of how to do it and ignorance of the cost of not doing it (cf. Vancouver, Tamanini et al. , 2010 ). Factors that will speed its adoption are easy-to-use modeling software and training opportunities. My coauthor and I recently published a tutorial on computational modeling ( Vancouver & Weinhardt, 2012 ), and we provide more details on how to use a specific, free, easy-to-use modeling platform on our web site ( https://sites.google.com/site/motivationmodeling/home ).

On the methodology level I think research simulations (i.e., virtual worlds) will increase in importance. They offer a great deal of control and the ability to measure many variables continuously or frequently. On the analysis level I anticipate an increased use of Bayesian and Hierarchical Bayesian analysis, particularly to assess computational model fits ( Kruschke, 2010 ; Rouder, & Lu, 2005 ; Wagenmakers, 2007 ).

I predict that significant advances in various areas will be made in the near future through the appropriate application of mixture latent modeling approaches. These approaches combine different latent variable techniques such as latent growth modeling, latent class modeling, latent profile analysis, and latent transition analysis into a unified analytical model ( Wang & Hanges, 2011 ). They could also integrate continuous variables and discrete variables, as either predictor or outcome variables, in a single analytical model to describe and explain simultaneous quantitative and qualitative changes over time. In a recent study, my coauthor and I applied an example of a mixture latent model to understand the retirement process ( Wang & Chan, 2011 ). Despite or rather because of the power and flexibility of these advanced mixture techniques to fit diverse models to longitudinal data, I will repeat the caution I made over a decade ago—that the application of these complex models to assess changes over time should be guided by adequate theories and relevant previous empirical findings ( Chan, 1998 ).

My hope or wish for the next big thing is the use of longitudinal methods to integrate the micro and macro domains of our literature on work-related phenomena. This will entail combining aspects of growth modeling with multi-level processes. Although I do not have a particular conceptual framework in mind to illustrate this, my reasoning is based on the simple notion that it is the people who make the place. Therefore, it seems logical that we could, for example, study change in some aspect of firm performance across time as a function of change in some aspect of individual behavior and/or attitudes. Another example could be that we can study change in household well-being throughout the retirement process as a function of change in the two partners’ individual well-being over time. The analytical tools exist for undertaking such analyses. What are lacking at this point are the conceptual frameworks.

I hope the next big thing for longitudinal research will be dynamic computational models ( Ilgen & Hulin, 2000 ; Miller & Page, 2007 ; Weinhardt & Vancouver, 2012 ), which encode theory in a manner that is appropriately longitudinal/dynamic. If most theories are indeed theories of change, then this advancement promises to revolutionize what passes for theory in the organizational sciences (i.e., a computational model is a formal theory, with much more specific, risky, and therefore more meaningful predictions about phenomena—in comparison to the informal verbal theories that currently dominate and are somewhat vague with respect to time). My preferred approach is iterative: (a) authors first collect longitudinal data, then (b) inductively build a parsimonious computational model that can reproduce the data, then (c) collect more longitudinal data and consider its goodness of fit with the model, then (d) suggest possible model modifications, and then repeat steps (c) and (d) iteratively until some convergence is reached (e.g., Stasser, 2000 , 1988 describes one such effort in the context of group discussion and decision making theory). Exactly how to implement all the above steps is not currently well known, but developments in this area can potentially change what we think good theory is.

I am uncertain whether my “next big thing” truly reflects the wave of the future, or if it instead simply reflects my own hopes for where longitudinal research should head in our field. I will play it safe and treat it as the latter. Consistent with several other responses to this question, I hope that researchers will soon begin to incorporate far more complex dynamics of processes into both their theorizing and their methods of analysis. Although process dynamics can (and do) occur at all levels of analysis, I am particularly excited by the prospect of linking them across at least adjacent levels. For example, basic researchers interested in the dynamic aspects of affect recently have begun theorizing and modeling emotional experiences using various forms of differential structural equation or state-space models (e.g. Chow et al. , 2005 ; Kuppens, Oravecz, & Tuerlinckx, 2010 ), and, as the resulting parameters that describe within-person dynamics can be aggregated to higher levels of analysis (e.g., Beal, 2014 ; Wang, Hamaker, & Bergeman, 2012 ), they are inherently multilevel.

Another example of models that capture this complexity and are increasingly used in both immediate and longer-term longitudinal research are multivariate latent change score models ( Ferrer & McArdle, 2010 ; McArdle, 2009 ; Liu et al. , 2016 ). These models extend LGMs to include a broader array of sources of change (e.g., autoregressive and cross-lagged factors) and consequently capture more of the complexity of changes that can occur in one or more variables measured over time. All of these models share a common interest in modeling the underlying dynamic patterns of a variable (e.g., linear, curvilinear, or exponential growth, cyclical components, feedback processes), while also taking into consideration the “shocks” to the underlying system (e.g., affective events, organizational changes, etc.), allowing them to better assess the complexity of dynamic processes with greater accuracy and flexibility ( Wang et al. , 2016 ).

I believe that applying a dynamical systems framework will greatly advance our research. Applying the dynamic systems framework (e.g., DeShon, 2012 ; Vancouver, Weinhardt, & Schmidt, 2010 ; Wang et al. , 2016 ) forces us to more explicitly conceptualize how changes unfold over time in a particular system. Dynamic systems models can also answer the why question better by specifying how elements of a system work together over time to bring about the observed change at the system level. Studies on dynamic systems models also tend to provide richer data and more detailed analyses on the processes (i.e., the black boxes not measured in traditional research) in a system. A number of research design and analysis methods relevant for dynamical systems frameworks are available, such as computational modeling, ESM, event history analyses, and time series analyses ( Wang et al. , 2016 ).

M. Wang’s work on this article was supported in part by the Netherlands Institute for Advanced Study in the Humanities and Social Sciences.

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thesis on longitudinal studies

What (Exactly) Is A Longitudinal Study?

A plain-language explanation & definition (with examples).

By: Derek Jansen (MBA) | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably feeling a bit overwhelmed by all the technical lingo that’s hitting you. If you’ve landed here, chances are one of these terms is “longitudinal study”, “longitudinal survey” or “longitudinal research”.

Worry not – in this post, we’ll explain exactly:

  • What a longitudinal study is (and what the alternative is)
  • What the main advantages of a longitudinal study are
  • What the main disadvantages of a longitudinal study are
  • Whether to use a longitudinal or cross-sectional study for your research

What is a longitudinal study, survey and research?

What is a longitudinal study?

A longitudinal study or a longitudinal survey (both of which make up longitudinal research) is a study where the same data are collected more than once,  at different points in time . The purpose of a longitudinal study is to assess not just  what  the data reveal at a fixed point in time, but to understand  how (and why) things change  over time.

Longitudinal research involves a study where the same data are collected more than once, at different points in time

Example: Longitudinal vs Cross-Sectional

Here are two examples – one of a longitudinal study and one of a cross-sectional study – to give you an idea of what these two approaches look like in the real world:

Longitudinal study: a study which assesses how a group of 13-year old children’s attitudes and perspectives towards income inequality evolve over a period of 5 years, with the same group of children surveyed each year, from 2020 (when they are all 13) until 2025 (when they are all 18).

Cross-sectional study: a study which assesses a group of teenagers’ attitudes and perspectives towards income equality at a single point in time. The teenagers are aged 13-18 years and the survey is undertaken in January 2020.

From this example, you can probably see that the topic of both studies is still broadly the same (teenagers’ views on income inequality), but the data produced could potentially be very different . This is because the longitudinal group’s views will be shaped by the events of the next five years, whereas the cross-sectional group all have a “2020 perspective”. 

Additionally, in the cross-sectional group, each age group (i.e. 13, 14, 15, 16, 17 and 18) are all different people (obviously!) with different life experiences – whereas, in the longitudinal group, each the data at each age point is generated by the same group of people (for example, John Doe will complete a survey at age 13, 14, 15, and so on). 

There are, of course, many other factors at play here and many other ways in which these two approaches differ – but we won’t go down that rabbit hole in this post.

There are many differences between longitudinal and cross-sectional studies

What are the advantages of a longitudinal study?

Longitudinal studies and longitudinal surveys offer some major benefits over cross-sectional studies. Some of the main advantages are:

Patterns  – because longitudinal studies involve collecting data at multiple points in time from the same respondents, they allow you to identify emergent patterns across time that you’d never see if you used a cross-sectional approach. 

Order  – longitudinal studies reveal the order in which things happened, which helps a lot when you’re trying to understand causation. For example, if you’re trying to understand whether X causes Y or Y causes X, it’s essential to understand which one comes first (which a cross-sectional study cannot tell you).

Bias  – because longitudinal studies capture current data at multiple points in time, they are at lower risk of recall bias . In other words, there’s a lower chance that people will forget an event, or forget certain details about it, as they are only being asked to discuss current matters.

Need a helping hand?

thesis on longitudinal studies

What are the disadvantages of a longitudinal study?

As you’ve seen, longitudinal studies have some major strengths over cross-sectional studies. So why don’t we just use longitudinal studies for everything? Well, there are (naturally) some disadvantages to longitudinal studies as well.

Cost  – compared to cross-sectional studies, longitudinal studies are typically substantially more expensive to execute, as they require maintained effort over a long period of time.

Slow  – given the nature of a longitudinal study, it takes a lot longer to pull off than a cross-sectional study. This can be months, years or even decades. This makes them impractical for many types of research, especially dissertations and theses at Honours and Masters levels (where students have a predetermined timeline for their research)

Drop out  – because longitudinal studies often take place over many years, there is a very real risk that respondents drop out over the length of the study. This can happen for any number of reasons (for examples, people relocating, starting a family, a new job, etc) and can have a very detrimental effect on the study.

Some disadvantages to longitudinal studies include higher cost, longer execution time  and higher dropout rates.

Which one should you use?

Choosing whether to use a longitudinal or cross-sectional study for your dissertation, thesis or research project requires a few considerations. Ultimately, your decision needs to be informed by your overall research aims, objectives and research questions (in other words, the nature of the research determines which approach you should use). But you also need to consider the practicalities. You should ask yourself the following:

  • Do you really need a view of how data changes over time, or is a snapshot sufficient?
  • Is your university flexible in terms of the timeline for your research?
  • Do you have the budget and resources to undertake multiple surveys over time?
  • Are you certain you’ll be able to secure respondents over a long period of time?

If your answer to any of these is no, you need to think carefully about the viability of a longitudinal study in your situation. Depending on your research objectives, a cross-sectional design might do the trick. If you’re unsure, speak to your research supervisor or connect with one of our friendly Grad Coaches .

thesis on longitudinal studies

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Home > ETDS > Dissertations and Theses > 3043

Dissertations and Theses

A review of "longitudinal study" in developmental psychology.

Emily H. Finley , Portland State University

Portland State University. Department of Psychology

First Advisor

Frank Wesley

Date of Publication

Document type, degree name.

Master of Science (M.S.) in Psychology

Genetic psychology

10.15760/etd.2856

Physical Description

1 online resource (92 p.)

The purpose of this library research thesis is to review the "longitudinal study" in terms of problems and present use. A preliminary search of the literature on longitudinal method revealed problems centering around two areas: (1) definition of "longitudinal study" and (2) practical problems of method itself. The purpose of this thesis then is to explore through a search of books and journals the following questions:

1. How can “longitudinal study” be defined?

2. What problems are inherent in the study of the same individuals over time and how can these problems be solved?

A third question which emerges from these two is:

3. How is “longitudinal study” being used today?

This thesis differentiates traditional longitudinal study from other methods of study: the cross-sectional study, the time-lag study, the experimental study, the retrospective study, and the study from records. Each of these methods of study is reviewed according to its unique problems and best uses and compared with the longitudinal study. Finally, the traditional longitudinal study is defined as the study: (1) of individual change under natural conditions not controlled by the experimenter, (2) which proceeds over time from the present to the future by measuring the same individuals repeatedly, and (3) which retains individuality of data in analyses.

Some problem areas of longitudinal study are delineated which are either unique to this method or especially difficult. The following problems related to planning the study are reviewed: definition of study objectives, selection of method of study, statistical methods, cost, post hoc analysis and replication of the study, time factor in longitudinal study, and the problem of allowing variables to operate freely. Cultural shift and attrition are especially emphasized. The dilemma is examined which is posed by sample selection with its related problems of randomization and generalizability of the study, together with the problems of repeated measurements and selection of control groups. These problems are illustrated with studies from the literature.

Not only are these problems delineated cut considerable evidence is shown that we have already started to accumulate data that will permit their solution. This paper presents a number of studies which have considered these problems separately or as a side issue of a study on some other topic. Some recommendations for further research in problem areas are suggested.

At the same time that this thesis notes differentiation of the longitudinal study from other studies, it also notes integration of results of longitudinal studies with results of other studies. The tenet adopted here is: scientific knowledge is cumulative and not dependent on one crucial experiment.

Trends in recent longitudinal studies are found to be toward more strict observance of scientific protocols and toward limitation of time and objectives of the study. When objectives of the study are well defined and time is limited to only enough for specified change to take place, many of the problems of longitudinal study are reduced to manageable proportions.

Although modern studies are of improved quality, longitudinal method is not being sufficiently used today to supply the demand for this type of data. Longitudinal study is necessary to answer some of the questions in developmental psychology. We have no alternative but to continue to develop this important research tool.

In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).

A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Psychology.

Persistent Identifier

http://archives.pdx.edu/ds/psu/17811

Recommended Citation

Finley, Emily H., "A review of "longitudinal study" in developmental psychology" (1972). Dissertations and Theses. Paper 3043. https://doi.org/10.15760/etd.2856

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Longitudinal analysis of the UK COVID-19 Psychological Wellbeing Study: Trajectories of anxiety, depression and COVID-19-related stress symptomology

Affiliations.

  • 1 Department of Psychology, Glasgow Caledonian University, Cowcaddens Road, Glasgow G4 0BA, United Kingdom. Electronic address: [email protected].
  • 2 Department of Psychology, Glasgow Caledonian University, Cowcaddens Road, Glasgow G4 0BA, United Kingdom. Electronic address: [email protected].
  • 3 Stress Trauma and Related Conditions (STARC) Research Lab, School of Psychology, Queen's University Belfast, BT9 5BN, United Kingdom. Electronic address: [email protected].
  • 4 Department of Psychology, Glasgow Caledonian University, Cowcaddens Road, Glasgow G4 0BA, United Kingdom. Electronic address: [email protected].
  • 5 Stress Trauma and Related Conditions (STARC) Research Lab, School of Psychology, Queen's University Belfast, BT9 5BN, United Kingdom. Electronic address: [email protected].
  • PMID: 34388511
  • PMCID: PMC8424320
  • DOI: 10.1016/j.psychres.2021.114138

COVID-19 has had a negative impact on the mental health of individuals. The aim of the COVID-19 Psychological Wellbeing Study was to identify trajectories of anxiety, depression and COVID-19-related traumatic stress (CV19TS) symptomology during the first UK national lockdown. We also sought to explore risk and protective factors. The study was a longitudinal, three-wave survey of UK adults conducted online. Analysis used growth mixture modelling and logistic regressions. Data was collected from 1958 adults. A robust 4-class model for anxiety, depression, and CV19TS symptomology distinguished participants in relation to the severity and stability of symptomology. Classes described low and stable and high and stable symptomology, and symptomology that improved or declined across the study period. Several risk and protection factors were identified as predicting membership of classes (e.g., mental health factors, sociodemographic factors and COVID-19 worries). This study reports trajectories describing a differential impact of COVID-19 on the mental health of UK adults. Some adults experienced psychological distress throughout, some were more vulnerable in the early weeks, and for others vulnerability was delayed. These findings emphasise the need for appropriate mental health support interventions to promote improved outcomes in the COVID-19 recovery phase and future pandemics.

Keywords: Anxiety; COVID-19; Depression; Mental health; Traumatic stress; longitudinal studies.

Copyright © 2021. Published by Elsevier B.V.

PubMed Disclaimer

Conflict of interest statement

Trajectories for depression, anxiety and…

Trajectories for depression, anxiety and CV19TS.

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What is a Longitudinal Study? Definition, Types, and Examples

Is your long-term research strategy unclear? Learn how longitudinal studies decode complexity. Read on for insights.

Godi Yeshaswi

May 8, 2024

thesis on longitudinal studies

In this Article

Have you ever wondered why certain questions can only be answered by going back in time?

Imagine being able to predict the long-term success of a new marketing campaign—not necessarily just from the first click-through rates, but from tracking the customer journey month after month or even year after year. This might include tracking brand awareness , purchase behavior, and customer satisfaction at appropriate intervals. This is where longitudinal studies, like time travelers, enter the research world to decode change and development across individuals, populations, and entire societies.

In this blog, we not only define what longitudinal studies are but also explore the different types and find real-world examples that prove the advantage of this research approach. 

What is a Longitudinal Study?

A longitudinal study is a type of research where the scope of the study is observed and studied in the same set of people over a long period of time. This could be from a few weeks to many years.

They are most often found in different fields like health, economics, and medicine. They serve to give knowledge of the passage of events without knowing what is happening.

A company may conduct a study to observe how things change with time without interfering with what's happening. For example, an e-commerce company may ask the same questions to the same people every few months or years to determine whether the advertisement is working or whether more people are falling in love with their products.

Types of Longitudinal Studies

thesis on longitudinal studies

Cohort Studies  

Cohort studies follow specific groups, or cohorts, of individuals over time. These groups usually share a common characteristic, such as being born in the same year or living in the same area. Researchers observe how this group changes and develops, often focusing on the impact of certain exposures or events on their health, behavior, or other outcomes.

Key Points:

Selection: Participants are chosen based on a shared characteristic.

Focus: Studying the effects of exposures or experiences on the group.

Example: The Nurses' Health Study , a large prospective cohort study launched in 1976, has followed over 100,000 female nurses to investigate various risk factors for chronic diseases like heart disease, cancer, and dementia. By observing their health and lifestyle choices over decades, researchers have gained valuable insights into the long-term impact of different factors on health outcomes.

Panel Studies  

Panel studies involve collecting data from the same group of individuals at multiple time points. Unlike cohort studies, panel studies focus on the same people rather than forming groups based on shared characteristics. This allows researchers to examine individual-level changes over time. 

Selection: Representative sample of a larger population.

Focus: Observing general trends and changes within the sample.

Example: The American National Election Studies (ANES) is a long-running panel study that surveys a representative sample of the US population every two years. This allows researchers to track changes in public opinion on various political and social issues over time, revealing trends in voter preferences and societal attitudes.

Retrospective Studies

Retrospective studies look back in time to collect data on past events or behaviors. Researchers gather information from participants about their past experiences and then follow up with them to track outcomes. These studies are useful for investigating long-term effects or rare events. 

Data source: Existing records, medical charts, surveys, etc.

Focus: Analyzing past data to identify trends and associations.

Example: The Danish National Birth Cohort study utilizes existing data from national registries, following all individuals born in Denmark since 1996. Researchers can analyze their health records, educational attainment, and socioeconomic data to identify risk factors for various health conditions. By analyzing historical data over a long period, researchers can investigate the long-term consequences of early-life exposures on health outcomes later in life. This can inform preventative measures and interventions during critical developmental stages. 

Pros & Cons of Longitudinal Studies

Advantages of longitudinal studies, understanding change.

They can give some of the most valuable insights into the way people, population, or phenomena change over time, enabling a researcher to trace trends, patterns, and causal relations that would be invisible in a snapshot view.

Cause-and-effect Insights  

Longitudinal studies—though not conclusive—can add to the understanding of potential cause-and-effect relationships by tracing how changes in one variable herald changes in another.

Rare Events 

They can trace events that are rare and that, in a snapshot study, might not be seen. It provides data about rare occurrences.

Generalizability

Longitudinal studies can yield generalizable results depending on sample size and ways of selecting the sample.

Disadvantages of Longitudinal Studies

Time and resource intensive.

Conducting longitudinal studies can take years and usually requires hundreds of hours of time, resources, and sustained participant engagement. Thus, this is often the major barrier, particularly for long-term studies.

Participants who drop out of a study can also affect generalizability and introduce bias. The researcher will need to develop strategies to minimize attrition and control for possible biases.

Taking repeated data from the same participants can be expensive and will require a substantial amount of funding and logistical planning.

Delayed Results

Due to its extended duration, it may take years to notice meaningful changes and to obtain definitive results. This must be braved with challenges, while the research area requires an immediate solution.

Longitudinal studies have significant advantages in the study of change and development through time. However, there are substantial challenges in the design and conduct of such research that need to be pondered carefully.

