type of longitudinal research

Longitudinal Study Design

Julia Simkus

Editor at Simply Psychology

BA (Hons) Psychology, Princeton University

Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master's Degree in Counseling for Mental Health and Wellness in September 2023. Julia's research has been published in peer reviewed journals.

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Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

A longitudinal study is a type of observational and correlational study that involves monitoring a population over an extended period of time. It allows researchers to track changes and developments in the subjects over time.

What is a Longitudinal Study?

In longitudinal studies, researchers do not manipulate any variables or interfere with the environment. Instead, they simply conduct observations on the same group of subjects over a period of time.

These research studies can last as short as a week or as long as multiple years or even decades. Unlike cross-sectional studies that measure a moment in time, longitudinal studies last beyond a single moment, enabling researchers to discover cause-and-effect relationships between variables.

They are beneficial for recognizing any changes, developments, or patterns in the characteristics of a target population. Longitudinal studies are often used in clinical and developmental psychology to study shifts in behaviors, thoughts, emotions, and trends throughout a lifetime.

For example, a longitudinal study could be used to examine the progress and well-being of children at critical age periods from birth to adulthood.

The Harvard Study of Adult Development is one of the longest longitudinal studies to date. Researchers in this study have followed the same men group for over 80 years, observing psychosocial variables and biological processes for healthy aging and well-being in late life (see Harvard Second Generation Study).

When designing longitudinal studies, researchers must consider issues like sample selection and generalizability, attrition and selectivity bias, effects of repeated exposure to measures, selection of appropriate statistical models, and coverage of the necessary timespan to capture the phenomena of interest.

Panel Study

• A panel study is a type of longitudinal study design in which the same set of participants are measured repeatedly over time.
• Data is gathered on the same variables of interest at each time point using consistent methods. This allows studying continuity and changes within individuals over time on the key measured constructs.
• Prominent examples include national panel surveys on topics like health, aging, employment, and economics. Panel studies are a type of prospective study .

Cohort Study

• A cohort study is a type of longitudinal study that samples a group of people sharing a common experience or demographic trait within a defined period, such as year of birth.
• Researchers observe a population based on the shared experience of a specific event, such as birth, geographic location, or historical experience. These studies are typically used among medical researchers.
• Cohorts are identified and selected at a starting point (e.g. birth, starting school, entering a job field) and followed forward in time. 
• As they age, data is collected on cohort subgroups to determine their differing trajectories. For example, investigating how health outcomes diverge for groups born in 1950s, 1960s, and 1970s.
• Cohort studies do not require the same individuals to be assessed over time; they just require representation from the cohort.

Retrospective Study

• In a retrospective study , researchers either collect data on events that have already occurred or use existing data that already exists in databases, medical records, or interviews to gain insights about a population.
• Appropriate when prospectively following participants from the past starting point is infeasible or unethical. For example, studying early origins of diseases emerging later in life.
• Retrospective studies efficiently provide a “snapshot summary” of the past in relation to present status. However, quality concerns with retrospective data make careful interpretation necessary when inferring causality. Memory biases and selective retention influence quality of retrospective data.

Allows researchers to look at changes over time

Because longitudinal studies observe variables over extended periods of time, researchers can use their data to study developmental shifts and understand how certain things change as we age.

High validation

Since objectives and rules for long-term studies are established before data collection, these studies are authentic and have high levels of validity.

Eliminates recall bias

Recall bias occurs when participants do not remember past events accurately or omit details from previous experiences.

Flexibility

The variables in longitudinal studies can change throughout the study. Even if the study was created to study a specific pattern or characteristic, the data collection could show new data points or relationships that are unique and worth investigating further.

Limitations

Costly and time-consuming.

Longitudinal studies can take months or years to complete, rendering them expensive and time-consuming. Because of this, researchers tend to have difficulty recruiting participants, leading to smaller sample sizes.

Large sample size needed

Longitudinal studies tend to be challenging to conduct because large samples are needed for any relationships or patterns to be meaningful. Researchers are unable to generate results if there is not enough data.

Participants tend to drop out

Not only is it a struggle to recruit participants, but subjects also tend to leave or drop out of the study due to various reasons such as illness, relocation, or a lack of motivation to complete the full study.

This tendency is known as selective attrition and can threaten the validity of an experiment. For this reason, researchers using this approach typically recruit many participants, expecting a substantial number to drop out before the end.

Report bias is possible

Longitudinal studies will sometimes rely on surveys and questionnaires, which could result in inaccurate reporting as there is no way to verify the information presented.