Ways to Collect Longitudinal Study Data

When you're planning a study that follows people over time, you have to decide where to get your information from. There are two main options: using data that's already been collected by someone else or collecting your own data.

Using data from other sources means you can access information that's already been gathered by previous studies. This saves you time and money because you don't have to collect it yourself. But the downside is that you're limited to the information that was collected before, and it might not cover everything you're interested in.

If you can't find data that fits your study, you'll have to collect your own. This means gathering information yourself, which can make sure it's exactly what you need. The methods you use to collect data depend on the type of study you're doing. You can use live interviews, surveys, focus group discussions, etc, to collect data.

The key to getting good data is using the right tools to collect it. This helps you get the information quickly and accurately.

Use Cases of Longitudinal Studies

Here are some compelling use cases for longitudinal studies in consumer research:

Tracking Brand Loyalty and Customer Satisfaction

Panel Study: A company can recruit a representative sample of their customers and conduct regular surveys over time. This enables them to track changes in brand awareness, satisfaction levels, and purchase behavior. By seeing how these metrics change, companies can identify trends in customer loyalty, pinpoint areas for improvement, and measure the effectiveness of marketing campaigns.

Understanding Consumer Behavior and Preferences

Cohort Study: A company may focus on a specific customer segment defined by demographics, purchasing habits, or product usage. By following this cohort over time, they are able to observe how preferences, needs, and behaviors change with changing life stages, economic situations, or technological advancements. This helps companies adapt their products, services, and marketing strategies to stay relevant to their target audience.

Measuring the Long-term Impact of Marketing Initiatives

Prospective Cohort Study: A company can introduce a new marketing campaign and recruit a group of customers exposed to it. By following this cohort over time and comparing their behavior to a control group, the company can measure the long-term impact of the campaign on brand awareness, purchase behavior, and customer lifetime value. This allows for evidence-based decisions on future marketing investment and campaign optimization.

Identifying Emerging Trends and Predicting Future Needs

Retrospective and Panel Studies: With the help of historical customer data along with current trends, companies can identify emerging patterns in consumer behavior. This can inform product development, service innovation, and marketing strategies to stay ahead of the curve and anticipate future customer needs.

Personalization and Customer Relationship Management:

Longitudinal Data Gathering: A business that works on continuous data collection on customers' tastes and purchase history and their association with the brand can be better in personalized marketing messages, product recommendations, and other customer service interactions. This facilitates the building of more intimate relationships and, hence, better customer satisfaction and retention.

Monitoring Product Usage and User Engagement

Panel Study: Enlist a sample of users representing the population and monitor usage patterns over some period. This enables users to follow the frequency of use for specific features, how users engage with the product over time, and many other factors. This information can help in the product design, pointing out areas for improvement and tailoring the user experience.

Understanding User Needs and Preferences

Cohort Study: Focus on a specific subset of users defined by demographics, usage patterns, or some other dimension. By following this cohort over time, a company can monitor how their needs, preferences, and expectations are changing as they become experienced users. This information may help in product updates, feature development, and marketing strategies in order to match user needs.

Evaluating the Long-Term Effectiveness of Product Updates

Prospective Cohort Study: Make a specific product update or feature available to one group of users and expose the control group to the product without receiving the update. By monitoring the changes in usage patterns, satisfaction levels, and task completion rates over time, a company may know how effective the update is and what should be improved.

Feature Adoption and User Behavior Trends

Retrospective and Panel Studies: The analysis of historical usage data and current trends brings the emerging patterns of interaction of users with the product. This would help drive future product roadmaps and feature development priorities and even predict emerging user needs.

Personalization and User Experience Optimization

Longitudinal Data Collection: Constant data gathering with respect to user behavior, preferences, and feedback allows for personal product recommendations, feature suggestions, and in-app guidance. This leads to a more engaged and customized user experience; hence, it increases the satisfaction and retention of users.

Longitudinal Studies with Decode Diary Studies

thesis on longitudinal studies

Decode provides a powerful DIY platform that makes it quite easy for researchers to conduct longitudinal studies, providing such rich tools to capture the experiences and behavior of users over long periods. Decode Diary studies allow researchers to conduct longitudinal research using longitudinal surveys, video responses, and image responses, improving the longitudinal research design.

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Frequently Asked Questions

What is a longitudinal study in research.

In a longitudinal study, researchers keep checking the same people over time to see if anything changes. These studies are like watching from the sidelines without trying to change anything, just to see how things naturally evolve.

What is the difference between longitudinal and cross-sectional studies?

The big difference is that cross-sectional studies talk to new groups of people every time they happen, while longitudinal studies stick with the same group of people and watch how they change over time.

What is an example of a longitudinal study?

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A company tracking the career progression and performance of employees participating in a leadership development program over several years. By regularly collecting data on metrics like job performance, promotions, and job satisfaction, the company can evaluate the program's long-term impact on grooming future leaders and improving organizational success.

Yeshaswi is a dedicated and enthusiastic individual with a strong affinity for tech and all things content. When he's not at work, he channels his passion into his love for football, especially for F.C. Barcelona and the GOAT, Lionel Messi. Instead of hitting the town for parties, he prefers to spend quality time cuddling with his Golden Retriever, Oreo.

Product Marketing Specialist

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thesis on longitudinal studies

  • Open access
  • Published: 19 March 2012

A modest start, but a steady rise in research use: a longitudinal study of nurses during the first five years in professional life

  • Lars Wallin 1 , 2 ,
  • Petter Gustavsson 3 ,
  • Anna Ehrenberg 4 &
  • Ann Rudman 3  

Implementation Science volume  7 , Article number:  19 ( 2012 ) Cite this article

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Newly graduated nurses are faced with a challenging work environment that may impede their ability to provide evidence-based practice. However, little is known about the trajectory of registered nurses' use of research during the first years of professional life. Thus, the aim of the current study was to prospectively examine the extent of nurses' use of research during the first five years after undergraduate education and specifically assess changes over time.

Survey data from a prospective cohort of 1,501 Swedish newly graduated nurses within the national LANE study (Longitudinal Analyses of Nursing Education and Entry in Worklife) were used to investigate perceived use of research over the first five years as a nurse. The dependent variables consisted of three single items assessing instrumental, conceptual, and persuasive research use, where the nurses rated their use on a five-point scale, from 'never' (1) to 'on almost every shift' (5). These data were collected annually and analyzed both descriptively and by longitudinal growth curve analysis.

Instrumental use of research was most frequently reported, closely followed by conceptual use, with persuasive use occurring to a considerably lower extent. The development over time showed a substantial general upward trend, which was most apparent for conceptual use, increasing from a mean of 2.6 at year one to 3.6 at year five (unstandardized slope +0.25). However, the descriptive findings indicated that the increase started only after the second year. Instrumental use had a year one mean of 2.8 and a year five mean of 3.5 (unstandardized slope +0.19), and persuasive use showed a year one mean of 1.7 and a year five mean of 2.0 (unstandardized slope +0.09).

There was a clear trend of increasing research use by nurses during their first five years of practice. The level of the initial ratings also indicated the level of research use in subsequent years. However, it took more than two years of professional development before this increase 'kicked in.' These findings support previous research claiming that newly graduated nurses go through a 'transition shock,' reducing their ability to use research findings in clinical work.

Peer Review reports

Healthcare in many countries is facing growing demands from an ageing population, in parallel with decreasing resources. In order to optimize healthcare effectiveness, evidence-based practice has been proposed as a strategy for incorporating new and adequate knowledge into practice [ 1 – 3 ]. The application of the best available evidence in the care of individual patients is in fact crucial for all healthcare organizations to deliver quality care. Evidence-based practice includes consciously applying the best available evidence based on research findings, clinical experience, and patient preferences [ 1 ]. Because nurses are the largest group of healthcare practitioners, their contribution to evidence-based practice is pivotal [ 4 ]. It is, therefore, disquieting that studies indicate that nurses' use of research in their clinical practice varies considerably [ 5 – 7 ]. Additionally, little is known about the transition from education to practice and how nurses' capacity for making use of research findings in clinical practice develops over time. Thus, this study investigated the extent of Swedish nurses' perceived use of research over the first five years of their professional career.

In order to enhance the readiness for a changing healthcare system, Swedish nursing education has shifted from vocational training to a three-year bachelor's degree program, in parallel with developments in many other countries. This shift involves an academic perspective in both content and educational methods, including courses in research methods, a bachelor's thesis, and a move towards more self-directed learning. Newly graduated nurses are thereby expected to posses the skills underpinning evidence-based practice, namely questioning prevailing practices, searching, critically appraising, and using scientific knowledge in clinical practice [ 8 ]. However, research studies and a national audit of higher education show that the academic perspective in education is still deficient and nursing students perceive a gap between the academic and clinical elements of their education [ 9 , 10 ]. These flaws in undergraduate education may impact newly graduated nurses' capacity to base their practice on research findings.

Research utilization is a prominent facet of the concept of evidence-based practice. A definition was proposed by Estabrooks and colleagues: 'that process by which specific research-based knowledge (science) is implemented in practice' [ 11 ]. Research utilization has been conceptualized to comprise instrumental, conceptual, and persuasive use of research. The concepts were developed in social science, e.g ., by Larsen, who proposed that knowledge utilization could be classified as instrumental and conceptual [ 12 ]. Beyer and Trice added symbolic (persuasive) utilization [ 13 ]. These three research utilization concepts were presented to nursing researchers through the work of Stetler [ 14 , 15 ]. Estabrooks continued this work by developing definitions and measures and assessing the constructs in nursing [ 16 ]. Instrumental utilization refers to the concrete application of research to practice in making specific decisions/interventions; conceptual utilization refers to a change in thinking in response to research, but not necessarily in behaviour (an informing and enlightening use); and persuasive utilization refers to the use of specific research findings to convince others.

In a recent systematic review of instruments for measuring research use, Estabrooks' operationalization of instrumental, conceptual, and persuasive use is called 'kinds of research use' [ 17 ]. In another systematic review on extent of nurses' use of research, Squires et al . report five surveys using 'kinds of research use' as a measure [ 7 ]. Research use ranged from moderate-low to high depending on the kind of research use, on average these results represented 5 (using research on half of the shifts) on a 7-point frequency response scale. In Estabrooks' index study, a survey including Canadian staff nurses, scores were highest for conceptual use, followed by instrumental and persuasive use [ 16 ]. Profetto-McGrath et al . found the same distribution of results among Canadian nurses in adult surgical and paediatric care [ 18 ]. Kenny, who investigated nurses in US Army hospitals, also reported a comparable distribution [ 19 ]. Milner and co-workers studied research use among Canadian staff nurses, educators and managers, and reported-in line with the other studies-that conceptual use of research was most frequent, with persuasive being least frequent [ 20 ]. In contrast, in two recent Swedish studies, including two national samples of newly graduated nurses (one to three years after graduation), instrumental research use was most common, followed by conceptual and persuasive use [ 6 , 21 ]. Aside from one of the Swedish studies [ 6 ] that had a one-year longitudinal approach (two measurement waves), we have not been able to identify any study using the 'kinds of research use' measures in a longitudinal study. Looking at studies using other measurement tools for research use, there appear to be a few intervention studies using pre- and post-measurement designs, thus not presenting true longitudinal data having more than two measurement occasions [ 7 ].

As in many other developed countries, Swedish healthcare is challenged by increasing demands on the healthcare system and simultaneously being allocated fewer resources. The number of hospital beds has decreased by 21% during the period from 1999 to 2008. Today, Swedish healthcare has the fewest hospital beds per inhabitant compared to other countries within the OECD (Organization for Economic Co-operation and Development). This has resulted in an estimated number of occupied beds in medical wards of 100% to 105% [ 22 ], i.e ., overcrowding due to limited hospital bed capacity. Newly graduated nurses predominantly work in hospital settings [ 23 ], and are thus exposed to a busy work environment including seriously ill patients, often with multiple diseases and short lengths of stay. Also, the job turnover of nurses is high in many settings [ 24 ], creating a situation where the novice nurse all too soon can become 'the most experienced' at the workplace, adding to a demanding work context. It has been suggested that circumstances during early work life, such as work-related stress and a lack of experiential knowledge, hinder the provision of evidence-based practice [ 25 , 26 ]. The challenge that faces newly graduated nurses in clinical practice has been described as a 'reality shock' [ 27 ] or, more recently, as a 'transition shock' [ 28 , 29 ]. According to the authors advocating the existence of a transition shock, new nurses are confronted with the hierarchical hospital system, characterized by dominant normative behaviours, described as prescriptive, intellectually oppressive, and cognitively restrictive [ 29 ]. Previously adopted school values come into conflict with work life values, and skills such as the critical appraisal of current practices and openness to new knowledge may therefore be difficult to maintain.

In conclusion, newly graduated nurses are faced with a challenging work environment that may affect their ability to apply evidence-based practice. However, little is known about the trajectory of nurses' research use during the first years of clinical practice. Therefore, the aim of this study was to prospectively examine the extent of nurses' use of research during the first five years after undergraduate training and to specifically assess changes over time.

Design and participants

Data from a prospective cohort of 1,501 Swedish newly graduated nurses within the national LANE study (Longitudinal Analyses of Nursing Education and Entry in Worklife) were used to investigate the primary outcome for this report-the extent and course of perceived use of research findings-over the first five years of practice as a nurse. Students from all of the 26 universities providing undergraduate nursing education in Sweden participated in the study, and their estimated time point for graduation was December 2004. The cohort was therefore called EX2004 (EX = examination). In total, 2,331 nursing students were invited to participate in the study while in their second semester of nursing education. 1,702 (73%) gave informed consent, and 1,501 (88%) of those subsequently also entered the profession and continued to participate in the study (Figure 1 ). Data were self-reported and collected through annual postal surveys. (For details of the overall LANE study, see Rudman et al. [ 30 ].)

figure 1

Description of the five data collections, i.e ., sample selection, participant recruitment, consent, timing of follow-ups and the wave response . Work life sample (n = 1501) consisted of the group of nursing students who both entered the profession and participated in the study after education. Year 1: one year after graduation (in 2006), year 2: two years after graduation (in 2007), year 3: three years after graduation (in 2008), year 4: four years after graduation (in 2009), year 5: five years after graduation (in 2010). Abbreviations: INS = Instrumental research use, CON = Conceptual research use, PER = Persuasive research use.

The EX2004 cohort was compared with the total population of Swedish nurses who graduated in the same year to examine representativeness. Six different demographic variables from population-based national registers were tested, namely age, gender, country of birth, residency (large city), marital status, and parenthood. The only difference that was found concerned the proportion of participating females, which was 1% higher (89% versus 88%) than among all the graduating nurses in 2004 [ 30 ].

Descriptions of sample selection, participant recruitment, consent, timing of follow-ups, and wave response rates are presented in Figure 1 . The 1,501 nurses who completed their undergraduate training and continued to participate in the study were assessed annually five times post-graduation- i.e ., after one, two, three, four, and five years-and constituted the longitudinal sample to monitor change over time in the present study (in Figure 1 designated as 'the work life sample' which consisted of the group of nursing students who entered the profession as registered nurses and participated in the study after education). At data collection year one, i.e ., after the first year post-graduation, the 1,501 nurses were on average 32.5 (SD 7.25) years old (ranging from 24 to 56 years). A majority were female (89%), of Swedish background (92%) and had previous experience in the field of healthcare (60%). The samples used for exploring extent of nurses' use of research during the first five years after undergraduate training are reported in the five boxes in the bottom of Figure 1 . The discrepancy in sample size between numbers participating at one, two, three, four, and five years after graduation and the questionnaire responses year one, two, three, four, and five refers to non-responses to the specific research use items. The seemingly high discrepancies between the overall response rates and the research use response rates are related to the fact that nurses who did not work at a specific time point (for example, due to maternity or sick leave) did not fill in the work life section of the survey.

Common patterns of missing data comprised non-response in one of five data collections in work life (n = 223) and subsequent non-response after each of the first three data collections in work life (n = 239; 81 + 95 + 63 respectively). Differences between respondents with complete (across all data collections) and incomplete data were tested for age, gender, non-Swedish origin, social class, previous experience (of university studies, work in the healthcare system, or clinical training), marital status, parenthood, and self-rated health. The magnitude of these associations ( i.e ., the effect sizes) is given as estimated by tetrachoric or polyserial correlations. The associations showed that incomplete data were more frequent among younger participants (r = 0.12; p < 0.001), male participants (r = 0.11; p < 0.001), participants with non-Swedish origin (r = 0.25; p < 0.001), participants not raised in a working class family (r = 0.10; p < 0.001), and participants not entering parenthood during the first five years post-graduation (r = 0.22; p < 0.001). More information on the handling of missing data is presented under 'Auxiliary Variables' below.

Instrument for measuring research use

The LANE questionnaire included three single items assessing instrumental, conceptual, and persuasive research use, originally developed by Estabrooks [ 16 ], and recently labelled as measures of 'kinds of research use' [ 7 , 17 ]. A Canadian version of the items published in 2004 [ 31 ] constituted the foundation for the Swedish translation and adaptation performed by our research group. Each item was structured with a definition of the concept (instrumental, conceptual, or persuasive), followed by three examples of research use exemplifying the current concept. The instrumental use item was phrased as follows:

"Instrumental research use means that you use research findings (nursing or other kinds of research) in a concrete way in providing patient care. Instrumental RU can be based on scientific articles or recommendations in systematic literature reviews, clinical guidelines, protocols or other documents based on research findings. For example:

Assess the risk of pressure ulcers by using the modified Norton Scale.

Use of physiological saline instead of heparin to keep a peripheral vein catheter open.

Use of compression treatment in the treatment of venous leg ulcers."

Respondents were then asked to estimate their extent of research use according to each concept during the previous four working weeks. The response alternatives were 1 = 'never,' 2 = 'on some shifts,' 3 = 'on about half of the working shifts,' 4 = 'on more than half of the working shifts,' 5 = 'on almost every shift,' and 6 = 'don't know.' To evaluate the feasibility and face validity of the Swedish version of the items, a group of clinical nurses reviewed each item. The items were also reviewed by the staff of the technical and language laboratory at Statistics Sweden (SCB). These operations resulted in minor revisions.

The 'kinds of research use' items have been used in multiple studies with consistent findings across studies, which speak for the credibility of the measurement approach. Content validity was assessed in the index study by Estabrooks [ 16 ]. This approach to measure research use was included in a recently published systematic review on psychometric properties of research utilization instruments [ 17 ]. The report covered the following issues. Response processes (clarity and understanding of items and functionality of response scales) have been assessed in four studies and reported as valid. Significant relationships between the research use variables and other variables that theoretically or empirically have been shown to link to research use have been identified in seven studies. Further, in comparison with most other instruments assessing research use, the 'kinds of research use' include clear definitions of the constructs of interest, i.e ., instrumental, conceptual, and persuasive use. This measure is also clear that the focus is on research use itself, not on factors related to research use, which can be confusing component of some other instruments [ 17 ].

Analysis of longitudinal data

Descriptive cross-sectional analyses were conducted on the prevalence of research use at all five data collection waves during work life using SPSS statistics 17.0.

The longitudinal analysis applied a multilevel model (also called the linear mixed model) for change [ 32 ] implemented as latent growth curve modelling in the structural equation modelling framework [ 33 ]. The unconditional growth curve model was used to estimate a linear trajectory for the entire sample ( i.e ., estimating an intercept and a slope), at the same time estimating the amount of individual variability in baseline levels ( i.e ., variance of individual intercepts) and individual variability in the rate of change ( i.e ., variance of individual slopes). In addition, the association ( i.e ., the covariance) between baseline levels and rates of change was estimated. Moreover, the significance of an additional non-linear effect was tested, adding a quadratic effect to this unconditional growth curve model.

The latent growth curve model was estimated using the Mplus 6.0 software program [ 34 ]. In line with the current recommendations on the statistical treatment of longitudinal data [ 35 ], all available research use ratings from the 1,501 respondents were included and full information maximum likelihood estimation was used to estimate parameters in the model in the presence of missing data. In order to evaluate the stability of the estimated effects, models were re-estimated on a sample with complete research use data from all five measurement waves. Models were also estimated using the robust standard error option to correct for non-normality ( i.e ., Robust Maximum Likelihood estimator) and the categorical option to correct for the ordinal nature of the rating data ( i.e ., Robust Weighted Least Squares estimator). Before estimation, an evaluation was made of whether the clustered nature of the data needed to be taken into account (when data collection was initiated the eligible students were nested within 26 different educational institutions). The possible impact of this nesting on future research use was estimated using intraclass correlations (ICC). The correlations were generally around 0 (ICC less than 0.010 in magnitude) and the highest ICC was 0.017 (for conceptual research use one year post-graduation). Thus, these near zero effects of nesting data indicated that no further control for the impact of educational institutions was needed when estimating the effects and sources of individual differences in longitudinal growth [ 36 ].