• Data were collected for each child at three-time points: at 11 months after adoption, at 4.5 years of age and at 10.5 years of age. The first two sets of results showed that the adoptees were behind the non-institutionalised group however by 10.5 years old there was no difference between the two groups. The Romanian orphans had caught up with the children raised in normal Canadian families.
• The role of positive psychology constructs in predicting mental health and academic achievement in children and adolescents (Marques Pais-Ribeiro, & Lopez, 2011)
• The correlation between dieting behavior and the development of bulimia nervosa (Stice et al., 1998)
• The stress of educational bottlenecks negatively impacting students’ wellbeing (Cruwys, Greenaway, & Haslam, 2015)
• The effects of job insecurity on psychological health and withdrawal (Sidney & Schaufeli, 1995)
• The relationship between loneliness, health, and mortality in adults aged 50 years and over (Luo et al., 2012)
• The influence of parental attachment and parental control on early onset of alcohol consumption in adolescence (Van der Vorst et al., 2006)
• The relationship between religion and health outcomes in medical rehabilitation patients (Fitchett et al., 1999)

Goals of Longitudinal Data and Longitudinal Research

The objectives of longitudinal data collection and research as outlined by Baltes and Nesselroade (1979):

• Identify intraindividual change : Examine changes at the individual level over time, including long-term trends or short-term fluctuations. Requires multiple measurements and individual-level analysis.
• Identify interindividual differences in intraindividual change : Evaluate whether changes vary across individuals and relate that to other variables. Requires repeated measures for multiple individuals plus relevant covariates.
• Analyze interrelationships in change : Study how two or more processes unfold and influence each other over time. Requires longitudinal data on multiple variables and appropriate statistical models.
• Analyze causes of intraindividual change: This objective refers to identifying factors or mechanisms that explain changes within individuals over time. For example, a researcher might want to understand what drives a person’s mood fluctuations over days or weeks. Or what leads to systematic gains or losses in one’s cognitive abilities across the lifespan.
• Analyze causes of interindividual differences in intraindividual change : Identify mechanisms that explain within-person changes and differences in changes across people. Requires repeated data on outcomes and covariates for multiple individuals plus dynamic statistical models.

How to Perform a Longitudinal Study

When beginning to develop your longitudinal study, you must first decide if you want to collect your own data or use data that has already been gathered.

Using already collected data will save you time, but it will be more restricted and limited than collecting it yourself. When collecting your own data, you can choose to conduct either a retrospective or prospective study .

In a retrospective study, you are collecting data on events that have already occurred. You can examine historical information, such as medical records, in order to understand the past. In a prospective study, on the other hand, you are collecting data in real-time. Prospective studies are more common for psychology research.

Once you determine the type of longitudinal study you will conduct, you then must determine how, when, where, and on whom the data will be collected.

A standardized study design is vital for efficiently measuring a population. Once a study design is created, researchers must maintain the same study procedures over time to uphold the validity of the observation.

A schedule should be maintained, complete results should be recorded with each observation, and observer variability should be minimized.

Researchers must observe each subject under the same conditions to compare them. In this type of study design, each subject is the control.

Methodological Considerations

Important methodological considerations include testing measurement invariance of constructs across time, appropriately handling missing data, and using accelerated longitudinal designs that sample different age cohorts over overlapping time periods.

Testing measurement invariance

Testing measurement invariance involves evaluating whether the same construct is being measured in a consistent, comparable way across multiple time points in longitudinal research.

This includes assessing configural, metric, and scalar invariance through confirmatory factor analytic approaches. Ensuring invariance gives more confidence when drawing inferences about change over time.

Missing data

Missing data can occur during initial sampling if certain groups are underrepresented or fail to respond.

Attrition over time is the main source – participants dropping out for various reasons. The consequences of missing data are reduced statistical power and potential bias if dropout is nonrandom.

Handling missing data appropriately in longitudinal studies is critical to reducing bias and maintaining power.

It is important to minimize attrition by tracking participants, keeping contact info up to date, engaging them, and providing incentives over time.

Techniques like maximum likelihood estimation and multiple imputation are better alternatives to older methods like listwise deletion. Assumptions about missing data mechanisms (e.g., missing at random) shape the analytic approaches taken.

Accelerated longitudinal designs

Accelerated longitudinal designs purposefully create missing data across age groups.

Accelerated longitudinal designs strategically sample different age cohorts at overlapping periods. For example, assessing 6th, 7th, and 8th graders at yearly intervals would cover 6-8th grade development over a 3-year study rather than following a single cohort over that timespan.

This increases the speed and cost-efficiency of longitudinal data collection and enables the examination of age/cohort effects. Appropriate multilevel statistical models are required to analyze the resulting complex data structure.

In addition to those considerations, optimizing the time lags between measurements, maximizing participant retention, and thoughtfully selecting analysis models that align with the research questions and hypotheses are also vital in ensuring robust longitudinal research.

So, careful methodology is key throughout the design and analysis process when working with repeated-measures data.

Cohort effects

A cohort refers to a group born in the same year or time period. Cohort effects occur when different cohorts show differing trajectories over time.

Cohort effects can bias results if not accounted for, especially in accelerated longitudinal designs which assume cohort equivalence.

Detecting cohort effects is important but can be challenging as they are confounded with age and time of measurement effects.

Cohort effects can also interfere with estimating other effects like retest effects. This happens because comparing groups to estimate retest effects relies on cohort equivalence.

Overall, researchers need to test for and control cohort effects which could otherwise lead to invalid conclusions. Careful study design and analysis is required.

Retest effects

Retest effects refer to gains in performance that occur when the same or similar test is administered on multiple occasions.

For example, familiarity with test items and procedures may allow participants to improve their scores over repeated testing above and beyond any true change.

Specific examples include:

• Memory tests – Learning which items tend to be tested can artificially boost performance over time
• Cognitive tests – Becoming familiar with the testing format and particular test demands can inflate scores
• Survey measures – Remembering previous responses can bias future responses over multiple administrations
• Interviews – Comfort with the interviewer and process can lead to increased openness or recall

To estimate retest effects, performance of retested groups is compared to groups taking the test for the first time. Any divergence suggests inflated scores due to retesting rather than true change.

If unchecked in analysis, retest gains can be confused with genuine intraindividual change or interindividual differences.

This undermines the validity of longitudinal findings. Thus, testing and controlling for retest effects are important considerations in longitudinal research.

Data Analysis

Longitudinal data involves repeated assessments of variables over time, allowing researchers to study stability and change. A variety of statistical models can be used to analyze longitudinal data, including latent growth curve models, multilevel models, latent state-trait models, and more.

Latent growth curve models allow researchers to model intraindividual change over time. For example, one could estimate parameters related to individuals’ baseline levels on some measure, linear or nonlinear trajectory of change over time, and variability around those growth parameters. These models require multiple waves of longitudinal data to estimate.

Multilevel models are useful for hierarchically structured longitudinal data, with lower-level observations (e.g., repeated measures) nested within higher-level units (e.g., individuals). They can model variability both within and between individuals over time.

Latent state-trait models decompose the covariance between longitudinal measurements into time-invariant trait factors, time-specific state residuals, and error variance. This allows separating stable between-person differences from within-person fluctuations.

There are many other techniques like latent transition analysis, event history analysis, and time series models that have specialized uses for particular research questions with longitudinal data. The choice of model depends on the hypotheses, timescale of measurements, age range covered, and other factors.

In general, these various statistical models allow investigation of important questions about developmental processes, change and stability over time, causal sequencing, and both between- and within-person sources of variability. However, researchers must carefully consider the assumptions behind the models they choose.

Longitudinal vs. Cross-Sectional Studies

Longitudinal studies and cross-sectional studies are two different observational study designs where researchers analyze a target population without manipulating or altering the natural environment in which the participants exist.

Yet, there are apparent differences between these two forms of study. One key difference is that longitudinal studies follow the same sample of people over an extended period of time, while cross-sectional studies look at the characteristics of different populations at a given moment in time.

Longitudinal studies tend to require more time and resources, but they can be used to detect cause-and-effect relationships and establish patterns among subjects.

On the other hand, cross-sectional studies tend to be cheaper and quicker but can only provide a snapshot of a point in time and thus cannot identify cause-and-effect relationships.

Both studies are valuable for psychologists to observe a given group of subjects. Still, cross-sectional studies are more beneficial for establishing associations between variables, while longitudinal studies are necessary for examining a sequence of events.

1. Are longitudinal studies qualitative or quantitative?

Longitudinal studies are typically quantitative. They collect numerical data from the same subjects to track changes and identify trends or patterns.

However, they can also include qualitative elements, such as interviews or observations, to provide a more in-depth understanding of the studied phenomena.

2. What’s the difference between a longitudinal and case-control study?

Case-control studies compare groups retrospectively and cannot be used to calculate relative risk. Longitudinal studies, though, can compare groups either retrospectively or prospectively.

In case-control studies, researchers study one group of people who have developed a particular condition and compare them to a sample without the disease.

Case-control studies look at a single subject or a single case, whereas longitudinal studies are conducted on a large group of subjects.

3. Does a longitudinal study have a control group?

Yes, a longitudinal study can have a control group . In such a design, one group (the experimental group) would receive treatment or intervention, while the other group (the control group) would not.

Both groups would then be observed over time to see if there are differences in outcomes, which could suggest an effect of the treatment or intervention.

However, not all longitudinal studies have a control group, especially observational ones and not testing a specific intervention.

Baltes, P. B., & Nesselroade, J. R. (1979). History and rationale of longitudinal research. In J. R. Nesselroade & P. B. Baltes (Eds.), (pp. 1–39). Academic Press.

Cook, N. R., & Ware, J. H. (1983). Design and analysis methods for longitudinal research. Annual review of public health , 4, 1–23.

Fitchett, G., Rybarczyk, B., Demarco, G., & Nicholas, J.J. (1999). The role of religion in medical rehabilitation outcomes: A longitudinal study. Rehabilitation Psychology, 44, 333-353.

Harvard Second Generation Study. (n.d.). Harvard Second Generation Grant and Glueck Study. Harvard Study of Adult Development. Retrieved from https://www.adultdevelopmentstudy.org.

Le Mare, L., & Audet, K. (2006). A longitudinal study of the physical growth and health of postinstitutionalized Romanian adoptees. Pediatrics & child health, 11 (2), 85-91.

Luo, Y., Hawkley, L. C., Waite, L. J., & Cacioppo, J. T. (2012). Loneliness, health, and mortality in old age: a national longitudinal study. Social science & medicine (1982), 74 (6), 907–914.