Model fit was evaluated using multiple fit indices. These indices and proposed cut-off points were chosen on the basis of their performance in Monte Carlo simulations and recommendations based on these simulations [ 37 , 38 ]. Specifically, good model fit was indicated by a standardized root mean square residual (SRMR) below 0.08, a root mean square error of approximation (RMSEA) of around 0.05, a non-significant close fit test (Cfit), and a comparative fit index (CFI) of around 0.95. Misfit due to possible deviation from the linear model was explored by applying an unspecified trajectories growth model [ 38 ]. In such a model, the deviation from the imposed linear trajectory can be evaluated separately for each individual time point.

Auxiliary variables

In order to improve the accuracy and power of the analysis, the methodological literature currently recommends the inclusion of external variables in the estimation process [ 35 ]. Such variables (called auxiliary variables) should be chosen to reflect possible differences among respondents with complete data and incomplete data ( i.e ., reflecting the assumption that data are missing at random). At the same time, these variables should not be related to levels of research use (as this would reflect an indication of data not missing at random).

As described, the comparisons between respondents with complete data across all five years versus those with incomplete data showed that non-response was more frequent among younger participants, male subjects, participants of non-Swedish origin, participants not raised in a working class family, and participants not entering parenthood during the first five years post-graduation. These variables were therefore chosen to be used as auxiliary variables. Furthermore, this set of variables was scrutinized with the aim of finding indications that data were not missing at random, i.e ., that these variables were also related to levels of research use. Associations are given as estimated by polychoric correlations. Among these variables, only gender was associated with future research use (males using research to a lesser extent, correlation about 0.11 across measures and measurement occasions). Thus, this could introduce a bias because male subjects were also more frequently found to be among the non-responders. The methodological literature currently recommends also including such a variable, as its inclusion as an auxiliary variable will reduce-but not completely eliminate-bias in the estimation [ 35 ]. The procedure of including these external (auxiliary) variables was fully automated in the Mplus software program using Graham's saturated correlates approach [ 34 ], which has been shown to improve accuracy without altering the substantive interpretation of the parameters of the latent growth curve model [ 35 ].

Ethical considerations

The Research Ethics Committee at Karolinska Institutet, Sweden, approved the study (KI01-045, 2001-05-14 and 2003-12-29). Initially, informed consent was provided from all respondents. They received information about the study, guaranteeing confidentiality and indicating that participation was voluntary and could be terminated at any time.

The findings on the nurses' ratings of the extent to which they used research in clinical practice during their first five years as nurses are first presented descriptively, focusing on the longitudinal trend of the cross-sectional material. Additionally, we make full use of prospective longitudinal data, and present results from testing linear increase over time using latent growth curve modelling. Note that the results relating to the prevalence of research use refer to the five response categories (Table 1 ), but in the longitudinal modelling we refer to the categories as a scale because data in that analysis is treated as continuous (Figure 2 ).

figure 2

Development of instrumental, conceptual and persuasive research use across the first five years of practice . Estimates from a Latent Growth Model and a revised model (freeing the second time point from being included in the linear model). Extent of research use during the previous four working weeks were assessed on a response scale from 1 to 5 (the y-axis: 1 = 'never,' 2 = 'on some shifts,' 3 = 'on about half of the working shifts,' 4 = 'on more than half of the working shifts,' 5 = 'on almost every shift'). High values indicate high extent of research use and low values indicate low extent.

Five years' longitudinal data on nurses' extent of research use

There was variation in the extent of the three 'kinds of research use' (Table 1 ). Overall, instrumental use of research was the most frequently reported, but there was considerably less difference between instrumental and conceptual use than between instrumental/conceptual and persuasive use of research. Taking findings from the data collection wave five years after graduation as an example, 44% of the nurses rated their instrumental use of research as occurring 'on almost all shifts,' 35% reported conceptual use, and 5% of the respondents reported persuasive use of research 'on almost all shifts.' On the other hand, also using year five data, 67% of the respondents reported infrequent use of persuasive research ( i.e ., 'not at all' and 'on some shifts'), compared to 29% for both instrumental and conceptual use of research.

Instrumental research use

Focusing on instrumental use, the cross-sectional findings over the five years showed that the nurses' use of research initially looked stable and, if anything, lessened as indicated by a small dip (Table 1 ). In the first and second years, a similar proportion of the nurses (34%) reported that they used research instrumentally on 50% or more of their shifts, while 44% of them rated 'never' and 'on some shifts' in year one and 48% in year two. However, in year three there was a rise in frequent users ('50% and above') to 47% of the nurses. This upward trend continued through year five, when 54% of the nurses reported their instrumental use to be this frequent. The infrequent research users ('never' and 'on some shifts') changed from representing 44% of the sample in year two to 29% in year five.

Treating respondents' ratings as continuous data and imposing a linear growth model across the five years of data resulted in a baseline (year one) mean of 2.8 and an unstandardized linear slope of +0.19 (Figure 2a and Table 2 ). The model showed good fit (Table 2 ) and illustrates the finding that nurses went from a moderate level of instrumental research use (a mean of 2.8) to increase their research use (mean of about 3.5) at year five (see Stability of results below for more information on model fit). The model also shows that there was a substantial variability in research use at year one and modest (but still significant) variability in individuals' increase over time. This means that the variability was initially large relative to the variability in change. In addition, there was a correlation between initial levels and change over time, reflecting that the rate of increase was smaller for those with an initially high level of research use. Furthermore, the possible presence of a non-linear effect was tested by adding a quadratic main effect to the growth model. The χ 2 did not decrease significantly and the new added quadratic effect was not statistically significant. Because this new parameter did not significantly improve model fit, no further parameters were added to the model.

Conceptual research use

Conceptual research use exhibited a pattern similar to instrumental use, but at a somewhat lower initial level (Table 1 ). There was also a slight decrease in research use between years one and two. One year after graduation, 23% of the nurses reported conceptual use of research on '50% and above' of their shifts, compared to 21% after two years. Analogous to instrumental use, an increase in conceptual use was reported at year three. Three years after graduation, 40% of the nurses rated their conceptual use of research as occurring on '50% and above' of their work shifts. At year five, this higher level of conceptual research use was reported by 45% of the nurses. Parallel to this, the low users' ratings changed from representing 54% of the sample in year two to 29% of the sample in year five.

Applying the linear growth model to nurses' ratings of conceptual research use generated a mean of 2.6 at year one and an unstandardized linear slope of +0.25 (Figure 2b and Table 2 ). From a moderate level (a mean of 2.6), the conceptual use of research increased to a mean of 3.6 at year five (see Stability of Results below for more information on model fit). Similar to the model of instrumental research use, this model showed a substantial variability in conceptual research use at year one, but only modest variability of individuals' increase of ratings over time. Also similar to the instrumental model, the rate of increase was smaller for those with an initially high level of research use. However, these data must be interpreted with caution because two indices of model fit indicated poor model fit (see below for an exploration of causes of misfit). Finally, the inclusion of a quadratic effect did not improve model fit, and no further parameters were added to the model.

Persuasive research use

It was also possible to identify a pattern of a small dip in persuasive research use from year one to year two, and an incremental increase thereafter, albeit at a substantially lower level compared with instrumental and conceptual use (Table 1 ). Persuasive research use on '50% and above' of the work shifts was stable between years one and two, comprising about 5% of the nurses. At year three, persuasive research use had changed to comprise 11% of the nurses, and this figure remained at a similar level (10%) at year five. The proportion of nurses rating their persuasive use of research at the lower end of the response scale ('never' and 'on some shifts') started with 77% of the nurses at year one and ended at 67% in year five.

Applying the linear growth model to the data on persuasive research use resulted in a year one mean of 1.7 and an unstandardized linear slope of +0.09 (Figure 2c and Table 2 ). Similar to the instrumental model, this model showed good model fit (Table 2 ). From a modest level of using research persuasively (a mean of 1.7 in year one), the nurses reported a modest increase in their persuasive use ending at a mean of about 2.0 at year five (see Stability of Results below for more information on model fit). Similar to the other two models, this model indicated a large variability at year one but a smaller variability in the increase of research use in individuals over time. Again, an inclusion of a quadratic effect did not improve model fit, and no further parameters were added to the model.

Stability of results

In order to evaluate the longitudinal results, the linear growth model was also estimated with other estimators (MLR and WLS) and applied to a smaller sub-sample comprising only those individuals with complete ratings of research use from all data collection waves. As can be seen in Table 2 , model fit and estimated parameters were almost identical regardless of estimator used. In addition, the longitudinal effects estimated for the smaller sample are close, in fact almost identical, to estimates from the larger sample. When using this smaller sub-sample, the only differences that can be observed concern significance levels due to the loss of power.

An exploration of causes of misfit in the longitudinal model for the conceptual research use data was undertaken applying an unspecified trajectories growth model and revealed that ratings from the second year deviated from the linear trend. Not imposing a linear trend between the two first years resulted in a large improvement in model fit (Table 2 and Figure 2 ) The estimated linear trend (when freeing the second time point from being included in the linear model) generated a mean of 2.71 at year one and a linear slope of +0.23. Similar to the original model, this model indicated a substantial variability in conceptual research use at year one but a modest variability of individuals' increase of ratings over time. The same revised model was also applied to the longitudinal data for Instrumental research use and Persuasive research use (Table 2 and Figure 2 ). In similar, there was a 50% reduction in χ 2 and an improvement in model fit.

In this five-year longitudinal study, we found instrumental use of research to be most frequently reported, closely followed by conceptual use. The development over time showed a consistent pattern of upward trend for all three 'kinds of research use,' but with the descriptive findings indicating that the increase in using research only started after the second year. Using the same metric approach as Squires and co-workers [ 7 ], i.e ., dividing the possible range of scores into quartiles: low (1.0 to 1.99), moderate-low (2.0 to 2.99), moderate-high (3.0 to 3.99) and high (4.0 to 5.0), we found that instrumental and conceptual use went from moderate-low in year one to moderate-high in year five, and persuasive use from low in year one to moderate-low in year five. The extent of research use at year five is comparable to what has been reported in other studies using the same measures [ 7 ]. One issue that may be of more interest is whether these levels of research use are 'acceptable.' One could expect that nurses should use research findings on all working shifts (at least instrumentally). For example, presumably nurses should wash their hands between patients on all shifts. However, it must be noted that the items measure the perceived extent of research use, making the scoring dependent on the respondents' awareness of the knowledge base of their clinical practice. This makes it hard to obtain a firm estimation of what levels of research use could optimally be expected.

Several authors have emphasized the importance of successful adjustment to work life for nurses' professional development as well as for quality of patient care [ 39 , 40 ]. Our findings indicate that there was a 'delay,' extending over two years, in the development of professional practice in terms of research use, which might be a manifestation of a 'transition shock.' Using patterns across all three types of research use to address extent of research use, our group previously identified an increase in the number of individuals characterized by a profile of low use of research from year one to year two after graduation [ 6 ]. Consequences of a stressful entry into work life, such as burnout, have frequently been described in the literature [ 5 , 29 , 30 , 41 , 42 ]. The underlying causes may be that the new nurses invest a great deal of time and energy in their professional role in order to manage the situation and adjust for their inexperience. The burden of work, in combination with novice nurses' limited skills, may drain their energy further and result in a downward prioritization of certain work tasks or even lowered ambitions. A recent study on the development of burnout among nurses during the first three years post-graduation showed that the most typical change trend was substantial increases in burnout levels between the first and second years of practice [ 43 ]. As symptoms of stress, like job burnout, have been previously shown to be related to lower research use [ 44 , 45 ], the initial stable low level of research use found in the present study may be a consequence of the newcomers' struggle to handle their inexperience in the intensive and challenging reality of work life.

There was a significant linear increase in all three kinds of research use. This was most prominent for conceptual use, which went from a mean of 2.6 in year one to 3.6 in year five. The reason for this change cannot be fully understood from our data. It could be interpreted in at least two ways. There appears to be a beneficial adaptation to contextual conditions that starts to have an effect after two years, and there are factors in the work context that support this development. The work context comprises plenty of factors that may support or hinder the use of research-based knowledge, such as the behaviour of leaders, evaluation and feedback mechanisms, professional interaction, and the availability of information sources such as research databases and practice guidelines [ 46 , 47 ]. Such components of context may of course facilitate positive development. This would also imply that nursing education does not prepare all nurses sufficiently to use research in a deliberate way; this skill appears to be acquired gradually during professional life. Our findings might also be interpreted in the light of Benner's application of the Dreyfus model of skill acquisition to nursing [ 48 ]. In this model, nurses develop from being novices and advanced beginners to become competent clinicians after two to three years of clinical experience. At this more competent stage of practice, nurses have developed from rule-governed decision-making to the conscious, abstract, analytical contemplation of clinical problems, which could include increased awareness and use of research to guide practice.

Additionally, these findings provide some food for thought on the issue of individual characteristics versus contextual factors related to evidence-based practice. Rycroft-Malone argues for not viewing evidence use as an individual activity; instead, it would be more helpful to see evidence-based practice as a system property acknowledging the importance of context [ 49 ]. We also believe that such a perspective is beneficial for obtaining a better understanding of how research is used in practice. However, our findings indicate that individual characteristics are important ingredients in establishing evidence-based practice. There was a substantial degree of individual variability in research use levels at year one (which was most prominent for instrumental use), but a more modest variability among the respondents in the amount of increase over time. This means that the levels of research use at year one-proximal to undergraduate training-is an important indicator for the level of research use over the first five years. It underlines that individual characteristics, such as attitudes, intentions, skills, and knowledge, which students bring from undergraduate training into work life, play a substantial role in terms of their use of research in their early professional careers. This is in line with the findings of Squires et al. [ 7 ]; attitudes towards research were a determinant of instrumental research use found in four studies using the 'kinds of research use' measure, and additionally found as a determinant in eight studies examining research use in general. It also implies that undergraduate education, at least to some extent, did prepare the nurses to be eventual users of research in their practice.

Methodological considerations

The current study has some obvious strengths. The data cover five consecutive years early on in the subjects' professional careers and constitutes, to the best of our knowledge, the first prospective longitudinal study on nurses' use of research. We have a national sample with good response rates throughout the five data collection waves. The outcome measure-'kinds of research use'-has been the subject of psychometric evaluation and judged to have a number of assets concerning validity [ 17 ]. However, there are issues with measuring research use through self-report. The main problem is that a respondent will base the ratings of extent of research use on what he/she is aware of being research-based, rather than actual use of research. Another limitation is the categorical response scale, causing a lack of precision in the measurement.

In the present study, latent growth curve models were utilized, but analyses of possible causes of inadequate fit indicated that levels of conceptual research use did not show a linear increase from baseline to the second year. A lack of increase, or even a decrease, of conceptual research use appeared when scrutinizing the extent of high and low users in the cross-sectional data. Because this trend was also apparent for the other two dimensions of research use, the same exploratory model was applied as for the conceptual research use data. Again, model fit increased (from good to excellent) when allowing data from the second year to deviate from the linear increasing trend. This non-linear trend could of course reflect idiosyncratic characteristics of our sample, and replications are needed before drawing firm conclusions based on this trend. However, as previously discussed, the first two to three years post-graduation can be very demanding for newly graduated nurses.

There was, as expected, a successive loss of respondents over the five years, but our analyses do not indicate that this would have threatened the internal validity of the study. However, males were more often found among non-respondents and at the same time were also found to report low research use more often. This raises an issue: can the identified trend of increasing research use over the first years of professional life be an indication of a selection bias reflecting that low users leave the cohort? First, the subsample of male subjects is small (less than 10%) and the correlation between gender and response across time is very low (r = 0.11), so this interaction would not produce the longitudinal trends found for research use. Moreover, when all longitudinal data analyses were estimated on both a sample using all available data and on a (possible selective) sample only including respondents with complete data from all data collections, estimated parameters were found to be almost identical in the two samples. Taken together, the stable results, with replication in the two samples, suggest that the longitudinal effects observed in this study are valid.

There was a clear trend of increasing research use by nurses during their first five years of practice. This trend of increase, encompassing all three kinds of use, and a relatively small amount of individual variability in the increase, indicates that the initial level of research use is an important indicator of the level of research use over the first five years. What the students bring from undergraduate training into work life appears to play a substantial role in terms of their use of research in their early professional careers. However, it must be noted that it took more than two years of professional development before this increase 'kicked in.' This 'delay' supports previous research claiming that newly graduated nurses go through a 'transition shock' that, at least initially, reduces their ability to use research findings in clinical work. Our findings emphasize the need to consider both individual characteristics and contextual factors in the promotion of evidence-based practice.

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Acknowledgements

This work was supported by grants from AFA Insurance. LW was supported by the Centre for Caring Sciences at Karolinska Institutet.

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LW contributed to the design of the study and drafted the manuscript. PG contributed to the design of the study, performed the statistical analysis, and helped to draft the manuscript. AE contributed to the design of the study and helped to draft the manuscript. AR contributed to the design of the study, was responsible for acquisition of data, performed the statistical analysis, and helped to draft the manuscript. All authors read and approved the final manuscript.

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Wallin, L., Gustavsson, P., Ehrenberg, A. et al. A modest start, but a steady rise in research use: a longitudinal study of nurses during the first five years in professional life. Implementation Sci 7 , 19 (2012). https://doi.org/10.1186/1748-5908-7-19

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thesis on longitudinal studies

Frequently asked questions

What is an example of a longitudinal study.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Frequently asked questions: Methodology

Attrition refers to participants leaving a study. It always happens to some extent—for example, in randomized controlled trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Action research is conducted in order to solve a particular issue immediately, while case studies are often conducted over a longer period of time and focus more on observing and analyzing a particular ongoing phenomenon.

Action research is focused on solving a problem or informing individual and community-based knowledge in a way that impacts teaching, learning, and other related processes. It is less focused on contributing theoretical input, instead producing actionable input.

Action research is particularly popular with educators as a form of systematic inquiry because it prioritizes reflection and bridges the gap between theory and practice. Educators are able to simultaneously investigate an issue as they solve it, and the method is very iterative and flexible.

A cycle of inquiry is another name for action research . It is usually visualized in a spiral shape following a series of steps, such as “planning → acting → observing → reflecting.”

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

  • Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
  • Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

  • Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analyzing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalizations —often the goal of quantitative research . As such, a snowball sample is not representative of the target population and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones.

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

  • If there is no sampling frame available (e.g., people with a rare disease)
  • If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
  • If the research focuses on a sensitive topic (e.g., extramarital affairs)

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

  • Reproducing research entails reanalyzing the existing data in the same manner.
  • Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 
  • A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
  • A successful replication shows that the reliability of the results is high.

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Purposive and convenience sampling are both sampling methods that are typically used in qualitative data collection.

A convenience sample is drawn from a source that is conveniently accessible to the researcher. Convenience sampling does not distinguish characteristics among the participants. On the other hand, purposive sampling focuses on selecting participants possessing characteristics associated with the research study.

The findings of studies based on either convenience or purposive sampling can only be generalized to the (sub)population from which the sample is drawn, and not to the entire population.

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection, using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity , and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

  • Convergent validity : The extent to which your measure corresponds to measures of related constructs
  • Discriminant validity : The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviors of your research subjects in real world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as “people watching” with a purpose.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.

In statistics, dependent variables are also called:

  • Response variables (they respond to a change in another variable)
  • Outcome variables (they represent the outcome you want to measure)
  • Left-hand-side variables (they appear on the left-hand side of a regression equation)

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

Independent variables are also called:

  • Explanatory variables (they explain an event or outcome)
  • Predictor variables (they can be used to predict the value of a dependent variable)
  • Right-hand-side variables (they appear on the right-hand side of a regression equation).

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups. Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

  • Open-ended and flexible
  • Impossible to answer with “yes” or “no” (questions that start with “why” or “how” are often best)
  • Unambiguous, getting straight to the point while still stimulating discussion
  • Unbiased and neutral

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when: 

  • You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
  • You are constrained in terms of time or resources and need to analyze your data quickly and efficiently.
  • Your research question depends on strong parity between participants, with environmental conditions held constant.

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

  • You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
  • Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

  • You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions.
  • Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
  • You are seeking descriptive data, and are ready to ask questions that will deepen and contextualize your initial thoughts and hypotheses.
  • Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts.

The four most common types of interviews are:

  • Structured interviews : The questions are predetermined in both topic and order. 
  • Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
  • Unstructured interviews : None of the questions are predetermined.
  • Focus group interviews : The questions are presented to a group instead of one individual.