Marques, S. C., Pais-Ribeiro, J. L., & Lopez, S. J. (2011). The role of positive psychology constructs in predicting mental health and academic achievement in children and adolescents: A two-year longitudinal study. Journal of Happiness Studies: An Interdisciplinary Forum on Subjective Well-Being, 12( 6), 1049–1062.

Sidney W.A. Dekker & Wilmar B. Schaufeli (1995) The effects of job insecurity on psychological health and withdrawal: A longitudinal study, Australian Psychologist, 30: 1,57-63.

Stice, E., Mazotti, L., Krebs, M., & Martin, S. (1998). Predictors of adolescent dieting behaviors: A longitudinal study. Psychology of Addictive Behaviors, 12 (3), 195–205.

Tegan Cruwys, Katharine H Greenaway & S Alexander Haslam (2015) The Stress of Passing Through an Educational Bottleneck: A Longitudinal Study of Psychology Honours Students, Australian Psychologist, 50:5, 372-381.

Thomas, L. (2020). What is a longitudinal study? Scribbr. Retrieved from https://www.scribbr.com/methodology/longitudinal-study/

Van der Vorst, H., Engels, R. C. M. E., Meeus, W., & Deković, M. (2006). Parental attachment, parental control, and early development of alcohol use: A longitudinal study. Psychology of Addictive Behaviors, 20 (2), 107–116.

Further Information

• Schaie, K. W. (2005). What can we learn from longitudinal studies of adult development?. Research in human development, 2 (3), 133-158.
• Caruana, E. J., Roman, M., Hernández-Sánchez, J., & Solli, P. (2015). Longitudinal studies. Journal of thoracic disease, 7 (11), E537.

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What Is a Longitudinal Study?

Tracking Variables Over Time

Steve McAlister / The Image Bank / Getty Images

The Typical Longitudinal Study

Potential pitfalls, frequently asked questions.

A longitudinal study follows what happens to selected variables over an extended time. Psychologists use the longitudinal study design to explore possible relationships among variables in the same group of individuals over an extended period.

Once researchers have determined the study's scope, participants, and procedures, most longitudinal studies begin with baseline data collection. In the days, months, years, or even decades that follow, they continually gather more information so they can observe how variables change over time relative to the baseline.

For example, imagine that researchers are interested in the mental health benefits of exercise in middle age and how exercise affects cognitive health as people age. The researchers hypothesize that people who are more physically fit in their 40s and 50s will be less likely to experience cognitive declines in their 70s and 80s.

Longitudinal vs. Cross-Sectional Studies

Longitudinal studies, a type of correlational research , are usually observational, in contrast with cross-sectional research . Longitudinal research involves collecting data over an extended time, whereas cross-sectional research involves collecting data at a single point.

To test this hypothesis, the researchers recruit participants who are in their mid-40s to early 50s. They collect data related to current physical fitness, exercise habits, and performance on cognitive function tests. The researchers continue to track activity levels and test results for a certain number of years, look for trends in and relationships among the studied variables, and test the data against their hypothesis to form a conclusion.

Examples of Early Longitudinal Study Design

Examples of longitudinal studies extend back to the 17th century, when King Louis XIV periodically gathered information from his Canadian subjects, including their ages, marital statuses, occupations, and assets such as livestock and land. He used the data to spot trends over the years and understand his colonies' health and economic viability.

In the 18th century, Count Philibert Gueneau de Montbeillard conducted the first recorded longitudinal study when he measured his son every six months and published the information in "Histoire Naturelle."

The Genetic Studies of Genius (also known as the Terman Study of the Gifted), which began in 1921, is one of the first studies to follow participants from childhood into adulthood. Psychologist Lewis Terman's goal was to examine the similarities among gifted children and disprove the common assumption at the time that gifted children were "socially inept."

Types of Longitudinal Studies

Longitudinal studies fall into three main categories.

• Panel study : Sampling of a cross-section of individuals
• Cohort study : Sampling of a group based on a specific event, such as birth, geographic location, or experience
• Retrospective study : Review of historical information such as medical records

Benefits of Longitudinal Research

A longitudinal study can provide valuable insight that other studies can't. They're particularly useful when studying developmental and lifespan issues because they allow glimpses into changes and possible reasons for them.

For example, some longitudinal studies have explored differences and similarities among identical twins, some reared together and some apart. In these types of studies, researchers tracked participants from childhood into adulthood to see how environment influences personality , achievement, and other areas.

Because the participants share the same genetics , researchers chalked up any differences to environmental factors . Researchers can then look at what the participants have in common and where they differ to see which characteristics are more strongly influenced by either genetics or experience. Note that adoption agencies no longer separate twins, so such studies are unlikely today. Longitudinal studies on twins have shifted to those within the same household.

As with other types of psychology research, researchers must take into account some common challenges when considering, designing, and performing a longitudinal study.

Longitudinal studies require time and are often quite expensive. Because of this, these studies often have only a small group of subjects, which makes it difficult to apply the results to a larger population.

Selective Attrition

Participants sometimes drop out of a study for any number of reasons, like moving away from the area, illness, or simply losing motivation . This tendency, known as selective attrition , shrinks the sample size and decreases the amount of data collected.

If the final group no longer reflects the original representative sample , attrition can threaten the validity of the experiment. Validity refers to whether or not a test or experiment accurately measures what it claims to measure. If the final group of participants doesn't represent the larger group accurately, generalizing the study's conclusions is difficult.

The World’s Longest-Running Longitudinal Study

Lewis Terman aimed to investigate how highly intelligent children develop into adulthood with his "Genetic Studies of Genius." Results from this study were still being compiled into the 2000s. However, Terman was a proponent of eugenics and has been accused of letting his own sexism , racism , and economic prejudice influence his study and of drawing major conclusions from weak evidence. However, Terman's study remains influential in longitudinal studies. For example, a recent study found new information on the original Terman sample, which indicated that men who skipped a grade as children went on to have higher incomes than those who didn't.

A Word From Verywell

Longitudinal studies can provide a wealth of valuable information that would be difficult to gather any other way. Despite the typical expense and time involved, longitudinal studies from the past continue to influence and inspire researchers and students today.

A longitudinal study follows up with the same sample (i.e., group of people) over time, whereas a cross-sectional study examines one sample at a single point in time, like a snapshot.

A longitudinal study can occur over any length of time, from a few weeks to a few decades or even longer.

That depends on what researchers are investigating. A researcher can measure data on just one participant or thousands over time. The larger the sample size, of course, the more likely the study is to yield results that can be extrapolated.

Piccinin AM, Knight JE. History of longitudinal studies of psychological aging . Encyclopedia of Geropsychology. 2017:1103-1109. doi:10.1007/978-981-287-082-7_103

Terman L. Study of the gifted . In: The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation. 2018. doi:10.4135/9781506326139.n691

Sahu M, Prasuna JG. Twin studies: A unique epidemiological tool .  Indian J Community Med . 2016;41(3):177-182. doi:10.4103/0970-0218.183593

Almqvist C, Lichtenstein P. Pediatric twin studies . In:  Twin Research for Everyone . Elsevier; 2022:431-438.

Warne RT. An evaluation (and vindication?) of Lewis Terman: What the father of gifted education can teach the 21st century . Gifted Child Q. 2018;63(1):3-21. doi:10.1177/0016986218799433

Warne RT, Liu JK. Income differences among grade skippers and non-grade skippers across genders in the Terman sample, 1936–1976 . Learning and Instruction. 2017;47:1-12. doi:10.1016/j.learninstruc.2016.10.004

Wang X, Cheng Z. Cross-sectional studies: Strengths, weaknesses, and recommendations .  Chest . 2020;158(1S):S65-S71. doi:10.1016/j.chest.2020.03.012

Caruana EJ, Roman M, Hernández-Sánchez J, Solli P. Longitudinal studies .  J Thorac Dis . 2015;7(11):E537-E540. doi:10.3978/j.issn.2072-1439.2015.10.63

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

• What’s a Longitudinal Study? Types, Uses & Examples

Research can take anything from a few minutes to years or even decades to complete. When a systematic investigation goes on for an extended period, it’s most likely that the researcher is carrying out a longitudinal study of the sample population. So how does this work? 

In the most simple terms, a longitudinal study involves observing the interactions of the different variables in your research population, exposing them to various causal factors, and documenting the effects of this exposure. It’s an intelligent way to establish causal relationships within your sample population. 

In this article, we’ll show you several ways to adopt longitudinal studies for your systematic investigation and how to avoid common pitfalls. 

What is a Longitudinal Study? 

A longitudinal study is a correlational research method that helps discover the relationship between variables in a specific target population. It is pretty similar to a cross-sectional study , although in its case, the researcher observes the variables for a longer time, sometimes lasting many years. 

For example, let’s say you are researching social interactions among wild cats. You go ahead to recruit a set of newly-born lion cubs and study how they relate with each other as they grow. Periodically, you collect the same types of data from the group to track their development. 

The advantage of this extended observation is that the researcher can witness the sequence of events leading to the changes in the traits of both the target population and the different groups. It can identify the causal factors for these changes and their long-term impact. 

Characteristics of Longitudinal Studies

1. Non-interference: In longitudinal studies, the researcher doesn’t interfere with the participants’ day-to-day activities in any way. When it’s time to collect their responses , the researcher administers a survey with qualitative and quantitative questions . 

2. Observational: As we mentioned earlier, longitudinal studies involve observing the research participants throughout the study and recording any changes in traits that you notice. 

3. Timeline: A longitudinal study can span weeks, months, years, or even decades. This dramatically contrasts what is obtainable in cross-sectional studies that only last for a short time. 

Cross-Sectional vs. Longitudinal Studies 

• Definition 

A cross-sectional study is a type of observational study in which the researcher collects data from variables at a specific moment to establish a relationship among them. On the other hand, longitudinal research observes variables for an extended period and records all the changes in their relationship. 

Longitudinal studies take a longer time to complete. In some cases, the researchers can spend years documenting the changes among the variables plus their relationships. For cross-sectional studies, this isn’t the case. Instead, the researcher collects information in a relatively short time frame and makes relevant inferences from this data. 