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

  • Inductive generalization : You use observations about a sample to come to a conclusion about the population it came from.
  • Statistical generalization: You use specific numbers about samples to make statements about populations.
  • Causal reasoning: You make cause-and-effect links between different things.
  • Sign reasoning: You make a conclusion about a correlational relationship between different things.
  • Analogical reasoning: You make a conclusion about something based on its similarities to something else.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Triangulation can help:

  • Reduce research bias that comes from using a single method, theory, or investigator
  • Enhance validity by approaching the same topic with different tools
  • Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

  • It’s time-consuming and labor-intensive, often involving an interdisciplinary team.
  • Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

  • Data triangulation : Using data from different times, spaces, and people
  • Investigator triangulation : Involving multiple researchers in collecting or analyzing data
  • Theory triangulation : Using varying theoretical perspectives in your research
  • Methodological triangulation : Using different methodologies to approach the same topic

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure. 

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

In general, the peer review process follows the following steps: 

  • First, the author submits the manuscript to the editor.
  • Reject the manuscript and send it back to author, or 
  • Send it onward to the selected peer reviewer(s) 
  • Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made. 
  • Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardization and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Every dataset requires different techniques to clean dirty data , but you need to address these issues in a systematic way. You focus on finding and resolving data points that don’t agree or fit with the rest of your dataset.

These data might be missing values, outliers, duplicate values, incorrectly formatted, or irrelevant. You’ll start with screening and diagnosing your data. Then, you’ll often standardize and accept or remove data to make your dataset consistent and valid.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimize or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyze, detect, modify, or remove “dirty” data to make your dataset “clean.” Data cleaning is also called data cleansing or data scrubbing.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

In multistage sampling , you can use probability or non-probability sampling methods .

For a probability sample, you have to conduct probability sampling at every stage.

You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

These are four of the most common mixed methods designs :

  • Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions. 
  • Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
  • Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
  • Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

No, the steepness or slope of the line isn’t related to the correlation coefficient value. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes.

To find the slope of the line, you’ll need to perform a regression analysis .

Correlation coefficients always range between -1 and 1.

The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions.

The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation.

These are the assumptions your data must meet if you want to use Pearson’s r :

  • Both variables are on an interval or ratio level of measurement
  • Data from both variables follow normal distributions
  • Your data have no outliers
  • Your data is from a random or representative sample
  • You expect a linear relationship between the two variables

Quantitative research designs can be divided into two main categories:

  • Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
  • Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

The priorities of a research design can vary depending on the field, but you usually have to specify:

  • Your research questions and/or hypotheses
  • Your overall approach (e.g., qualitative or quantitative )
  • The type of design you’re using (e.g., a survey , experiment , or case study )
  • Your sampling methods or criteria for selecting subjects
  • Your data collection methods (e.g., questionnaires , observations)
  • Your data collection procedures (e.g., operationalization , timing and data management)
  • Your data analysis methods (e.g., statistical tests  or thematic analysis )

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.

Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them.

While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy .

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

  • In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
  • In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation reflects the strength and/or direction of the association between two or more variables.

  • A positive correlation means that both variables change in the same direction.
  • A negative correlation means that the variables change in opposite directions.
  • A zero correlation means there’s no relationship between the variables.

Random error  is almost always present in scientific studies, even in highly controlled settings. While you can’t eradicate it completely, you can reduce random error by taking repeated measurements, using a large sample, and controlling extraneous variables .

You can avoid systematic error through careful design of your sampling , data collection , and analysis procedures. For example, use triangulation to measure your variables using multiple methods; regularly calibrate instruments or procedures; use random sampling and random assignment ; and apply masking (blinding) where possible.

Systematic error is generally a bigger problem in research.

With random error, multiple measurements will tend to cluster around the true value. When you’re collecting data from a large sample , the errors in different directions will cancel each other out.

Systematic errors are much more problematic because they can skew your data away from the true value. This can lead you to false conclusions ( Type I and II errors ) about the relationship between the variables you’re studying.

Random and systematic error are two types of measurement error.

Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).

Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently records weights as higher than they actually are).

On graphs, the explanatory variable is conventionally placed on the x-axis, while the response variable is placed on the y-axis.

  • If you have quantitative variables , use a scatterplot or a line graph.
  • If your response variable is categorical, use a scatterplot or a line graph.
  • If your explanatory variable is categorical, use a bar graph.

The term “ explanatory variable ” is sometimes preferred over “ independent variable ” because, in real world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so “explanatory variables” is a more appropriate term.

The difference between explanatory and response variables is simple:

  • An explanatory variable is the expected cause, and it explains the results.
  • A response variable is the expected effect, and it responds to other variables.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

  • A control group that receives a standard treatment, a fake treatment, or no treatment.
  • Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

There are 4 main types of extraneous variables :

  • Demand characteristics : environmental cues that encourage participants to conform to researchers’ expectations.
  • Experimenter effects : unintentional actions by researchers that influence study outcomes.
  • Situational variables : environmental variables that alter participants’ behaviors.
  • Participant variables : any characteristic or aspect of a participant’s background that could affect study results.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

Advantages:

  • Only requires small samples
  • Statistically powerful
  • Removes the effects of individual differences on the outcomes

Disadvantages:

  • Internal validity threats reduce the likelihood of establishing a direct relationship between variables
  • Time-related effects, such as growth, can influence the outcomes
  • Carryover effects mean that the specific order of different treatments affect the outcomes

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

  • Prevents carryover effects of learning and fatigue.
  • Shorter study duration.
  • Needs larger samples for high power.
  • Uses more resources to recruit participants, administer sessions, cover costs, etc.
  • Individual differences may be an alternative explanation for results.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

If something is a mediating variable :

  • It’s caused by the independent variable .
  • It influences the dependent variable
  • When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

There are three key steps in systematic sampling :

  • Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
  • Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
  • Choose every k th member of the population as your sample.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 x 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

  • In single-stage sampling , you collect data from every unit within the selected clusters.
  • In double-stage sampling , you select a random sample of units from within the clusters.
  • In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset.

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

Blinding is important to reduce research bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behavior in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

  • In a single-blind study , only the participants are blinded.
  • In a double-blind study , both participants and experimenters are blinded.
  • In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analyzing the data.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalization .

There are various approaches to qualitative data analysis , but they all share five steps in common:

  • Prepare and organize your data.
  • Review and explore your data.
  • Develop a data coding system.
  • Assign codes to the data.
  • Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

There are five common approaches to qualitative research :

  • Grounded theory involves collecting data in order to develop new theories.
  • Ethnography involves immersing yourself in a group or organization to understand its culture.
  • Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
  • Phenomenological research involves investigating phenomena through people’s lived experiences.
  • Action research links theory and practice in several cycles to drive innovative changes.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

When conducting research, collecting original data has significant advantages:

  • You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
  • You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!

You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .

  • The type of soda – diet or regular – is the independent variable .
  • The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Using careful research design and sampling procedures can help you avoid sampling bias . Oversampling can be used to correct undercoverage bias .

Some common types of sampling bias include self-selection bias , nonresponse bias , undercoverage bias , survivorship bias , pre-screening or advertising bias, and healthy user bias.

Sampling bias is a threat to external validity – it limits the generalizability of your findings to a broader group of people.

A sampling error is the difference between a population parameter and a sample statistic .

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment and situation effect.

The two types of external validity are population validity (whether you can generalize to other groups of people) and ecological validity (whether you can generalize to other situations and settings).

The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it.

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

Longitudinal study Cross-sectional study
observations Observations at a in time
Observes the multiple times Observes (a “cross-section”) in the population
Follows in participants over time Provides of society at a given point

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction and attrition .

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

  • If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
  • If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
  • If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Discrete and continuous variables are two types of quantitative variables :

  • Discrete variables represent counts (e.g. the number of objects in a collection).
  • Continuous variables represent measurable amounts (e.g. water volume or weight).

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

  • The  independent variable  is the amount of nutrients added to the crop field.
  • The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

  • A testable hypothesis
  • At least one independent variable that can be precisely manipulated
  • At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

  • How you will manipulate the variable(s)
  • How you will control for any potential confounding variables
  • How many subjects or samples will be included in the study
  • How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables .

External validity is the extent to which your results can be generalized to other contexts.

The validity of your experiment depends on your experimental design .

Reliability and validity are both about how well a method measures something:

  • Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
  • Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Open Access

Peer-reviewed

Research Article

The enhancive effect of the 2014–2016 El Niño-induced drought on the control of soil-transmitted helminthiases without anthelmintics: A longitudinal study

Roles Conceptualization, Data curation, Investigation, Methodology, Writing – original draft, Writing – review & editing

Affiliations School of Pharmacy, Walailak University, Nakhon Si Thammarat, Thailand, Drug and Cosmetics Excellence Center, Walailak University, Nakhon Si Thammarat, Thailand

Roles Conceptualization, Data curation, Methodology, Writing – review & editing

Affiliation School of Public Health, Walailak University, Nakhon Si Thammarat, Thailand

Roles Conceptualization, Investigation, Writing – review & editing

Affiliations Department of Medical Science, School of Medicine, Walailak University, Nakhon Si Thammarat, Thailand, Research Center in Tropical Pathobiology, Walailak University, Nakhon Si Thammarat, Thailand

Roles Conceptualization, Data curation, Writing – review & editing

Affiliation Department of Environmental Health, Faculty of Public Health, Mulawarman University, Samarinda, East Kalimantan, Indonesia

Roles Conceptualization, Data curation, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations School of Public Health, Walailak University, Nakhon Si Thammarat, Thailand, School of Allied Health Sciences, Walailak University, Nakhon Si Thammarat, Thailand

ORCID logo

  • Attarat Pattanawongsa, 
  • Pattanasak Kammaneechan, 
  • Prasit Na-ek, 
  • Blego Sedionoto, 
  • Witthaya Anamnart

PLOS

  • Published: July 12, 2024
  • https://doi.org/10.1371/journal.pntd.0012331
  • Reader Comments

This is an uncorrected proof.

Fig 1

Soil-transmitted helminthiases (STHs) are common in tropical and subtropical regions. Southern Thailand experiences an extended rainy season, leading to persistently moist soil. This condition supports the life cycle of STHs, hindering effective control due to reinfection and low drug efficacy. We implemented a novel STH control strategy during the dry season aimed at decreasing reinfection rates without enhancing sanitation or hygiene practices. However, there were unexpected, prolonged droughts linked to El Niño events from 2014 to 2016. Additionally, we assessed the effects of these drought conditions on further control measures without the use of anthelmintics.

Methodology/Principal findings

A longitudinal study was conducted from 2012 to 2016. Stool samples collected from 299 participants were analyzed using the Kato-Katz and agar plate culture methods. Participants who tested positive for STHs received a single 400 mg dose of albendazole. The efficacy of the treatment was evaluated three weeks later. To confirm the control measures were implemented during the dry season, we monitored the number of rainy days following albendazole treatment for 52 days, of which 38 were without rain. Follow-up stool examinations were carried out in 2013 and 2016, with no additional doses of albendazole administered. Rainfall and rainy day data, which served as indicators of unexpected droughts due to El Niño, were collected from the nearest local meteorological stations. Before the drought, there was a decrease in STH prevalence in 2013—except for trichuriasis—attributable to the dry season control efforts. Despite these efforts, STH prevalence remained high. Remarkably, in 2016, following the drought period, the prevalence of trichuriasis, which had not changed previously, spontaneously declined without further albendazole treatment compared to 2013.

Furthermore, the prevalence of strongyloidiasis remained unchanged likely due to its low susceptibility to drought conditions, as it can reproduce within hosts. Conversely, the prevalence of other STHs consistently declined. The drought and possible improvements in sanitation and hygiene practices contributed to this decrease by reducing rates of reinfection and new infection and by increasing the natural cure rate. Additionally, some participants infected with hookworms or Trichuris who were not cured by albendazole experienced natural remission.

Conclusions/Significance

Control measures implemented during the dry season, combined with a 14-month-long drought induced by the El Niño event of 2014–2016, and some improvements in sanitation and hygiene practices, contributed to a decrease in both the prevalence and intensity of STHs, except for S . stercoralis . Over time, S . stercoralis is likely to become the predominant species among the STHs.

Author summary

Soil-transmitted helminths (STHs) are prevalent in tropical rainforest regions of southern Thailand, where frequent rainfall can enhance the environmental life cycle of STHs, leading to reinfection. To mitigate this, we implemented control measures during the dry season from 2012 to 2016, aiming to reduce reinfection rates. For STH detection, we used agar plate culture and the Kato-Katz methods to analyze stool samples collected from 299 participants. Only those who tested positive for STHs received a single dose of albendazole, without accompanying health education, sanitation, or hygiene interventions. The effectiveness of the treatment was evaluated three weeks later. Follow-up assessments were conducted in 2013 and 2016 without further administration of albendazole. In 2013, a year after the albendazole treatment, there was a decrease in the prevalence of all STHs, except for trichuriasis. Unexpectedly, the El Niño event from 2014–2016 triggered prolonged droughts from 2014 to 2015, which likely led to a reduction in the prevalence and intensity of all STHs, excluding S . stercoralis . This exception was due to its low natural cure rate (NCR), as it can reproduce within the host. Additionally, the drought likely led to a reduction in the reinfection rate and an increase in the NCR. Notably, the NCR of trichuriasis induced by drought was more significant than that achieved through albendazole treatment. Furthermore, participants infected with hookworm or T . trichiura who were not cured by albendazole experienced natural remission. Improvements in sanitation and hygiene also likely contributed to the decreased prevalence of STHs. Based on these findings, we recommend that the control of STHs, especially in tropical rainforests characterized by long rainy seasons and persistently wet soil, should be strategically timed during the dry season to optimize effectiveness.

Citation: Pattanawongsa A, Kammaneechan P, Na-ek P, Sedionoto B, Anamnart W (2024) The enhancive effect of the 2014–2016 El Niño-induced drought on the control of soil-transmitted helminthiases without anthelmintics: A longitudinal study. PLoS Negl Trop Dis 18(7): e0012331. https://doi.org/10.1371/journal.pntd.0012331

Editor: Qu Cheng, Huazhong University of Science and Technology Tongji Medical College, CHINA

Received: January 3, 2024; Accepted: June 28, 2024; Published: July 12, 2024

Copyright: © 2024 Pattanawongsa et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. All relevant data are within the paper and its Supporting Information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Soil-transmitted helminths (STHs) comprise species such as Ascaris lumbricoides , Trichuris trichiura , hookworm, and Strongyloides stercoralis . In 2010, global infections were estimated at approximately 819 million for A . lumbricoides , 464.4 million for T . trichiura , and 438.9 million for hookworm. Additionally, out of 4.98 million years lived with disability (YLDs) attributed to STHs, 65%, 22%, and 13% were caused by hookworms, A . lumbricoides , and T . trichiura , respectively [ 1 ]. It is estimated that 30–100 million people globally are infected with S . stercoralis [ 2 ]. The hookworm species Necator americanus and Ancylostoma duodenale are particularly noteworthy, with N . americanus being prevalent in Asia, the sub-Saharan region of Africa, and Central and South America, and is the dominant species in our study areas in southern Thailand [ 3 , 4 ]. STHs are most common in tropical and subtropical regions, especially in low-income and rural areas. The transmission of STHs is influenced by several factors including poor sanitation and hygiene, inadequate water supply, urbanization, and climatic and environmental conditions [ 5 , 6 ]. Climatic factors are crucial for the control of STHs as the development and survival of all larval stages require moisture, which is typically maintained by rainfall in the environment where the stool containing these larvae is deposited on wet soil [ 6 , 7 ]. The climate in Nakhon Si Thammarat (NST) on the eastern coast of southern Thailand is classified as tropical rainforest, while other provinces feature tropical monsoon climates. The various parts of Thailand outside these regions generally experience tropical savanna climates [ 8 ]. Consequently, NST receives significantly more rainfall than other provinces on the eastern coast of southern Thailand, which exceeds the average rainfall for the country ( S1 Table ). This heavy rainfall contributes to a 10-month rainy season in the tropical rainforest region of NST, resulting in persistently wet soil throughout most of the year. In contrast, the tropical savanna climate found in most parts of Thailand outside the southern region experiences a shorter, five-month rainy season, followed by a dry season lasting at least seven months, resulting in predominantly dry soil. The prolonged periods of wet soil in NST compared to shorter periods in other regions are likely a critical factor in the 3–10 times higher prevalence of STHs in the southern region than in other parts of Thailand [ 9 , 10 ]. This higher prevalence is likely exacerbated by increased rates of reinfection and reduced efficacy of albendazole (ABZ), which poses significant challenges to our STH control efforts.

The control of STHs includes preventive chemotherapy (PC), public health education, and interventions in water, sanitation, and hygiene (WASH) [ 11 ]. The World Health Organization recommends PC as a primary strategy [ 12 ], although reinfection often occurs within months to years following treatment [ 13 ]. Consequently, combining PC with WASH interventions is advised for more effective control of STHs [ 11 ]. In our study area, all individuals over the age of two received PCs from public health officials in 2006–2007 in accordance with the policy of the Ministry of Public Health. This involved administering a single dose of 400 mg albendazole every six months. Despite these efforts, when we conducted parasitological diagnostics for human strongyloidiasis from 2008 to 2011, the prevalence of STHs remained high. This persistent prevalence can likely be attributed to inadequate sanitation, poor hygiene practices, and impoverished living conditions. The participant information sheet indicated that those testing positive for any helminth eggs would receive anthelmintic treatment. Specifically, albendazole was administered to positive participants three times over three years, corresponding to the long rainy season and the generally wet soil conditions prevalent throughout the study region. We focused on hookworm infections, monitoring both the treatment outcomes and rates of reinfection. Our findings showed that 13–18% of treated participants were not cured by the treatment, and 80–90% of those initially cured experienced reinfection within six months. While reinfected participants were consistently cured with each round of albendazole, those who remained uncured after initial treatments continued to be uncured despite receiving two to three rounds of albendazole. Notably, uncured participants typically exhibited low mean corpuscular volume (MCV) and mean corpuscular hemoglobin (MCH), suggesting they might be carriers of abnormal hemoglobin, whereas cured cases usually had normal MCV and MCH levels. These uncured participants serve as reservoirs for ongoing hookworm transmission. This raises further questions about the impact of climatic factors, such as frequent rainfall in this tropical rainforest area, which may have contributed to the ineffectiveness of previous STH control efforts in 2006–2007. Additionally, we gathered feedback regarding anthelmintic intake. Our findings revealed several challenges: 1) Some adults did not take anthelmintics because their STH infections were asymptomatic; 2) despite receiving anthelmintics, some individuals, including children, chose not to take them; 3) only Ascaris infections provoked concern among participants, as these worms are visible to the naked eye in stool, prompting requests for treatment; 4) The prevalence of ascariasis was below 20%, which was insufficient to motivate widespread participation in PC; 5) economic constraints led people to prioritize meeting their basic daily needs over seeking health interventions. Combined, these factors make the control of STHs through PC challenging, even when we were successful in distributing anthelmintics. In response, our initial focus has shifted towards finding strategies to prevent reinfection. Furthermore, we aim to investigate host factors that may contribute to the low efficacy of ABZ in this context. Addressing both reinfection and ABZ efficacy could significantly enhance the effectiveness of PC in controlling STHs. Currently, our approach remains to treat only those who test positive for STHs, as this at least ensures treatment for those infected and may help mitigate reinfection rates.

Therefore, ABZ treatment was limited to the control group of STH-positive individuals in the community. Due to the pervasive poverty in this area, it is difficult to implement public health education, sanitation, and hygiene interventions. The local population is more focused on meeting their basic needs than on improving sanitation and cleanliness. Poverty, inadequate sanitation and hygiene, ABZ administration being restricted to only positive cases, persistent reinfections, uncured cases, and the challenge of preventing consistently damp soil have all contributed to the ineffectiveness of current control measures. During the driest month of the year, we sought to control hookworms and other STHs, hoping for at least 30 consecutive dry days to potentially dry out the soil. However, achieving this is challenging, as it can rain unexpectedly, even during the dry season, in the tropical rainforest climate of our study area. Notably, a significant drought occurred from February 2014 to March 2015 due to the strongest El Niño events recorded between 2014 and 2016 [ 14 ], but were unable to ascertain how this affected STH control. Our goal was to reduce the reinfection rate of STHs by managing the disease during the dry season and assessing the impact of prolonged drought periods on ongoing control efforts without the use of anthelmintics.

Materials and methods

Ethics statement.