While cross-sectional studies give you a snapshot of the situation in the research environment, longitudinal studies are better suited for contexts where you need to analyze a problem long-term. 

• Sample Data

Longitudinal studies repeatedly observe the same sample population, while cross-sectional studies are conducted with different research samples. 

Because longitudinal studies span over a more extended time, they typically cost more money than cross-sectional observations. 

Types of Longitudinal Studies 

The three main types of longitudinal studies are: 

• Panel Study
• Retrospective Study
• Cohort Study 

These methods help researchers to study variables and account for qualitative and quantitative data from the research sample. 

1. Panel Study 

In a panel study, the researcher uses data collection methods like surveys to gather information from a fixed number of variables at regular but distant intervals, often spinning into a few years. It’s primarily designed for quantitative research, although you can use this method for qualitative data analysis . 

When To Use Panel Study

If you want to have first-hand, factual information about the changes in a sample population, then you should opt for a panel study. For example, medical researchers rely on panel studies to identify the causes of age-related changes and their consequences. 

Advantages of Panel Study  

• It helps you identify the causal factors of changes in a research sample. 
• It also allows you to witness the impact of these changes on the properties of the variables and information needed at different points of their existing relationship. 
• Panel studies can be used to obtain historical data from the sample population. 

Disadvantages of Panel Studies

• Conducting a panel study is pretty expensive in terms of time and resources. 
• It might be challenging to gather the same quality of data from respondents at every interval. 

2. Retrospective Study

In a retrospective study, the researcher depends on existing information from previous systematic investigations to discover patterns leading to the study outcomes. In other words, a retrospective study looks backward. It examines exposures to suspected risk or protection factors concerning an outcome established at the start of the study.

When To Use Retrospective Study 

Retrospective studies are best for research contexts where you want to quickly estimate an exposure’s effect on an outcome. It also helps you to discover preliminary measures of association in your data. 

Medical researchers adopt retrospective study methods when they need to research rare conditions. 

Advantages of Retrospective Study

• Retrospective studies happen at a relatively smaller scale and do not require much time to complete. 
• It helps you to study rare outcomes when prospective surveys are not feasible.

Disadvantages of Retrospective Study

• It is easily affected by recall bias or misclassification bias.
• It often depends on convenience sampling, which is prone to selection bias. 

3. Cohort Study  

A cohort study entails collecting information from a group of people who share specific traits or have experienced a particular occurrence simultaneously. For example, a researcher might conduct a cohort study on a group of Black school children in the U.K. 

During cohort study, the researcher exposes some group members to a specific characteristic or risk factor. Then, she records the outcome of this exposure and its impact on the exposed variables. 

When To Use Cohort Study

You should conduct a cohort study if you’re looking to establish a causal relationship within your data sets. For example, in medical research, cohort studies investigate the causes of disease and establish links between risk factors and effects. 

Advantages of Cohort Studies

• It allows you to study multiple outcomes that can be associated with one risk factor. 
• Cohort studies are designed to help you measure all variables of interest. 

Disadvantages of Cohort Studies

• Cohort studies are expensive to conduct.
• Throughout the process, the researcher has less control over variables. 

When Would You Use a Longitudinal Study? 

If you’re looking to discover the relationship between variables and the causal factors responsible for changes, you should adopt a longitudinal approach to your systematic investigation. Longitudinal studies help you to analyze change over a meaningful time. 

How to Perform a Longitudinal Study?

There are only two approaches you can take when performing a longitudinal study. You can either source your own data or use previously gathered data.

1. Sourcing for your own data

Collecting your own data is a more verifiable method because you can trust your own data. The way you collect your data is also heavily dependent on the type of study you’re conducting.

If you’re conducting a retrospective study, you’d have to collect data on events that have already happened. An example is going through records to find patterns in cancer patients.

For a prospective study, you collect the data in real-time. This means finding a sample population, following them, and documenting your findings over the course of your study.

Irrespective of what study type you’d be conducting, you need a versatile data collection tool to help you accurately record your data. One we strongly recommend is Formplus . Signup here for free.

2. Using previously gathered data

Governmental and research institutes often carry out longitudinal studies and make the data available to the public. So you can pick up their previously researched data and use them for your own study. An example is the UK data service website .

Using previously gathered data isn’t just easy, they also allow you to carry out research over a long period of time. 

The downside to this method is that it’s very restrictive because you can only use the data set available to you. You also have to thoroughly examine the source of the data given to you. 

Advantages of a Longitudinal Study 

• Longitudinal studies help you discover variable patterns over time, leading to more precise causal relationships and research outcomes. 
• When researching developmental trends, longitudinal studies allow you to discover changes across lifespans and arrive at valid research outcomes. 
• They are highly flexible, which means the researcher can adjust the study’s focus while it is ongoing. 
• Unlike other research methods, longitudinal studies collect unique, long-term data and highlight relationships that cannot be discovered in a short-term investigation. 
• You can collect additional data to study unexpected findings at any point in your systematic investigation. 