This study received approval from the Ethics Committee in Human Research at Walailak University, Thailand, under approval numbers 12/024 and 15/071. We obtained additional data on the prevalence of STHs for the years 2008 and 2023, under approval numbers 07/009 and 23/028-01, respectively. Data collection specifically related to the prevalence of STHs in school-aged children was approved under number 14/012. Participants reviewed the participant information sheet and provided their written informed consent to participate in the study. For participants under 18, written consent was obtained from their parents.

Study design

From March 2012 to June 2016, we conducted a longitudinal study in Village 11, Mokhalan Subdistrict, Thasala District, NST, Thailand, which is located 8 km from our Walailak University laboratory. The geographic coordinates of the study area are 8° 40’ 0" N, 99° 55’ 54" E ( Fig 1 ).

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  • PPT PowerPoint slide
  • PNG larger image
  • TIFF original image

The maps show respective (left to right) Nakhon Si Thammarat Province, Thasala District, and Mokhalan Subdistrict (● the study area, ▲ station 0747 and ■ station 0064). The maps were created by Microsoft 365 (Excel) with the available data source from https://github.com/prasertcbs/thailand_gis/blob/main/province/province_region.xlsx and https://github.com/prasertcbs/thailand_gis/blob/main/province/province_simplify/province_simplify.shp for the left map, https://github.com/prasertcbs/thailand_gis/blob/main/amphoe/thailand_province_amphoe.xlsx , and https://github.com/prasertcbs/thailand_gis/blob/main/amphoe/thailand_province_amphoe/thailand_province_amphoe.shp for the middle map, and https://github.com/prasertcbs/thailand_gis/blob/main/tambon/thailand_province_amphoe_tambon_simplify.xlsx and https://github.com/prasertcbs/thailand_gis/blob/main/tambon/shapefiles/Nakhon%20Si%20Thammarat/Nakhon%20Si%20Thammarat.shp for the right map (retrieved on 10 April, 2024).

https://doi.org/10.1371/journal.pntd.0012331.g001

The sample size was calculated according to the formula n = Z 2 P(1-P)/e 2 due to the uncertain population size in village 11, which also overlapped with other villages.

n, sample size

Z, Z- score for 95% confidence level = 1.96

P, the expected prevalence of STH infection is 74% = 0.74

1-P, the proportion of the population not infected with STHs 26% = 0.26

e, margin of error or confidence interval = 0.05

Participation in this study was entirely voluntary. The following were excluded:

  • Expectant mothers.
  • School-aged children (SAC) at Wat Mokhalan Primary School, the only primary school where some children in the village studied. This exclusion was due to ongoing PC since 2012, though we previously assessed the prevalence in 5-12-year-old SAC in 2008, 2013, and 2017.
  • Children under five years of age.
  • Senior citizens over 75, a rarely encountered age group in this village.

A total of 299 individuals provided stool samples, which were collected at 6:00 am in a plastic container, stored in a dry box, and transported to the Walailak University laboratory within 10 minutes. The stools were examined within three hours of defecation. Individuals who tested positive for STH eggs received a single 400 mg dose of ABZ. Those testing positive for S . stercoralis alone, or coinfected with other STHs, were administered three consecutive 400 mg doses of ABZ. Stool samples were collected again three weeks after ABZ administration to assess treatment outcomes. ABZ was administered on April 2, 2012, marking the beginning of the dry season. Rain was recorded for 52 days starting on April 3, 2012. In 2013, stool samples from all 299 participants were collected again and examined for hookworms and other STHs. The final follow-up for 282 participants (17 missing) occurred over the next three years, up to 2016. An unexpected drought, attributed to El Niño from 2014 to 2016, affected the study. Therefore, our longitudinal study from 2012 to 2016 inadvertently included a 14-month-long drought intervention. Major events during this period are summarized in Fig 2 .

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https://doi.org/10.1371/journal.pntd.0012331.g002

Laboratory procedure

Kato-Katz (KK) and agar plate culture (APC) methods were used to detect STHs. Duplicate KK was employed to detect eggs of A . lumbricoides , T . trichiura , and hookworm, while triplicate APC was used for detecting S . stercoralis and hookworm larvae. APC was also used to confirm KK’s ability to detect hookworms, as Rhabditis -related eggs can resemble those of hookworms. The KK procedure was as follows [ 15 ]: 50 mg of stool was weighed and placed on a glass slide, covered with a cellophane paper soaked overnight in glycerin-phenol-malachite green reagent, spread using a rubber bar, and left at room temperature for 30 minutes. The smears were then examined, and the number of eggs was calculated in duplicate, with the mean number of eggs multiplied by 20 to express the results as eggs per gram of stool. The APC method was performed as follows [ 16 ]: 2 grams of stool were placed on a nutrient agar plate and incubated at room temperature for six days. Then, 10 milliliters of 10% formalin were added to the agar surface and the plate was further incubated for 30 minutes. The supernatant was transferred to a 15 ml centrifuge tube and centrifuged at 700 × g for 10 minutes. The pellet was examined for larvae and free-living males and females.

Rainfall, rainy days, length of dry spells, maximum temperature and relative humidity

From 2006 to 2016, rainfall data in mm for Thailand, the eastern coast of southern Thailand, NST, Thasala district of NST, and two local meteorological stations were obtained from the Thai Meteorological Department ( https://www.tmd.go.th ) and the National Hydroinformatics Data Center ( https://www.thaiwater.net ). Rainfall records for Thasala were only available from 2010 to 2014, while data from the two local meteorological stations were available from May 2014 onward. The two local stations, stations 0064 and 0747, located 7 km and 8 km south and southeast of the study area respectively ( Fig 1 ), were chosen as reference stations due to their proximity and the completeness of their data. Data on maximum temperature and relative humidity for 2006–2016 were also sourced from https://www.tmd.go.th . A "dry spell" is defined as a sequence of 15 or more consecutive non-rainy days (with no rain or ≤ 1 mm of rain) during the wet season. The term "dry spell length" refers to the number of consecutive non-rainy days between two precipitation events, which affects soil moisture dynamics. In 2014, a significant dry spell occurred, consisting of 51 consecutive non-rainy days.

The number of days per dry spell from 2011 to 2020 in NST can be accessed at https://www.tmd.go.th . Daily rainfall data for NST in 2012 were also retrieved from this source.

Statistical analysis

thesis on longitudinal studies

The chi-square test was used to compare the differences in prevalence of STHs before and after ABZ administration from 2012 to 2013, and before and after the drought period from 2013 to 2016. Paired t-tests were conducted to assess differences in the intensity of infection before and after both ABZ administration and drought treatment. The chi-square test was also employed to evaluate differences in RRs and NIRs before and after the drought, as well as to compare the NCR of S . stercoralis and other STHs. A p-value of 0.05 was considered statistically significant. All statistical analyses were performed using IBM SPSS Statistics (version 29.0.0; New York, USA).

A total of 1,390 people resided in this village, comprising 212 households. Of these, 22 households lacked a latrine ( S2 Table ). Additionally, human excreta and sewage were not properly disposed of; they were removed from septic tanks and deposited at rubber or palm farms a few kilometers from the village.

From 2006 to 2011, prior to the current study, the average yearly rainfall was 2952.3 mm, and there were 178 rainy days. In contrast, during the study period from 2012 to 2016, the rainfall totaled 2494.3 mm and there were 161 rainy days ( S3 Table ). The average maximum temperature from 2006 to 2016 ranged from 33.9°C to 35.3°C. However, during April and May from 2014 to 2016, temperatures ranged from 37.0°C to 38.1°C ( S4 Table ). The average relative humidity from 2006 to 2016 ranged between 80% and 83% ( S5 Table ).

In 2012, the prevalence of STHs was lowest among participants aged 21–30. There was no significant difference in the prevalence of STHs between sexes across all age groups, except for trichuriasis in the 5–10-year age group, where the prevalence in females was significantly higher than in males (P < 0.05) ( Table 1 ).

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https://doi.org/10.1371/journal.pntd.0012331.t001

The prevalence and intensity of STHs according to infection severity did not significantly differ between sexes, except in the 11-20-year age group. In this group, the intensity of light hookworm infection was significantly higher in females than in males (P < 0.05). Additionally, in the 21-30-year age group, the intensity of light hookworm infection was significantly greater in males than in females (P < 0.05) ( S6 Table ).

At ages 11–20 years, the prevalence of total single infections was significantly lower than that of total multiple infections (P < 0.001). Conversely, at ages 21–30 and 51–60 years, the prevalence of total single infections was significantly higher than that of total multiple infections (P < 0.00001 and P < 0.01, respectively) ( Table 2 ).

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https://doi.org/10.1371/journal.pntd.0012331.t002

Effect of albendazole administration during the dry season followed by long-term drought on the prevalence and intensity of STHs

Our current study began in 2012 with the administration of ABZ during the dry season, during which there were 38 days without rain, 13 days of slight rain, and only one day of moderate rain ( S7 Table ). One year later (2013), significant reductions were noted in the prevalence of ascariasis (12.0% vs. 5.7%, P < 0.01), hookworm infection (64.5% vs. 39.8%, P < 0.001), and strongyloidiasis (26.1% vs. 17.7%, P < 0.05) ( Fig 3 and S8 Table ).

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A longitudinal study was conducted from 2012 to 2016 with 299 participants. Albendazole was administered in the dry season to STH-positive participants only in 2012. Two follow-ups were done in 2013 and 2016. El Niño 2014–2016 induced a 14-month-long drought from 2014 to 2015. Prevalence of STHs in 2008 (n = 296), 2019 (n = 304), and 2023 (n = 225), were used for comparison before and after the longitudinal study.

https://doi.org/10.1371/journal.pntd.0012331.g003

The intensity of ascariasis and hookworm infection also decreased significantly (6255 ± 4108.9 vs. 6663.5 ± 12138.7, P < 0.01; 740.3 ± 561.2 vs. 394.9 ± 198.2, P < 0.001) ( S8 Table ). However, the prevalence and intensity of trichuriasis remained unchanged, likely due to the low efficacy of albendazole against this infection. Droughts, triggered by El Niño episodes, occurred between 2014 and 2015. From February-April 2014 to May-December 2014 ( Fig 4 and S3 , S9 and S10 Tables), there was minimal rainfall (1.6, 0, and 3 mm/month in NST; 0, 4, 47, 7, 49.5, 0, and 0 mm/month at local meteorological station 0064; 6.5, 2, 0, 9, 0, and 8.5 mm/month at local meteorological station 0747).

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Monthly rainfall and rainy days in Nakhon Si Thammarat from February to April 2014 were the average measurements of all local meteorological stations. Monthly rainfall and rainy days at local meteorological station 0064 or station 0747 were measured only at station 0064 or station 0747.

https://doi.org/10.1371/journal.pntd.0012331.g004

Additionally, there was a significant lack of rainy days during these periods (3 of 89 days in NST, 8 of 183 days at station 0747, and 14 of 214 days at station 0064). In 2015, limited rainfall continued from January to March (59, 2, and 1 mm at station 0747; 0, 1, and 0 mm at station 0064) with few rainy days (11 of 90 days at station 0747 and 1 of 90 days at station 0064). Yearly rainfall from 2006–2022 in NST Province which was an average of approximately 100 local meteorological stations in the province was shown ( Fig 5 ). El Niño also led to 51 dry spell days in 2014 and 31 in 2015 ( S1 Table ).

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The strongest El Niño event in 2014–2016 induced a long drought in 2014–2015.

https://doi.org/10.1371/journal.pntd.0012331.g005

In 2016, after a three-year follow-up period without treatment, there was a continued decrease in the prevalence of ascariasis and hookworm infection, from 5.7% to 1.8% (P < 0.01) and from 39.8% to 26.2% (P < 0.001), respectively. The intensity of these infections also decreased significantly (from 6664 to 1640, P < 0.05; and from 395 to 205, P < 0.001, respectively). Notably, the previously unchanged prevalence of trichuriasis significantly decreased (from 20.7% to 9.9%, P < 0.001). However, the prevalence of strongyloidiasis remained unchanged. Additionally, the prevalence of infection with any STH continued to decrease following ABZ administration in 2012 and in 2016 (P < 0.001) ( Fig 3 and S8 Table ).

Additionally, supporting data from school-aged children aged 5–12 who had received PC since 2012 are detailed in the S11 Table . Following the drought, similar to the broader community, there was a significant decrease in the prevalence and intensity of ascariasis, trichuriasis, and hookworm infection (from 3.9% to 0%, 15.4% to 5.3%, P < 0.05; and from 42.3% to 10.7%, P < 0.00001, respectively). While the prevalence of strongyloidiasis did not significantly decrease, it showed a downward trend, moving from 10.3% (8/78) in 2013 to 4.0% (3/75) in 2017.

Surveillance data from the community in 2019 indicated a continuing decline in the prevalence of STHs, including strongyloidiasis. Of the 304 randomly sampled participants, 72 were children aged 3–12. Notably, only 3 children (4.2%) tested positive for hookworm eggs, while tests for Ascaris eggs, Trichuris eggs, and Strongyloides larvae were negative across the board. By 2023, data showed a persistent decrease in the prevalence of STHs among adults over 17 years, with ascariasis eliminated, trichuriasis approaching zero, and lower incidences of hookworm infection and strongyloidiasis ( S8 Table ).

Albendazole treatment outcomes versus drought in the treatment of STHs excluding strongyloidiasis

The ABZ cure rates (CRs) for A . lumbricoides , T . trichiura , and hookworm were 100%, 44.4%, and 79.8%, respectively ( S12 Table ). Additionally, ABZ did not cure 45 patients with Trichuris and 39 with hookworm infections. One year after ABZ administration, the reinfection rates (RRs) for ascariasis, trichuriasis, and hookworm infection were 41.7%, 41.7%, and 39.6%, respectively ( Fig 6 ).

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In 2012, ABZ cure rate was calculated three weeks post-treatment. Reinfection and new infection rates were calculated in 2013 and 2016, one or four years post-treatment. The natural cure rate was calculated in 2016, three years after 2013, and one-year post-drought.

https://doi.org/10.1371/journal.pntd.0012331.g006

The new infection rates (NIRs) for these helminthiases and strongyloidiasis were 0.76%, 0.92%, 17.9%, and 1.8%, respectively. Post-drought, the prevalence of ascariasis and hookworm infection significantly decreased, with RRs of 8.3% (P < 0.001) and 3.9% (P < 0.00001), respectively, while the RR for trichuriasis did not significantly change (P > 0.05). The NIR for trichuriasis significantly increased (P < 0.05), whereas the NIR for hookworms significantly decreased (P < 0.001). The natural cure rates (NCRs) in 2016 for T . trichiura and hookworm were 79% (49/62) and 43.7% (52/119), respectively, while that of ascariasis was 100% (17/17). Of the 49 naturally cured trichuriasis cases, seven were reinfected, two were newly infected, and 40 were uncured. The 52 naturally cured hookworm cases included 24 reinfected, 15 newly infected, and 13 uncured cases. Among the 17 naturally cured cases of ascariasis, 15 were reinfected, and 2 were new infections. Despite a low CR, the prevalence of trichuriasis markedly decreased. Notably, the overall NCR for strongyloidiasis was significantly lower than for the other three STHs (P < 0.001) ( Fig 6 and S12 Table ).

In 2013, one year after administering albendazole in the dry season, the prevalence of trichuriasis remained unchanged due to its low CR of 44%, attributed to the limited efficacy of albendazole. In contrast, the prevalence of ascariasis declined significantly, supported by a high CR of 100%. Furthermore, the RRs and NIRs for both parasites showed no significant differences. It is well-documented that a single 400 mg dose of albendazole is highly effective against ascariasis but less so against trichuriasis [ 17 ]. The development of infective Ascaris eggs requires longer periods than those for Trichuris , potentially increasing exposure to Ascaris eggs when temperatures exceed 37°C [ 18 ]. This prolonged exposure to high temperatures, coupled with a lower baseline prevalence, shorter lifespan, and higher cure rate, likely contributed to the more rapid reduction in Ascaris prevalence compared to Trichuris . Furthermore, over the decade from 2005 to 2015, the overall prevalence of ascariasis in Africa decreased by 10%, while trichuriasis saw a modest reduction of only 2% [ 19 ]. The prevalence of hookworm infections also decreased, due to an 80% CR and a RR similar to that of trichuriasis. Moreover, the prevalence of strongyloidiasis decreased, potentially due to the administration of three consecutive doses of albendazole, despite its lower efficacy compared to ivermectin [ 20 ].

In 2016, one year following a drought, there was a noted decrease in the prevalence of ascariasis, trichuriasis, and hookworm infections. However, the prevalence of strongyloidiasis remained unchanged, attributed to its low NCR likely due to the drought’s limited impact. S . stercoralis is capable of reproducing within the host, allowing it to survive for extended periods, up to 65 years [ 21 ]. Unlike other STHs, 5–10% of the rhabditiform larvae of S . stercoralis in stool can develop into infective larvae within 24 hours under the high temperatures in our study area [ 22 ]. This rapid development provides S . stercoralis larvae with as greater opportunity to infect new hosts before succumbing to desiccation compared to other STHs. Additionally, rhabditiform larvae fail to develop if submerged in water [ 23 ] or if the stool is diluted more than 160 times [ 24 ], due to insufficient air or the depletion of essential stool nutrients, respectively. Consequently, S . stercoralis does not thrive in conditions of rainfall as do other STHs. In contrast, while a dry environment outside the host may generally favor the survival of S . stercoralis , it can be detrimental to other STHs. Notably, the El Niño phenomenon contributed to a prolonged drought, followed by dry soil conditions exacerbated by climate change, and possible improvement in sanitation and hygiene practices, which negatively impacted the transmission of S . stercoralis . Three years after this study, the prevalence of S . stercoralis decreased, and continued to decline over the following year. Subsequently, S . stercoralis became the predominant species relative to other STHs. The prevalence of ascariasis, trichuriasis, and hookworm infections decreased following the drought, driven by increased NCRs for these worms, along with decreased RRs of ascariasis and hookworm infections, and reduced NIRs of hookworm. However, the RR of trichuriasis remained unchanged, and its NIR increased, indicating a more widespread presence of Trichuris compared to Ascaris in the study area ( Table 1 ). Additionally, temperatures below 37°C promote the embryonation of Trichuris eggs, facilitating their transmission [ 17 ].

Wet soil can facilitate the development and survival of STHs, especially in rural areas where roads paved with concrete and cement are rare. When infected human stool or excreta are deposited on wet soil, which acts much like a nutrient-rich medium, the eggs can evolve into infective larvae capable of surviving in the environment for weeks or months, depending on the species [ 18 ]. Conversely, placing stool on dry soil can disrupt the development of hookworm rhabditiform larvae, ultimately halting their growth [ 7 ]. Drought conditions cause rapid evaporation of water from stool and hinder water absorption from stool placed on dry soil, disrupting the life cycle of STHs. Under such conditions, all stages of hookworm larval development cease within a few days [ 7 ]. Moreover, while the eggshells of A . lumbricoides and T . trichiura may shield the developing larvae inside from desiccation in dry stool, they cannot protect against high temperatures (> 37°C) [ 6 ]. Additionally, the absence of water during droughts prevents the movement of infective eggs or larvae from one area to another, further interrupting the transmission of STHs. This as well as improvement in sanitation and hygiene practices results in a decrease in RRs and NIRs. The NCRs for T . trichiura - and A . lumbricoides -infected participants were higher than those for hookworm-infected individuals due to the longer lifespan of hookworms. The life spans of A . lumbricoides , T . trichiura , and hookworm are 1–2, 1–2, and 3–4 years, respectively [ 18 ]. While most hookworms have a lifespan of 3–4 years, certain species of hookworms can survive for up to 18 years [ 25 ]. The development of larvae, both inside and outside the egg, was notably diminished during periods of drought [ 26 ]. N . americanus and Ancylostoma species generally require 3–10 days to develop into infective larvae. In contrast, the eggs of A . lumbricoides and T . trichiura need 28–84 and 10–30 days, respectively, to become infective [ 18 ]. Furthermore, the NCRs for reinfected and newly infected cases of Ascaris (17/17), hookworm (39/52), and Trichuris (9/49) suggest possible influence from the drought and potentially from improved hygiene practices, rather than solely from the administration of ABZ in the dry season.