Disadvantages and Limitations of a Longitudinal Study 

• It’s difficult to predict the results of longitudinal studies because of the extended time frame. Also, it may take several years before the data begins to produce observable patterns or relationships that can be monitored. 
• It costs lots of money to sustain a research effort for years. You’ll keep incurring costs every year compared to other forms of research that can be completed in a smaller fraction of the time.
• Longitudinal studies require a large sample size which might be challenging to achieve. Without this, the entire investigation will have little or no impact. 
• Longitudinal studies often experience panel attrition. This happens when some members of the research sample are unable to complete the study due to several reasons like changes in contact details, refusal, incapacity, and even death. 

Longitudinal Studies Examples

How does a longitudinal study work in the real world? To answer this, let’s consider a few typical scenarios. 

A researcher wants to know the effects of a low-carb diet on weight loss. So, he gathers a group of obese men and kicks off the systematic investigation using his preferred longitudinal study method. He records information like how much they weigh, the number of carbs in their diet, and the like at different points. All these data help him to arrive at valid research outcomes. 

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A researcher wants to know if there’s any relationship between children who drink milk before school and high classroom performance . First, he uses a sampling technique to gather a large research population. 

Then, he conducts a baseline survey to establish the premise of the research for later comparison. Next, the researcher gives a log to each participant to keep track of predetermined research variables . 

Example 3  

You decide to study how a particular diet affects athletes’ performance over time. First, you gather your sample population , establish a baseline for the research, and observe and record the required data.

Longitudinal Studies Frequently Asked Questions (FAQs) 

• Are Longitudinal Studies Quantitative or Qualitative?

Longitudinal studies are primarily a qualitative research method because the researcher observes and records changes in variables over an extended period. However, it can also be used to gather quantitative data depending on your research context. 

• What Is Most Likely the Biggest Problem with Longitudinal Research?

The biggest challenge with longitudinal research is panel attrition. Due to the length of the research process, some variables might be unable to complete the study for one reason or the other. When this happens, it can distort your data and research outcomes. 

• What is Longitudinal Data Collection?

Longitudinal data collection is the process of gathering information from the same sample population over a long period. Longitudinal data collection uses interviews, surveys, and observation to collect the required information from research sources. 

• What is the Difference Between Longitudinal Data and a Time Series Analysis?

Because longitudinal studies collect data over a long period, they are often mistaken for time series analysis. So what’s the real difference between these two concepts? 

In a time series analysis, the researcher focuses on a single individual at multiple time intervals. Meanwhile, longitudinal data focuses on multiple individuals at various time intervals. 

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Longitudinal Study: Overview, Examples & Benefits

By Jim Frost Leave a Comment

What is a Longitudinal Study?

A longitudinal study is an experimental design that takes repeated measurements of the same subjects over time. These studies can span years or even decades. Unlike cross-sectional studies , which analyze data at a single point, longitudinal studies track changes and developments, producing a more dynamic assessment.

A cohort study is a specific type of longitudinal study focusing on a group of people sharing a common characteristic or experience within a defined period.

Imagine tracking a group of individuals over time. Researchers collect data regularly, analyzing how specific factors evolve or influence outcomes. This method offers a dynamic view of trends and changes.

Consider a study tracking 100 high school students’ academic performances annually for ten years. Researchers observe how various factors like teaching methods, family background, and personal habits impact their academic growth over time.

Researchers frequently use longitudinal studies in the following fields:

• Psychology: Understanding behavioral changes.
• Sociology: Observing societal trends.
• Medicine: Tracking disease progression.
• Education: Assessing long-term educational outcomes.

Learn more about Experimental Designs: Definition and Types .

Duration of Longitudinal Studies

Typically, the objectives dictate how long researchers run a longitudinal study. Studies focusing on rapid developmental phases, like early childhood, might last a few years. On the other hand, exploring long-term trends, like aging, can span decades. The key is to align the duration with the research goals.

Implementing a Longitudinal Study: Your Options

When planning a longitudinal study, you face a crucial decision: gather new data or use existing datasets.

Option 1: Utilizing Existing Data

Governments and research centers often share data from their longitudinal studies. For instance, the U.S. National Longitudinal Surveys (NLS) has been tracking thousands of Americans since 1979, offering a wealth of data accessible through the Bureau of Labor Statistics .

This type of data is usually reliable, offering insights over extended periods. However, it’s less flexible than the data that the researchers can collect themselves. Often, details are aggregated to protect privacy, limiting analysis to broader regions. Additionally, the original study’s variables restrict you, and you can’t tailor data collection to meet your study’s needs.

If you opt for existing data, scrutinize the dataset’s origin and the available information.

Option 2: Collecting Data Yourself

If you decide to gather your own data, your approach depends on the study type: retrospective or prospective.

A retrospective longitudinal study focuses on past events. This type is generally quicker and less costly but more prone to errors.

The prospective form of this study tracks a subject group over time, collecting data as events unfold. This approach allows the researchers to choose the variables they’ll measure and how they’ll measure them. Usually, these studies produce the best data but are more expensive.

While retrospective studies save time and money, prospective studies, though more resource-intensive, offer greater accuracy.

Learn more about Retrospective and Prospective Studies .