STH control during the dry season may have contributed to a decrease in the prevalence of STHs, with the exception of trichuriasis. However, the prevalence likely increased subsequently because the sample size was only 299 out of the total 1,390 people in the village, leaving most positive STH cases untreated and acting as large reservoirs of infection. A previous intervention in Ghana during the dry season resulted in a significant decrease in the prevalence of Esophagostomum bifurcum and hookworm infections, from 53% to 5.4% and from 86.9% to 36.8%, respectively, after two rounds of mass treatment with a single oral dose of ABZ. The prevalence of both infections further decreased to 0.8% and 23.4%, respectively, after four rounds of treatment [ 27 ]. It is important to note that this was achieved through MDA, whereas our study involved only the oral administration of albendazole to participants who tested positive. Furthermore, in the absence of the El Niño-induced long drought, and/or the improvement in sanitation and hygiene practices, the prevalence in 2016 should have mirrored that of 2012. Additionally, in another study area located on the seashore at Village 4, Thasala Subdistrict, where only trichuriasis is prevalent. The control area located in the village is composed of three houses with roofs linked together, so sunlight cannot expose the narrow sandy ground. Three families have lived here. All of them are relatives with twenty members: seven adults and thirteen children ages two to seven years. All were infected with T . trichiura . People here wash foot before entering their houses. This characteristic makes the soil wet all the time. Young children frequently defecated on the soil. Thus, the life cycle of T . trichiura has been completely maintained. In 2016, one year post-drought, the prevalence of trichuriasis in the village with dry sandy soil was significantly decreased whereas in the control area with artificially wet sandy soil did not decrease ( S13 Table ). It reflected that the prevalence of trichuriasis in the village would not decrease in the presence of rainfall.

In 2016, we wondered why three children previously infected with hookworms and uncured by albendazole had been naturally cured despite frequently playing football barefoot. After that, the children have not been reinfected by any STHs anymore. We observed that the dry playground or soil has often been seen from 2014 to the present due to climate change. While the number of households lacking latrines decreased, excreta disposal was still poor. Excreta from some households was removed from septic tanks and deposited at rubber or palm farms a few kilometers from the village, however, some households deposited excreta near their homes. The excreta samples collected from thirty households were examined and 60% were positive for hookworm infective larvae using agar plate culture, and 10% were positive for Trichuris eggs. The excreta disposal here remains unchanged because of a lack of a disposal system. However, the life cycle of STHs in the excreta ceased under the dry soil conditions in the village (7). Hygiene might be improved in some households not exceeding 20% of the population which was not enough to cease the transmission of hookworms. Furthermore, hygiene depends on the individual. In addition, we followed two isolated areas (not included in the present study) because there were three ABZ uncured cases of hookworm infection over there, one had fifteen members in three households and the other had thirteen members in a household. We observed that both had no latrine, people walked barefoot when going outside, and drank unboiling groundwater. Post-drought the prevalence of STHs decreased despite poor WASH status (S14 and 15 Tables). Hence, we touched on drought from 2014 to 2015 likely to contribute to the successful STH control.

Approximately 30 El Niño events have occurred since 1990, with the three most significant recorded in 1982–1983, 1997–1998, and 2014–2016 [ 28 ]. Additional occurrences were noted from 2018 to 2019, and the phenomenon is ongoing in 2023. The El Niño event of 2014–2016 led to drought across the southern region and throughout Thailand. Droughts associated with El Niño events may contribute to a decrease in the prevalence of STHs in both controlled and uncontrolled areas. Two national surveys on the prevalence of STHs in Thailand conducted in 2009 [ 9 ] and 2019 [ 10 ] indicated a decline in the overall prevalence of hookworm infection and opisthorchiasis, from 6.5% and 8.7% to 4.5% and 2.2%, respectively. In southern Thailand, the prevalence of ascariasis, trichuriasis, and hookworm infection also decreased from 1.7%, 3.9%, and 15.8% to 0.7%, 2.2%, and 9.8%, respectively [ 9 , 10 ]. Additionally, in 2016, two studies involving schoolchildren and adults, and another study of elderly individuals in 2019 in Thasala, NST, Thailand, recorded prevalence rates of ascariasis, trichuriasis, hookworm infection, and strongyloidiasis at 0, 0.3, 10.7, and 0%; 0, 0.3, 8.0, and 0.9%; and 0, 2.1, 10.9, and 3.4%, respectively [ 29 – 31 ]. The studies also indicated that participants remained at a high risk of STH infection due to several factors: open defecation (7.5–18.5%); absence of hand washing before meals (15.5–25.1%); walking barefoot (2.2–54.2%); drinking unboiled or unfiltered water among schoolchildren (50%); and consuming unwashed vegetables (58–94%). Despite the decreasing prevalence and intensity of STHs, similar poor hygiene conditions were observed in our study area. Notably, in 2012, participants aged 21–30 years exhibited the lowest prevalence of STHs ( Table 1 ), possibly due to better self-care associated with young adulthood. Over time, we expected that individuals aged 11–20 years may improve their personal hygiene practices as they mature. Nevertheless, public health education remains essential for enhancing sanitation and hygiene practices to ensure the sustainable control of STHs in southern Thailand.

The first limitation of the present study is the lack of a questionnaire on the hygiene of the participants. However, in most cases, we visited participants’ households and provided knowledge on preventing STHs. Additionally, positive participants, particularly homemakers, often requested information on preventing reinfection after treatment, and we provided guidance accordingly. Despite improvements, as evidenced by the reduction from 22 households without latrines in 2012 to just eight in 2016, people still use excreta from septic tanks as fertilizer, which might impact the prevalence of STHs. The second limitation concerns the small sample size of 299, which might affect the power of statistical analysis and may only be representative of a portion of the population. The third limitation is that parameters affecting infection were not controlled for in the analysis, and the treatment’s effect during the dry season alone can have a substantial impact, as STHs can be controlled when the prevalence falls below a certain threshold. The fourth limitation involves the administration of albendazole to STH-positive participants during the dry season, specifically to manage hookworm reinfection. If there is no prolonged drought, we plan to apply this new knowledge in subsequent preventative campaigns. Lastly, the study combined data from single and multiple infections to calculate the metric of interest. A previous study indicated a positive association between Trichuris , hookworm, and Ascaris . Egg depositions for Ascaris and hookworm were lower in single infections compared to co-infections [ 32 ]. However, all Ascaris cases were cured, regardless of whether they were single or co-infections. Furthermore, 109 participants were co-infected with ten patterns, making statistical analysis challenging due to the small sample size for each pattern.

A control effort in the dry season followed by a 14-month drought induced by El Niño during 2014–2016, along with some improvement in sanitation and hygiene, led to decreases in the prevalence and intensity of STHs, excluding S . stercoralis . There were decreases in both RR and NIR for hookworms, a decrease in the RR for A . lumbricoides , and increases in the NCRs for A . lumbricoides , T . trichiura , and hookworm. These changes contributed to reduced prevalence and intensity levels. Our findings suggest that control of STHs should be initiated at the beginning of the dry season.

Supporting information

S1 table. rainfall in mm in thailand, the eastern coast of southern thailand, nakhon si thammarat (nst) province, thasala district, and 2 local meteorological stations and dry spell length days in nst province..

https://doi.org/10.1371/journal.pntd.0012331.s001

S2 Table. Demographic characteristics of village 11 in the Moklalan Subdistrict of Thasala District, Nakhon Si Thammarat, southern Thailand 2012.

https://doi.org/10.1371/journal.pntd.0012331.s002

S3 Table. Rainfall and rainy days in Nakhon Si Thammarat during 2006–2016.

https://doi.org/10.1371/journal.pntd.0012331.s003

S4 Table. Maximum temperature in Nakhon Si Thammarat during 2006–2016.

https://doi.org/10.1371/journal.pntd.0012331.s004

S5 Table. Relative humidity in Nakhon Si Thammarat during 2006–2016.

https://doi.org/10.1371/journal.pntd.0012331.s005

S6 Table. Prevalence and intensity of 3 soil-transmitted helminths by severity of infection, sex, and age in 2012.

https://doi.org/10.1371/journal.pntd.0012331.s006

S7 Table. Rainfall and rainy days after albendazole intake for 52 days from April 3 to May 24, 2012.

https://doi.org/10.1371/journal.pntd.0012331.s007

S8 Table. Prevalence and intensity (egg/gram of stool) of 4 soil-transmitted helminthiases in participants who received albendazole in 2012 only, living in village 11 and in surrounding areas from 2012–2016.

https://doi.org/10.1371/journal.pntd.0012331.s008

S9 Table. Monthly rainfall and rainy days were measured by station 0747 in Ban Tha Ngam, one of the stations nearest to Village 11 of Mokhalan.

https://doi.org/10.1371/journal.pntd.0012331.s009

S10 Table. Monthly rainfall and rainy days were measured by station 0064 Ban Nai Tub, one of the stations nearest to Village 11 of Mokhalan, and with the most complete data availability since 2014.

https://doi.org/10.1371/journal.pntd.0012331.s010

S11 Table. Prevalence and intensity (egg/gram of stool) of 4 soil-transmitted helminthiases in school-aged children (SAC) at Wat Mokhalan primary school who had received preventive chemotherapy (either a single dose of 400 mg albendazole or 500 mg mebendazole) since 2012.

https://doi.org/10.1371/journal.pntd.0012331.s011

S12 Table. Response of STHs to albendazole 3 weeks after albendazole administration and to drought one year after long-lasting drought.

https://doi.org/10.1371/journal.pntd.0012331.s012

S13 Table. Comparative prevalence of trichuriasis between a control area where wet soil has been artificially maintained and in other areas with dry sandy soil of Village 4 Thasala Subdistrict located at the seashore 2013–2018.

https://doi.org/10.1371/journal.pntd.0012331.s013

S14 Table. Prevalence and intensity (egg/gram, larva/gram of stool) of 4 soil-transmitted helminths in people in isolated area 1 of Village 11, 2008–2019.

https://doi.org/10.1371/journal.pntd.0012331.s014

S15 Table. Prevalence and intensity (egg/gram, larva/gram of stool) of 2 soil-transmitted helminths in people in isolated area 2 of Village 11, 2008–2019.

https://doi.org/10.1371/journal.pntd.0012331.s015

S1 Data. Raw data for S8 Table .

https://doi.org/10.1371/journal.pntd.0012331.s016

S2 Data. Raw data for S12 Table .

https://doi.org/10.1371/journal.pntd.0012331.s017

S3 Data. Numerical value for graphs Figs 3 and 5 .

https://doi.org/10.1371/journal.pntd.0012331.s018

Acknowledgments

We would like to thank Pawilai Dermlim, Malisa Thongrod, and Siraporn Innimit for their technical assistance.

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Understanding the effects of universal test and treat on longitudinal HIV care outcomes among South African youth: a retrospective cohort study

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thesis on longitudinal studies

  • Affiliation: Gillings School of Global Public Health, Department of Epidemiology
  • Other Affiliation: Division of Infectious Diseases, School of Medicine, Washington University in St. Louis, St. Louis, USA
  • Other Affiliation: MRC/Wits Rural Public Health and Health Transitions Research Unit
  • Other Affiliation: Population Council, New York, NY, USA
  • Other Affiliation: College of Nursing, Florida State University, Tallahassee, FL, USA
  • Other Affiliation: Biostatistics Division, School of Public Health, University of California, Berkeley, Berkeley, CA, USA
  • Introduction Little is known about the effects of universal test and treat (UTT) policies on HIV care outcomes among youth living with HIV (YLHIV). Moreover, there is a paucity of information regarding when YLHIV are most susceptible to disengagement from care under the newest treatment guidelines. The longitudinal HIV care continuum is an underutilized tool that can provide a holistic understanding of population-level HIV care trajectories and be used to compare treatment outcomes across groups. We aimed to explore effects of the UTT policy on longitudinal outcomes among South African YLHIV and identify temporally precise opportunities for re-engaging this priority population in the UTT era. Methods Using medical record data, we conducted a retrospective cohort study among youth aged 18–24 diagnosed with HIV from August 2015-December 2018 in nine health care facilities in South Africa. We used Fine and Gray sub-distribution proportional hazards models to characterize longitudinal care continuum outcomes in the population overall and stratified by treatment era of diagnosis. We estimated the proportion of individuals in each stage of the continuum over time and the restricted mean time spent in each stage in the first year following diagnosis. Sub-group estimates were compared using differences. Results A total of 420 YLHIV were included. By day 365 following diagnosis, just 23% of individuals had no 90-or-more-day lapse in care and were virally suppressed. Those diagnosed in the UTT era spent less time as ART-naïve (mean difference=-19.3 days; 95% CI: -27.7, -10.9) and more time virally suppressed (mean difference = 17.7; 95% CI: 1.0, 34.4) compared to those diagnosed pre-UTT. Most individuals who were diagnosed in the UTT era and experienced a 90-or-more-day lapse in care disengaged between diagnosis and linkage to care or ART initiation and viral suppression. Conclusions Implementation of UTT yielded modest improvements in time spent on ART and virally suppressed among South African YLHIV— however, meeting UNAIDS’ 95-95-95 targets remains a challenge. Retention in care and re-engagement interventions that can be implemented between diagnosis and linkage to care and between ART initiation and viral suppression (e.g., longitudinal counseling) may be particularly important to improving care outcomes among South African YLHIV in the UTT era.
  • Black People
  • Retrospective Studies
  • South Africa
  • HIV Infections
  • https://doi.org/10.17615/gqpq-x957
  • https://doi.org/10.1186/s12889-023-16353-9
  • Attribution 4.0 International
  • BMC Public Health
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10 Famous Examples of Longitudinal Studies

10 Famous Examples of Longitudinal Studies

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

Learn about our Editorial Process

longitudinal studies examples and definition, explained below

A longitudinal study is a study that observes a subject or subjects over an extended period of time. They may run into several weeks, months, or years. An examples is the Up Series which has been going since 1963.

Longitudinal studies are deployed most commonly in psychology and sociology, where the intention is to observe the changes in the subject over years, across a lifetime, and sometimes, even across generations.

There have been several famous longitudinal studies in history. Some of the most well-known examples are listed below.

Examples of Longitudinal Studies

1. up series.

Duration: 1963 to Now

The Up Series is a continuing longitudinal study that studies the lives of 14 subjects in Britain at 7-year intervals.

The study is conducted in the form of interviews in which the subjects report the changes that have occurred in their lives in the last 7 years since the last interview.

The interviews are filmed and form the subject matter of the critically acclaimed Up series of documentary films directed by Michael Apsted. 

When it was first conceived, the aim of the study was to document the life progressions of a cross-section of British children through the second half of the 20th century in light of the rapid social, economic, political, and demographic changes occuring in Britain.

14 children were selected from different socio-economic backgrounds for the first study in 1963 in which all were 7 years old.

The latest installment was filmed in 2019 by which time the participants had reached 63 years of age. 

The study noted that life outcomes of subjects were determined to a large extent by their socio-economic and demographic circumstances, and that chances for upward mobility remained limited in late 20th century Britain (Pearson, 2012).

2. Minnesota Twin Study

Duration: 1979 to 1990 (11 years)

Siblings who are twins not only look alike but often display similar behavioral and personality traits.

This raises an oft-asked question: how much of this similarity is genetic and how much of it is the result of the twins growing up together in a similar environment. 

The Minnesota twin study was a longitudinal study that set out to find an answer to this question by studying a group of twins from 1979 to 1990 under the supervision of Thomas J Bouchard.

The study found that identical twins who were reared apart in different environments did not display any greater chances of being different from each other than twins that were raised in the same environment.

The study concluded that the similarities and differences between twins are genetic in nature, rather than being the result of their environment (Bouchard et. al., 1990).

3. Grant Study

Duration: 1942 – Present

The Grant Study is one of the most ambitious longitudinal studies. It attempts to answer a philosophical question that has been central to human existence since the beginning of time – what is the secret to living a good life? (Shenk, 2009).

It does so by studying the lives of 268 male Harvard graduates who are interrogated at least every two years with the help of questionnaires, personal interviews, and gleaning information about their physical and mental well-being from their physicians.

Begun in 1942, the study continues to this day.

The study has provided researchers with several interesting insights into what constitutes the human quality of life. 

For instance:

  • It reveals that the quality of our relationships is more influential than IQ when it comes to our financial success.
  • It suggests that our relationships with our parents during childhood have a lasting impact on our mental and physical well-being until late into our lives.

In short, the results gleaned from the study (so far) strongly indicate that the quality of our relationships is one of the biggest factors in determining our quality of life. 

4. Terman Life Cycle Study

Duration: 1921 – Present

The Terman Life-Cycle Study, also called the Genetic Studies of Genius, is one of the longest studies ever conducted in the field of psychology.

Commenced in 1921, it continues to this day, over 100 years later!

The objective of the study at its commencement in 1921 was to study the life trajectories of exceptionally gifted children, as measured by standardized intelligence tests.

Lewis Terman, the principal investigator of the study, wanted to dispel the then-prevalent notion that intellectually gifted children tended to be:

  • socially inept, and
  • physically deficient

To this end, Terman selected 1528 students from public schools in California based on their scores on several standardized intelligence tests such as the Stanford-Binet Intelligence scales, National Intelligence Test, and the Army Alpha Test.

It was discovered that intellectually gifted children had the same social skills and the same level of physical development as other children.

As the study progressed, following the selected children well into adulthood and in their old age, it was further discovered that having higher IQs did not affect outcomes later in life in a significant way (Terman & Oden, 1959).

5. National Food Survey

Duration: 1940 to 2000 (60 years)

The National Food Survey was a British study that ran from 1940 to 2000. It attempted to study food consumption, dietary patterns, and household expenditures on food by British citizens.

Initially commenced to measure the effects of wartime rationing on the health of British citizens in 1940, the survey was extended and expanded after the end of the war to become a comprehensive study of British dietary consumption and expenditure patterns. 

After 2000, the survey was replaced by the Expenditure and Food Survey, which lasted till 2008. It was further replaced by the Living Costs and Food Survey post-2008. 

6. Millennium Cohort Study

Duration: 2000 to Present

The Millennium Cohort Study (MCS) is a study similar to the Up Series study conducted by the University of London.

Like the Up series, it aims to study the life trajectories of a group of British children relative to the socio-economic and demographic changes occurring in Britain. 

However, the subjects of the Millenium Cohort Study are children born in the UK in the year 2000-01.

Also unlike the Up Series, the MCS has a much larger sample size of 18,818 subjects representing a much wider ethnic and socio-economic cross-section of British society. 

7. The Study of Mathematically Precocious Youths

Duration: 1971 to Present

The Study of Mathematically Precocious Youths (SMPY) is a longitudinal study initiated in 1971 at the Johns Hopkins University.

At the time of its inception, the study aimed to study children who were exceptionally gifted in mathematics as evidenced from their Scholastic Aptitude Test (SAT) scores.

Later the study shifted to Vanderbilt University and was expanded to include children who scored exceptionally high in the verbal section of the SATs as well.

The study has revealed several interesting insights into the life paths, career trajectories, and lifestyle preferences of academically gifted individuals. For instance, it revealed:

  • Children with exceptionally high mathematical scores tended to gravitate towards academic, research, or corporate careers in the STEM fields.
  • Children with better verbal abilities went into academic, research, or corporate careers in the social sciences and humanities .

8. Baltimore Longitudinal Study of Aging

Duration: 1958 to Present

The Baltimore Longitudinal Study of Aging (BLSA) was initiated in 1958 to study the effects of aging, making it the longest-running study on human aging in America.

With a sample size of over 3200 volunteer subjects, the study has revealed crucial information about the process of human aging.

For instance, the study has shown that:

  • The most common ailments associated with the elderly such as diabetes, hypertension, and dementia are not an inevitable outcome of growing old, but rather result from genetic and lifestyle factors.
  • Aging does not proceed uniformly in humans, and all humans age differently. 

9. Nurses’ Health Study

Duration: 1976 to Present

The Nurses’ Health Study began in 1976 to study the effects of oral contraceptives on women’s health.

The first commercially available birth control pill was approved by the Food and Drug Administration (FDA) in 1960, and the use of such pills rapidly spread across the US and the UK.

At the same time, a lot of misinformation prevailed about the perceived harmful effects of using oral contraceptives.

The nurses’ health study aimed to study the long-term effects of the use of these pills by researching a sample composed of female nurses.

Nurses were specially chosen for the study because of their medical awareness and hence the ease of data collection that this enabled.

Over time, the study expanded to include not just oral contraceptives but also smoking, exercise, and obesity within the ambit of its research.

As its scope widened, so did the sample size and the resources required for continuing the research.

As a result, the study is now believed to be one of the largest and the most expensive observational health studies in history.

10. The Seattle 500 Study

Duration: 1974 to Present

The Seattle 500 Study is a longitudinal study being conducted by the University of Washington.

It observes a cohort of 500 individuals in the city of Seattle to determine the effects of prenatal habits on human health.

In particular, the study attempts to track patterns of substance abuse and mental health among the subjects and correlate them to the prenatal habits of the parents.  

From the examples above, it is clear that longitudinal studies are essential because they provide a unique perspective into certain issues which can not be acquired through any other method .

Especially in research areas that study developmental or life span issues, longitudinal studies become almost inevitable.

A major drawback of longitudinal studies is that because of their extended timespan, the results are likely to be influenced by epochal events. 

For instance, in the Genetic Studies of Genius described above, the life prospects of all the subjects would have been impacted by events such as the Great Depression and the Second World War.

The female participants in the study, despite their intellectual precocity, spent their lives as home makers because of the cultural norms of the era. Thus, despite their scale and scope, longitudinal studies do not always succeed in controlling background variables. 

Bouchard, T. J. Jr, Lykken, D. T., McGue, M., Segal, N. L., & Tellegen, A. (1990). Sources of human psychological differences: the Minnesota study of twins reared apart. Science , 250 (4978), 223–228. doi: https://doi.org/10.1126/science.2218526

Pearson, A. (2012, May) Seven Up!: A tale of two Englands that, shamefully, still exist The Telegraph https://www.telegraph.co.uk/comment/columnists/allison-pearson/9269805/Seven-Up-A-tale-of-two-Englands-that-shamefully-still-exist.html  

Shenk, J.W. (2009, June) What makes us happy? The Atlantic https://www.theatlantic.com/magazine/archive/2009/06/what-makes-us-happy/307439/  

Terman, L. M.  &  Oden, M. (1959). The Gifted group at mid-Life: Thirty-five years’ follow-up of the superior child . Genetic Studies of Genius Volume V . Stanford University Press.

Chris

  • Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 15 Body Language Signs He Likes You
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What Is A Longitudinal Study? Let’s Find Out!

what is a longitudinal study

What is a longitudinal study? This is definitely one of the questions we receive most often here at our writing company. The truth is that many students don’t have experience with these kinds of studies. They don’t know the difference between a cross sectional design and a longitudinal design. Of course, this also means that these students are not able to do such a study effectively.

So, what is a longitudinal design? In this blog post, we will tell you everything you need to know about this study. We will also give you an example of what you can achieve with it. And because we’ve mentioned cross-sectional studies above, we will compare the longitudinal study with them. It goes without saying that you will also learn about all the interesting benefits of longitudinal study. Let’s get started!

In-Depth Information About the Longitudinal Study

Let’s start with the definition of a longitudinal study. Basically, this is an observational study that observes the same set variables over a period of time. The study can take place over a short period of time such as days or weeks, or it can take place over months of years.

There have been some studies that took place over the course of decades. The Baltimore Longitudinal study of Aging (BLSA) is the world’s longest running study, having been started in 1958.

A longitudinal study uses the observational research technique to attempt to uncover relationships between specific variables that are not linked to background variables. Keep in mind that the study will focus on the same group of individuals. This is the longitudinal study definition, but there is definitely more to talk about.

Types of Longitudinal Studies

Did you know that there are 3 major types of longitudinal studies? When you are thinking about a longitudinal research design, you need to be sure what type would best suite your needs. Here are the most important types of studies explained:

Cohort study. In this study, you need to select a group of subjects based on a very specific trait. For example, you could choose individuals with the same birth date or who were born in the same city. The length of the study varies wildly. Panel study. A panel study usually involves a larger sample of people from a population. Also, this kind of study is usually conducted over prolonged periods of time. Most often, researchers use panel studies to do a quantitative analysis of the sample. Retrospective study. This study is based on existing data that has been gathered using the same methods. For example, examining patient records for people with Alzheimer’s over the last 10 years is a good example of a retrospective study.

Looking for a Longitudinal Study Example?

Now that you know what longitudinal research is and what it is useful for, it’s time to show you an example of such a study. We know, a longitudinal study example will help you a lot when it comes to writing a thesis. This is why we won’t give you one, but three longitudinal study examples:

Let’s start with a cohort study. An example of such a study is the monitoring of the over thirty male population in a specific town for lung cancer. The sample will be tested periodically over the course of the study and the aim is to find out the percentage of males over thirty who develop some form of lung cancer. Next, we’ll talk about the panel study. An easy to understand example of such a study is a customer satisfaction survey that you submit to select customers several times over a period of time. The aim of this study is to see whether customer satisfaction is on the rise or if there are some problems with the product you are selling. And finally, the retrospective study. A simple retrospective study example would be the AIDS retrospective. You can select a sample of people with AIDS and ask them about their medical history or their eating, drinking and smoking habits. The findings may lead to the discovery of the factors that contributed to them developing the disease.

Is a Cross-sectional Study the Same as Longitudinal Research?

You’ve probably heard about the term “cross-sectional study” before. Maybe it was something related to survey, or perhaps you’ve encountered the term while searching for ways to achieve your research goals for your thesis or dissertation. The question is: it his study the same as longitudinal research? The simple answer is NO. The two studies are different and should never be used interchangeably. Here are the main three differences between the two:

The cross-sectional study process requires you to do the observations at a single point in time. As we’ve told you above, a longitudinal study requires you to do repeated observations over a period of time. The cross-sectional study can tell you the state of a population at a given point in time. The longitudinal study, on the other hand, is used to highlight the changes in your sample over time. A cross-sectional study observes multiple groups of people (this is why it’s called cross-sectional) of the same population. The longitudinal study is performed on the same group of people for the entire duration of the experiment.

So, even though the parameters of the studies may be similar, you should never confuse a cross-sectional study with a longitudinal one. Your professor will penalize you severely if you fail to use the right methodology for the study you choose. Things get even worse if we’re talking about a thesis.

Longitudinal Study Strengths and Weaknesses

Now, it’s time to talk about some relevant longitudinal study strengths and weaknesses. After all, you need to learn when to use this kind of study and when to choose another type. Here are some of the strengths of such a study:

In most cases, a longitudinal study enables you to quickly identify trends. This makes it very useful in the fields of medicine, sociology and psychology. The data is collected in real time, so it’s relatively easy to find trends in the data. The longitudinal study is quite flexible. What does this mean? It means that even though the study observes a single point in time, the data can show patterns that can easily be generalized. In addition, the length of the study means that researchers have greater flexibility. The study has a high level of validity because of its length. The objectives and rules are set right from the start and the data is collected over a long period of time. In other words, the data you collect has a very high level of validity.

Of course, there are also some weaknesses when it comes to longitudinal studies. Here are just some of the weaknesses:

The longitudinal study has a relatively high degree of unpredictability. As you’ve probably already guessed, it has something to do with the original sample. Let’s say you are studying the effects of oral contraceptives on women thirty or older. Over time, many of the individuals may pass away or stop participating in your study. You need large – sometimes huge – samples if you want your study to be accurate. This presents a problem, especially if you are a student working on his thesis or dissertation. Finding a large sample can be a very time consuming – and resource consuming – task. You need plenty of time. We’ve told you that the longer the study, the better the results. Well, this means that you could spend years on your research. Unfortunately, this is not something you want to do when you want to get your PhD as soon as possible.

The Major Disadvantages of Longitudinal Studies

Now that you know why these studies may not be what you’re looking for, it’s time to show you the major disadvantages of longitudinal studies. Here are the two most important of them:

Longitudinal studies can get very expensive. If you think about it, you are probably talking about a sample consisting of at least 100 people. You will be observing this sample of the next months, if not years. As you can imagine, this can get expensive. If you are a student working on his dissertation, you may not have the necessary funding to undertake such a study. You can’t add new individuals to your sample. Why? Because the whole point of the study is to observe a specific group of people over a period of time. By introducing new people, you are introducing new variables that are distorting the accuracy of your results. Keep in mind that the longer the study lasts, the more people will stop participating in your study for various reasons.

These are the two major potential problems with a longitudinal study. While there are other cons to using this type of study, it is entirely up to you if you wish to set one up. If you plan things carefully, you’ll have an excellent opportunity to gather extremely useful data from your sample.

Getting Top Notch Research Help

OK, but how does longitudinal research fit within a thesis or dissertation? The main purpose of this study is to provide you with the data you need to analyze to support your thesis statement. Such a study works great for a dissertation because you can come up with something original (and you’ll have the data to find potentially life-changing results). However, any university professor will tell you that a longitudinal study is not easy to do.

This is why we are offering students and postgrads the research help they need. Our reliable experts are all PhD degree holders, so you can rest assured that you will be working closely with somebody who knows what he’s talking about. Also, keep in mind that many of our professional writers have been writing informative academic content for over 10 years. We work fast because of our knowledge, experience and expertise. Our experts can do the longitudinal study psychology students need for a very affordable price.

So, can you do my thesis? Yes, we can provide you with the assistance you need to complete the study and analyze the data. We’ll help you get your degree, no matter what it takes. And believe us, we haven’t had a supervisor who didn’t appreciate our fantastic work.

Of course, we can help you with much more than just doing the research. Our writers can write your thesis or dissertation from start to finish. In addition, our experienced editors and proofreaders will make sure everything is perfect. We can assure you that you will not have to proofread or edit our paper. It will be ready for submission from the moment you receive it. What are you waiting for? Get in touch with us for quick help and ask us about our current discounts!

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Frequently asked questions

What are the pros and cons of a longitudinal study.

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

Frequently asked questions: Methodology

Quantitative observations involve measuring or counting something and expressing the result in numerical form, while qualitative observations involve describing something in non-numerical terms, such as its appearance, texture, or color.

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Scope of research is determined at the beginning of your research process , prior to the data collection stage. Sometimes called “scope of study,” your scope delineates what will and will not be covered in your project. It helps you focus your work and your time, ensuring that you’ll be able to achieve your goals and outcomes.

Defining a scope can be very useful in any research project, from a research proposal to a thesis or dissertation . A scope is needed for all types of research: quantitative , qualitative , and mixed methods .

To define your scope of research, consider the following:

  • Budget constraints or any specifics of grant funding
  • Your proposed timeline and duration
  • Specifics about your population of study, your proposed sample size , and the research methodology you’ll pursue
  • Any inclusion and exclusion criteria
  • Any anticipated control , extraneous , or confounding variables that could bias your research if not accounted for properly.

Inclusion and exclusion criteria are predominantly used in non-probability sampling . In purposive sampling and snowball sampling , restrictions apply as to who can be included in the sample .

Inclusion and exclusion criteria are typically presented and discussed in the methodology section of your thesis or dissertation .

The purpose of theory-testing mode is to find evidence in order to disprove, refine, or support a theory. As such, generalisability is not the aim of theory-testing mode.

Due to this, the priority of researchers in theory-testing mode is to eliminate alternative causes for relationships between variables . In other words, they prioritise internal validity over external validity , including ecological validity .

Convergent validity shows how much a measure of one construct aligns with other measures of the same or related constructs .

On the other hand, concurrent validity is about how a measure matches up to some known criterion or gold standard, which can be another measure.

Although both types of validity are established by calculating the association or correlation between a test score and another variable , they represent distinct validation methods.

Validity tells you how accurately a method measures what it was designed to measure. There are 4 main types of validity :

  • Construct validity : Does the test measure the construct it was designed to measure?
  • Face validity : Does the test appear to be suitable for its objectives ?
  • Content validity : Does the test cover all relevant parts of the construct it aims to measure.
  • Criterion validity : Do the results accurately measure the concrete outcome they are designed to measure?

Criterion validity evaluates how well a test measures the outcome it was designed to measure. An outcome can be, for example, the onset of a disease.

Criterion validity consists of two subtypes depending on the time at which the two measures (the criterion and your test) are obtained:

  • Concurrent validity is a validation strategy where the the scores of a test and the criterion are obtained at the same time
  • Predictive validity is a validation strategy where the criterion variables are measured after the scores of the test

Attrition refers to participants leaving a study. It always happens to some extent – for example, in randomised control trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

  • Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
  • Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analysing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Construct validity refers to how well a test measures the concept (or construct) it was designed to measure. Assessing construct validity is especially important when you’re researching concepts that can’t be quantified and/or are intangible, like introversion. To ensure construct validity your test should be based on known indicators of introversion ( operationalisation ).

On the other hand, content validity assesses how well the test represents all aspects of the construct. If some aspects are missing or irrelevant parts are included, the test has low content validity.

  • Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Construct validity has convergent and discriminant subtypes. They assist determine if a test measures the intended notion.

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

  • A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
  • A successful replication shows that the reliability of the results is high.
  • Reproducing research entails reanalysing the existing data in the same manner.
  • Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalisations – often the goal of quantitative research . As such, a snowball sample is not representative of the target population, and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones. 

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

  • If there is no sampling frame available (e.g., people with a rare disease)
  • If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
  • If the research focuses on a sensitive topic (e.g., extra-marital affairs)

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection , using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

When your population is large in size, geographically dispersed, or difficult to contact, it’s necessary to use a sampling method .

This allows you to gather information from a smaller part of the population, i.e. the sample, and make accurate statements by using statistical analysis. A few sampling methods include simple random sampling , convenience sampling , and snowball sampling .

The two main types of social desirability bias are:

  • Self-deceptive enhancement (self-deception): The tendency to see oneself in a favorable light without realizing it.
  • Impression managemen t (other-deception): The tendency to inflate one’s abilities or achievement in order to make a good impression on other people.

Response bias refers to conditions or factors that take place during the process of responding to surveys, affecting the responses. One type of response bias is social desirability bias .

Demand characteristics are aspects of experiments that may give away the research objective to participants. Social desirability bias occurs when participants automatically try to respond in ways that make them seem likeable in a study, even if it means misrepresenting how they truly feel.

Participants may use demand characteristics to infer social norms or experimenter expectancies and act in socially desirable ways, so you should try to control for demand characteristics wherever possible.

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information – for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Peer review is a process of evaluating submissions to an academic journal. Utilising rigorous criteria, a panel of reviewers in the same subject area decide whether to accept each submission for publication.

For this reason, academic journals are often considered among the most credible sources you can use in a research project – provided that the journal itself is trustworthy and well regarded.

In general, the peer review process follows the following steps:

  • First, the author submits the manuscript to the editor.
  • Reject the manuscript and send it back to author, or
  • Send it onward to the selected peer reviewer(s)
  • Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made.
  • Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field.

It acts as a first defence, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure.

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

  • In a single-blind study , only the participants are blinded.
  • In a double-blind study , both participants and experimenters are blinded.
  • In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analysing the data.

Blinding is important to reduce bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behaviour in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a die to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalisability of your results, while random assignment improves the internal validity of your study.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardisation and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyse, detect, modify, or remove ‘dirty’ data to make your dataset ‘clean’. Data cleaning is also called data cleansing or data scrubbing.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimise or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Observer bias occurs when a researcher’s expectations, opinions, or prejudices influence what they perceive or record in a study. It usually affects studies when observers are aware of the research aims or hypotheses. This type of research bias is also called detection bias or ascertainment bias .

The observer-expectancy effect occurs when researchers influence the results of their own study through interactions with participants.

Researchers’ own beliefs and expectations about the study results may unintentionally influence participants through demand characteristics .

You can use several tactics to minimise observer bias .

  • Use masking (blinding) to hide the purpose of your study from all observers.
  • Triangulate your data with different data collection methods or sources.
  • Use multiple observers and ensure inter-rater reliability.
  • Train your observers to make sure data is consistently recorded between them.
  • Standardise your observation procedures to make sure they are structured and clear.

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviours of your research subjects in real-world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as ‘people watching’ with a purpose.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

You can organise the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomisation can minimise the bias from order effects.

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or by post. All questions are standardised so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

  • A control group that receives a standard treatment, a fake treatment, or no treatment
  • Random assignment of participants to ensure the groups are equivalent

Depending on your study topic, there are various other methods of controlling variables .

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

A true experiment (aka a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analysing data from people using questionnaires.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviours. It is made up of four or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with five or seven possible responses, to capture their degree of agreement.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyse your data.

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data are available for analysis; other times your research question may only require a cross-sectional study to answer it.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyse behaviour over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

Longitudinal study Cross-sectional study
observations Observations at a in time
Observes the multiple times Observes (a ‘cross-section’) in the population
Follows in participants over time Provides of society at a given point

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

A correlation reflects the strength and/or direction of the association between two or more variables.

  • A positive correlation means that both variables change in the same direction.
  • A negative correlation means that the variables change in opposite directions.
  • A zero correlation means there’s no relationship between the variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

  • In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
  • In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups . Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

  • Open-ended and flexible
  • Impossible to answer with ‘yes’ or ‘no’ (questions that start with ‘why’ or ‘how’ are often best)
  • Unambiguous, getting straight to the point while still stimulating discussion
  • Unbiased and neutral

Social desirability bias is the tendency for interview participants to give responses that will be viewed favourably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias in research can also occur in observations if the participants know they’re being observed. They might alter their behaviour accordingly.

A focus group is a research method that brings together a small group of people to answer questions in a moderated setting. The group is chosen due to predefined demographic traits, and the questions are designed to shed light on a topic of interest. It is one of four types of interviews .

The four most common types of interviews are:

  • Structured interviews : The questions are predetermined in both topic and order.
  • Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
  • Unstructured interviews : None of the questions are predetermined.
  • Focus group interviews : The questions are presented to a group instead of one individual.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

  • You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions
  • Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
  • You are seeking descriptive data, and are ready to ask questions that will deepen and contextualise your initial thoughts and hypotheses
  • Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

  • You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
  • Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when:

  • You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
  • You are constrained in terms of time or resources and need to analyse your data quickly and efficiently
  • Your research question depends on strong parity between participants, with environmental conditions held constant

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

When conducting research, collecting original data has significant advantages:

  • You can tailor data collection to your specific research aims (e.g., understanding the needs of your consumers or user testing your website).
  • You can control and standardise the process for high reliability and validity (e.g., choosing appropriate measurements and sampling methods ).

However, there are also some drawbacks: data collection can be time-consuming, labour-intensive, and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

If something is a mediating variable :

  • It’s caused by the independent variable
  • It influences the dependent variable
  • When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

  • The  independent variable  is the amount of nutrients added to the crop field.
  • The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Discrete and continuous variables are two types of quantitative variables :

  • Discrete variables represent counts (e.g., the number of objects in a collection).
  • Continuous variables represent measurable amounts (e.g., water volume or weight).

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

You want to find out how blood sugar levels are affected by drinking diet cola and regular cola, so you conduct an experiment .

  • The type of cola – diet or regular – is the independent variable .
  • The level of blood sugar that you measure is the dependent variable – it changes depending on the type of cola.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control, and randomisation.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomisation , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalisation .

In statistics, ordinal and nominal variables are both considered categorical variables .

Even though ordinal data can sometimes be numerical, not all mathematical operations can be performed on them.

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

‘Controlling for a variable’ means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

There are 4 main types of extraneous variables :

  • Demand characteristics : Environmental cues that encourage participants to conform to researchers’ expectations
  • Experimenter effects : Unintentional actions by researchers that influence study outcomes
  • Situational variables : Eenvironmental variables that alter participants’ behaviours
  • Participant variables : Any characteristic or aspect of a participant’s background that could affect study results

The difference between explanatory and response variables is simple:

  • An explanatory variable is the expected cause, and it explains the results.
  • A response variable is the expected effect, and it responds to other variables.

The term ‘ explanatory variable ‘ is sometimes preferred over ‘ independent variable ‘ because, in real-world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so ‘explanatory variables’ is a more appropriate term.

On graphs, the explanatory variable is conventionally placed on the x -axis, while the response variable is placed on the y -axis.

  • If you have quantitative variables , use a scatterplot or a line graph.
  • If your response variable is categorical, use a scatterplot or a line graph.
  • If your explanatory variable is categorical, use a bar graph.

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called ‘independent’ because it’s not influenced by any other variables in the study.

Independent variables are also called:

  • Explanatory variables (they explain an event or outcome)
  • Predictor variables (they can be used to predict the value of a dependent variable)
  • Right-hand-side variables (they appear on the right-hand side of a regression equation)

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it ‘depends’ on your independent variable.

In statistics, dependent variables are also called:

  • Response variables (they respond to a change in another variable)
  • Outcome variables (they represent the outcome you want to measure)
  • Left-hand-side variables (they appear on the left-hand side of a regression equation)

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

  • Inductive generalisation : You use observations about a sample to come to a conclusion about the population it came from.
  • Statistical generalisation: You use specific numbers about samples to make statements about populations.
  • Causal reasoning: You make cause-and-effect links between different things.
  • Sign reasoning: You make a conclusion about a correlational relationship between different things.
  • Analogical reasoning: You make a conclusion about something based on its similarities to something else.

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity, and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

  • Convergent validity : The extent to which your measure corresponds to measures of related constructs
  • Discriminant validity: The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Attrition bias can skew your sample so that your final sample differs significantly from your original sample. Your sample is biased because some groups from your population are underrepresented.

With a biased final sample, you may not be able to generalise your findings to the original population that you sampled from, so your external validity is compromised.

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment, and situation effect.

The two types of external validity are population validity (whether you can generalise to other groups of people) and ecological validity (whether you can generalise to other situations and settings).

The external validity of a study is the extent to which you can generalise your findings to different groups of people, situations, and measures.

Attrition bias is a threat to internal validity . In experiments, differential rates of attrition between treatment and control groups can skew results.

This bias can affect the relationship between your independent and dependent variables . It can make variables appear to be correlated when they are not, or vice versa.

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction, and attrition .

A sampling error is the difference between a population parameter and a sample statistic .

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

There are three key steps in systematic sampling :

  • Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
  • Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
  • Choose every k th member of the population as your sample.

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 × 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method .

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

In multistage sampling , you can use probability or non-probability sampling methods.

For a probability sample, you have to probability sampling at every stage. You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

  • In single-stage sampling , you collect data from every unit within the selected clusters.
  • In double-stage sampling , you select a random sample of units from within the clusters.
  • In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data are then collected from as large a percentage as possible of this random subset.

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from county to city to neighbourhood) to create a sample that’s less expensive and time-consuming to collect data from.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling , and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

Advantages:

  • Prevents carryover effects of learning and fatigue.
  • Shorter study duration.

Disadvantages:

  • Needs larger samples for high power.
  • Uses more resources to recruit participants, administer sessions, cover costs, etc.
  • Individual differences may be an alternative explanation for results.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

  • Only requires small samples
  • Statistically powerful
  • Removes the effects of individual differences on the outcomes
  • Internal validity threats reduce the likelihood of establishing a direct relationship between variables
  • Time-related effects, such as growth, can influence the outcomes
  • Carryover effects mean that the specific order of different treatments affect the outcomes

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomisation. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference between this and a true experiment is that the groups are not randomly assigned.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word ‘between’ means that you’re comparing different conditions between groups, while the word ‘within’ means you’re comparing different conditions within the same group.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Triangulation can help:

  • Reduce bias that comes from using a single method, theory, or investigator
  • Enhance validity by approaching the same topic with different tools
  • Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

  • It’s time-consuming and labour-intensive, often involving an interdisciplinary team.
  • Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

  • Data triangulation : Using data from different times, spaces, and people
  • Investigator triangulation : Involving multiple researchers in collecting or analysing data
  • Theory triangulation : Using varying theoretical perspectives in your research
  • Methodological triangulation : Using different methodologies to approach the same topic

Experimental designs are a set of procedures that you plan in order to examine the relationship between variables that interest you.

To design a successful experiment, first identify:

  • A testable hypothesis
  • One or more independent variables that you will manipulate
  • One or more dependent variables that you will measure

When designing the experiment, first decide:

  • How your variable(s) will be manipulated
  • How you will control for any potential confounding or lurking variables
  • How many subjects you will include
  • How you will assign treatments to your subjects

Exploratory research explores the main aspects of a new or barely researched question.

Explanatory research explains the causes and effects of an already widely researched question.

The key difference between observational studies and experiments is that, done correctly, an observational study will never influence the responses or behaviours of participants. Experimental designs will have a treatment condition applied to at least a portion of participants.

An observational study could be a good fit for your research if your research question is based on things you observe. If you have ethical, logistical, or practical concerns that make an experimental design challenging, consider an observational study. Remember that in an observational study, it is critical that there be no interference or manipulation of the research subjects. Since it’s not an experiment, there are no control or treatment groups either.

These are four of the most common mixed methods designs :

  • Convergent parallel: Quantitative and qualitative data are collected at the same time and analysed separately. After both analyses are complete, compare your results to draw overall conclusions. 
  • Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
  • Explanatory sequential: Quantitative data is collected and analysed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualise your quantitative findings.
  • Exploratory sequential: Qualitative data is collected and analysed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

There are five common approaches to qualitative research :

  • Grounded theory involves collecting data in order to develop new theories.
  • Ethnography involves immersing yourself in a group or organisation to understand its culture.
  • Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
  • Phenomenological research involves investigating phenomena through people’s lived experiences.
  • Action research links theory and practice in several cycles to drive innovative changes.

There are various approaches to qualitative data analysis , but they all share five steps in common:

  • Prepare and organise your data.
  • Review and explore your data.
  • Develop a data coding system.
  • Assign codes to the data.
  • Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

The research methods you use depend on the type of data you need to answer your research question .

  • If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
  • If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
  • If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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ORIGINAL RESEARCH article

Intuitive eating was associated with anxiety, depression, pregnancy weight and blood glucose in women with gestational diabetes mellitus: a prospective longitudinal study.

Xiao Xia Gao

  • 1 Fujian Maternity and Child Health Hospital, Fuzhou, Fujian Province, China
  • 2 School of Nursing, Fujian Medical University, Fujian Maternity and Child Health Hospital, Fuzhou, Fujian Province, China
  • 3 Fujian Obstetrics and Gynecology Hospital Affiliated to Fujian Medical University, Fuzhou, China, Fuzhou, Fujian Province, China
  • 4 Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University, Fuzhou, China
  • 5 Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, Guangdong Province, China
  • 6 Mindong Hospital, Fujian Medical University, Ningde, Fujian, China
  • 7 School of Nursing, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian Province, China

The final, formatted version of the article will be published soon.

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Background: Outside of pregnancy, intuitive eating (IE) is associated with lower body weight, blood glucose, and higher positive mood. However, little was known about the relationship between IE and anxiety-depression in the GDM population. Thus, this study aimed to investigate the association of IE with anxiety and depression, pregnancy weight and pregnancy blood glucose in the first and second GDM visit. Methods: Data from 310 pregnant women with GDM from the Fujian Maternal and Child Health Hospital Trial (Approval Number: 2020Y9133) were analysed. IE was assessed using the Intuitive Eating Scale-2 subscales of Eating for Physiological Reasons rather than Emotional Reasons (EPR), Relying on Hunger and Satiety Cues (RHSC) and Body-Food Choice Consistency (B-FCC). Observations included weight, body mass index (BMI), fasting plasma glucose (FPG) and 2-hour postprandial blood glucose; the Hospital Anxiety and Depression Scale (HADS) was used to assess the level of anxiety and depression in pregnant women with GDM. Linear regression analysis was used to assess the correlation between IE and anxiety, depression, pregnancy blood glucose and weight. Results: The cross-sectional analysis showed that the EPR eating behavior was negatively correlated with anxiety and depression, and the B-FCC eating behavior was negatively correlated with depression at both the first and second GDM visit; in addition, the B-FCC eating behavior was associated with lower BMI in the third trimester (all P < 0.05). In longitudinal analyses, the EPR eating behavior in the first visit for GDM predicted lower levels of anxiety and depression in the second GDM visit, whereas the RHSC eating behavior in the first visit for GDM was associated with lower FPG in the second GDM visit (all P < 0.01). Conclusions: These results suggest that practicing intuitive eating may be beneficial and that higher intuitive eating adherence can lead to lower levels of anxiety and depression and more ideal gestational weight and blood glucose values.

Keywords: Intuitive eating, Anxiety, Depression, GDM, weight, Blood Glucose

Received: 29 Mar 2024; Accepted: 10 Jul 2024.

Copyright: © 2024 Gao, Zheng, Chen, Jiang, Liao, Pan, Zou and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Xiu-Min Jiang, Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University, Fuzhou, China

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Xjtlu holds its first three minute thesis competition

Author catherine diamond.

Edit Tamara Kaup and Patricia Pieterse

12 Jul 2024

thesis on longitudinal studies

The finalists of XJTLU's first Three Minute Thesis competition, with the judges and Professor Adam Cross Credit: XJTLU

Could you describe 90,000 words of research in just three minutes? That’s just what nine PhD students did as XJTLU recently wrapped up its first Three Minute Thesis competition.

The annual worldwide contest, started by The University of Queensland in 2008, celebrates doctoral research by asking PhD students to explain their research within three minutes and with only one static presentation slide.

After preliminary selection, nine students from across the University’s schools and academies were chosen to present in XJTLU’s final.

They finalists were:

Entrepreneurship and Enterprise Hub Business
International Business School Suzhou (IBSS) Business
School of Advanced Technology Electronic and Electrical Engineering
School of Advanced Technology Electronic and Electrical Engineering
Academy of Film and Creative Technology English Culture and Communication
School of Science Environmental Science
School of Science Environmental Science
School of Science Chemistry
School of Science Public Health

Dr Sophie Sturup, Senior Associate Professor in the Design School’s Department of Urban Planning and Design, was one of the five judges. She explains why academics being able to speak effectively to anyone about their research in three minutes is useful.

“It gives you a way of letting people in on the secret you have been working on all this time,” she says.

The judges named Peiyun Li the winner and Yuhua Duan the runner-up. Both students are in the Department of Health and Environmental Sciences in the School of Science. Professor Adam Cross, Associate Vice-President for Education, awarded the trophies.

Li explains why she joined the competition: “At first, I signed up just to practice my presentation skills. However, when I carefully read the competition requirements, I realised it would be an important opportunity for my future career.”

thesis on longitudinal studies

Peiyun Li, winner of XJTLU’s Three Minute Thesis final, is a PhD student in the Department of Health and Environmental Sciences. Credit: Peiyun Li

Reaching a wider audience

Li says that researchers in her field need to be able to clearly communicate their findings and suggestions to different audiences, including the public and policymakers, to advocate for the importance of conservation.

“Unlike other academic presentations, this competition required me to use familiar terms so everyone could grasp the gist of my research. It was excellent training and a challenge for me,” she says.

Li and Duan both encountered difficulties while creating their presentations, with Li highlighting the complexity of balancing the introduction, results, and discussion in only three minutes.

Duan agrees, saying: “Three minutes is a short time to present a complete research thesis. It is necessary not only to set up the experimental background so that the audience can understand the significance of the subject, but also to present our experimental design and results.”

thesis on longitudinal studies

Runner-up in XJTLU’s Three Minute Thesis final: Yuhua Duan, a PhD student in the Department of Health and Environmental Sciences. Credit: Yuhua Duan

Dr Sturup explains both Li’s and Duan’s abilities to distill all aspects of their research into three-minute presentations led to their being awarded the top prizes.

She says: “Both students talked about their research specifically. Their whole talk was about what they were doing and why.

“They both made a strong connection between what they were doing and how it was going to solve a problem or help some problem they felt passionately about in some way.”

Li and Duan found the experience valuable and recommended other PhD students take part next year.

Duan says: “I think it’s a good way for PhD students to practise presenting in English. And in the process of preparing for the competition, everyone needs to think about how to quickly capture the focus and significance of our scientific research.”

Li will go on to represent XJTLU at the 2024 Asia-Pacific Three Minute Thesis Competition in October.

The judges of the competition were:

Dr Sophie Sturup , Senior Associate Professor in the Department of Urban Planning and Design, Design School;

Dr Ye Bai , Senior Associate Professor in the Department of Economics, IBSS;

Dr Sekar Raju , Senior Associate Professor in the Department of Biological Sciences, School of Science;

Dr Hua Li, Senior Language Lecturer in the English Language Centre, School of Languages; and

Ran Zang , a PhD student in Civil Engineering, Design School and one of the awardees of Best Presentation in the 2023 XJTLU Postgraduate Research Symposium.

thesis on longitudinal studies

Professor Zhoulin Ruan, Vice President of Academic Affairs, gives an opening speech at XJTLU’s Three Minute Thesis Competition . Credit: XJTLU

By Catherine Diamond

Edited by Tamara Kaup and Patricia Pieterse

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COMMENTS

  1. Longitudinal Study

    Revised on June 22, 2023. In a longitudinal study, researchers repeatedly examine the same individuals to detect any changes that might occur over a period of time. Longitudinal studies are a type of correlational research in which researchers observe and collect data on a number of variables without trying to influence those variables.

  2. A Longitudinal Analysis of Academic Achievement and Its Correlates in

    This study utilized data from 2,361 university students at the bachelor level who were part of an ongoing longitudinal study. Of the participants, 1,251 were male (52.9%) and 1,110 (47.1%) were female. The study was approved by the Institutional Review Board (IRB protocol number 2015/01-1).

  3. A Three-Paper Dissertation on Longitudinal Data Analysis in Education

    In longitudinal settings, modeling the covariance structure of repeated measure data is essential for proper analysis. The first paper in this three-paper dissertation presents a survey of four journals in the fields of Education and Psychology to identify the most commonly used methods for analyzing longitudinal data. It provides literature reviews and statistical details for each identified ...

  4. Longitudinal Research: A Panel Discussion on Conceptual Issues

    An important meta-trend in work, aging, and retirement research is the heightened appreciation of the temporal nature of the phenomena under investigation and the important role that longitudinal study designs play in understanding them (e.g., Heybroek, Haynes, & Baxter, 2015; Madero-Cabib, Gauthier, & Le Goff, 2016; Wang, 2007; Warren, 2015; Weikamp & Göritz, 2015).

  5. Development of narcissism across the life span: A meta-analytic review

    This meta-analytic review investigated the development of narcissism across the life span, by synthesizing the available longitudinal data on mean-level change and rank-order stability. Three factors of narcissism were examined: agentic, antagonistic, and neurotic narcissism. Analyses were based on data from 51 samples, including 37,247 participants. As effect size measures, we used the ...

  6. PDF Longitudinal data analysis using Generalized Linear Model with missing

    Longitudinal studies rely on data collected at several occasions from a set of selected individuals. The purpose of these studies is to use a regression-type model to express a response variable as a function of explanatory variables, or covariates. In this thesis, we use marginal models for the analysis of such data, which, coupled with the method

  7. PDF Statistical Analysis and Modeling Health Data: A Longitudinal Study

    Tharu, Bhikhari Prasad, "Statistical Analysis and Modeling Health Data: A Longitudinal Study" (2016). Graduate Theses and Dissertations. htp://scholarcommons.usf.edu/etd/6413. This Thesis is brought to you for free and open access by the Graduate School at Scholar Commons.

  8. What Is A Longitudinal Study? A Simple Definition

    Longitudinal study: a study which assesses how a group of 13-year old children's attitudes and perspectives towards income inequality evolve over a period of 5 years, ... Choosing whether to use a longitudinal or cross-sectional study for your dissertation, thesis or research project requires a few considerations.

  9. Longitudinal Study

    Longitudinal studies also allow repeated observations of the same individual over time. This means any changes in the outcome variable cannot be attributed to differences between individuals. Example: Individual differences. You decide to study how a particular weight-training program affects athletic performance.

  10. PDF Development of Narcissism Across the Life Span: A Meta-Analytic Review

    Studies were included in the meta-analysis if the following criteria were fulfilled: (a) the report was available in English or German language; (b) the study was empirical-quantitative; (c) the study used a longitudinal study design (i.e., it included two or more assessments of the same sample); (d) data were available for at least

  11. (PDF) The Early Childhood Longitudinal Study: Quantitative Data

    The Early Childhood Longitudinal Study (ECLS) program includes three longitudinal studies. that examine child development, school readiness, and early school experiences. The birth. cohort of the ...

  12. (PDF) Longitudinal studies

    Longitudinal cohort studies, particularly when conducted. prospectively in their pure form, offer numerous bene ts. These include: ( I) The ability to identify and relate events to. particular ...

  13. PDF A Longitudinal Study Assessing the Microsoft Office Skills Course

    Abstract. This paper explains a four-year longitudinal study of the assessment process for a Microsoft Office skills course. It examines whether there is an increase in students' knowledge based on responses to pre- and post-surveys that asked students to evaluate how well they can do particular tasks.

  14. A review of "longitudinal study" in developmental psychology

    The purpose of this library research thesis is to review the "longitudinal study" in terms of problems and present use. A preliminary search of the literature on longitudinal method revealed problems centering around two areas: (1) definition of "longitudinal study" and (2) practical problems of method itself. The purpose of this thesis then is to explore through a search of books and journals ...

  15. PubMed

    PubMed

  16. What is a Longitudinal Study? Definition, Types, and Examples

    A longitudinal study is a type of research where the scope of the study is observed and studied in the same set of people over a long period of time. This could be from a few weeks to many years. They are most often found in different fields like health, economics, and medicine. They serve to give knowledge of the passage of events without ...

  17. Cause and effect: Optimizing the designs of longitudinal studies in

    Despite the increase in longitudinal studies, to date the two-wave longitudinal design has continued to dominate the research scene, with the possible exception of the diary study (Bolger, Davis, & Rafaeli, 2003). Diary studies tend to focus on relatively volatile processes in which the phenomena of interest (e.g. mood or fatigue) change ...

  18. PDF Statistical Analysis of Longitudinal Data with a Case Study

    y introduce longitudinal data and then describe the GTHPI data that will be analyzed in this thesis. The standard statistical analysis models that can be used to study the longitudinal GTHPI data are explained in Chapter 3. The challenge of the study based on this data set is that there is a large proportion

  19. What motivates patients to remain in longitudinal observational studies?

    My thesis would not have been possible without her guidance and encouragement. ... Longitudinal studies are designed to examine associations between exposure to known or suspected causes of disease and subsequent morbidity or mortality (Coggon, Rose, & Barker, 2003). Since the first requirement of most longitudinal studies is the identification of

  20. A modest start, but a steady rise in research use: a longitudinal study

    Design and participants. Data from a prospective cohort of 1,501 Swedish newly graduated nurses within the national LANE study (Longitudinal Analyses of Nursing Education and Entry in Worklife) were used to investigate the primary outcome for this report-the extent and course of perceived use of research findings-over the first five years of practice as a nurse.

  21. What is an example of a longitudinal study?

    In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time. Longitudinal study Cross-sectional study; ... In a longer or more complex research project, such as a thesis or dissertation, ...

  22. The enhancive effect of the 2014-2016 El Niño-induced drought on the

    A longitudinal study was conducted from 2012 to 2016 with 299 participants. Albendazole was administered in the dry season to STH-positive participants only in 2012. Two follow-ups were done in 2013 and 2016. El Niño 2014-2016 induced a 14-month-long drought from 2014 to 2015. Prevalence of STHs in 2008 (n = 296), 2019 (n = 304), and 2023 (n ...

  23. Dissertations & Thesis

    Stone, V. (1980) Structural modeling of the relations among environment variables, health status, and intelligence in adulthood. Unpubl. doctoral dissertation, University of Southern California. Gruber-Baldini, A. (1991) The impact of health and disease on cognitive ability in adulthood and old age in the Seattle Longitudinal Study.

  24. Understanding the effects of universal test and treat on longitudinal

    Understanding the effects of universal test and treat on longitudinal HIV care outcomes among South African youth: a ... we conducted a retrospective cohort study among youth aged 18-24 diagnosed with HIV from August 2015-December 2018 in nine health care facilities in South Africa. ... Deposit your senior honors thesis. Scholarly Journal ...

  25. 10 Famous Examples of Longitudinal Studies

    As a result, the study is now believed to be one of the largest and the most expensive observational health studies in history. 10. The Seattle 500 Study. Duration: 1974 to Present. The Seattle 500 Study is a longitudinal study being conducted by the University of Washington.

  26. What Is A Longitudinal Study? Learn From Our Experts!

    As we've told you above, a longitudinal study requires you to do repeated observations over a period of time. The cross-sectional study can tell you the state of a population at a given point in time. The longitudinal study, on the other hand, is used to highlight the changes in your sample over time.

  27. What are the pros and cons of a longitudinal study?

    Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.. When a test has strong face validity, anyone would agree that the test's questions appear to measure what they are intended to measure.. For example, looking at a 4th grade math test ...

  28. Frontiers

    In longitudinal analyses, the EPR eating behavior in the first visit for GDM predicted lower levels of anxiety and depression in the second GDM visit, whereas the RHSC eating behavior in the first visit for GDM was associated with lower FPG in the second GDM visit (all P 0.01). Conclusions: These results suggest that practicing intuitive eating ...

  29. Longitudinal vs. Cross-Sectional Studies in Sociology

    Introduce the essay topic - comparing longitudinal and cross-sectional studies. Briefly define both methods and their key features. State thesis statement: Both longitudinal and cross-sectional studies offer valuable insights into social phenomena, but differ in their strengths and weaknesses, making them suitable for different research questions.

  30. Xjtlu holds its first three minute thesis competition

    Runner-up in XJTLU's Three Minute Thesis final: Yuhua Duan, a PhD student in the Department of Health and Environmental Sciences. Credit: Yuhua Duan. Dr Sturup explains both Li's and Duan's abilities to distill all aspects of their research into three-minute presentations led to their being awarded the top prizes.