Advantages of a Longitudinal Study

Longitudinal studies can provide insight into developmental phases and long-term changes, which cross-sectional studies might miss.

These studies can help you determine the sequence of events. By taking multiple observations of the same individuals over time, you can attribute changes to the other variables rather than differences between subjects. This benefit of having the subjects be their own controls is one that applies to all within-subjects studies, also known as repeated measures design. Learn more about Repeated Measures Designs .

Consider a longitudinal study examining the influence of a consistent reading program on children’s literacy development. In a longitudinal framework, factors like innate linguistic ability, which typically don’t fluctuate significantly, are inherently accounted for by using the same group of students over time. This approach allows for a more precise assessment of the reading program’s direct impact over the study’s duration.

Collectively, these benefits help you establish causal relationships. Consequently, longitudinal studies excel in revealing how variables change over time and identifying potential causal relationships .

Disadvantages of a Longitudinal Study

A longitudinal study can be time-consuming and expensive, given its extended duration.

For example, a 30-year study on the aging process may require substantial funding for decades and a long-term commitment from researchers and staff.

Over time, participants may selectively drop out, potentially skewing results and reducing the study’s effectiveness.

For instance, in a study examining the long-term effects of a new fitness regimen, more physically fit participants might be less likely to drop out than those finding the regimen challenging. This scenario potentially skews the results to exaggerate the program’s effectiveness.

Maintaining consistent data collection methods and standards over a long period can be challenging.

For example, a longitudinal study that began using face-to-face interviews might face consistency issues if it later shifts to online surveys, potentially affecting the quality and comparability of the responses.

In conclusion, longitudinal studies are powerful tools for understanding changes over time. While they come with challenges, their ability to uncover trends and causal relationships makes them invaluable in many fields. As with any research method, understanding their strengths and limitations is critical to effectively utilizing their potential.

Newman AB. An overview of the design, implementation, and analyses of longitudinal studies on aging . J Am Geriatr Soc. 2010 Oct;58 Suppl 2:S287-91. doi: 10.1111/j.1532-5415.2010.02916.x. PMID: 21029055; PMCID: PMC3008590.

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Longitudinal Studies: Methods, Benefits and Challenges

Introduction

What is a longitudinal study, what are examples of longitudinal studies, longitudinal studies vs. cross-sectional studies, benefits of longitudinal studies, types of longitudinal studies, how do you conduct a longitudinal study, challenges of longitudinal research.

Longitudinal research refers to any study that collects the same sample of data from the same group of people at different points in time. While time-consuming and potentially costly in terms of resources and effort, a longitudinal study has enormous utility in understanding complex phenomena that might change as time passes.

In this article, we will explore the nature and importance of longitudinal studies to allow you to decide whether your research inquiry warrants a longitudinal inquiry or if a cross-sectional study is more appropriate.

To understand a longitudinal study, let's start with a simple survey as an example. Determining the popularity of a particular product or service at a specific point in time can simply be a matter of collecting and analyzing survey responses from a certain number of people within a population. The qualitative and quantitative data collected from these surveys can tell you what people think at the moment those surveys were conducted. This is what is known as a cross-sectional study .

Now imagine the product that you're trying to assess is seasonal like a brand of ice cream or hot chocolate. What's popular in summer may not be popular in winter, and trends come and go as competing products enter the market. In this context, the one survey that was conducted is merely a snapshot of a moving phenomenon at a single point in time.

In a longitudinal study design, that same survey will be distributed to the same group of people at different time intervals (e.g., twice a year or once a month) to allow researchers to see if there are any changes. Perhaps there is an ice cream that is as popular in the winter as it is in the summer, which may be worth identifying to expand profitability. A longitudinal study would thus be useful to explore this question.

Longitudinal research isn't conducted simply for the sake of being able to say research was conducted over a extended period of time. A longitudinal analysis collects data at different points in time to observe changes in the characteristics of the object of inquiry. Ultimately, collecting data for a longitudinal study can help identify cause-and-effect relationships that cannot otherwise be perceived in discrete or cross-sectional studies.

Longitudinal studies are found in many research fields where time is an important factor. Let's look at examples in three different research areas.

Classroom research is often longitudinal because of the acknowledgment that successful learning takes place over time and not merely in a single class session. Such studies take place over several classes, perhaps over a semester or an entire academic year. A researcher might observe the same group of students as they progress academically or, conversely, identify any significant decline in learning outcomes to determine how changes in teaching and learning over time might affect student development.

Health sciences

Medical research often relies on longitudinal studies to determine the effectiveness and risk factors involved with drugs, treatments, or other medical remedies. Consider a dietary supplement that is purported to help people lose weight. Perhaps, in the beginning, people who take this supplement actually do lose weight. But what happens later on? Do they keep the weight off, gain it back or, even worse, gain even more weight in the long term? A longitudinal study can help researchers determine if that supplement produces sustainable results or is merely a quick fix that has negative side effects later on.

Product life cycles and market trends can take extended periods of time to manifest. In the meantime, competing products might enter the market and consequently affect customer loyalty and product image. If a cross-sectional study captures a snapshot of opinions in the marketplace, then think of a longitudinal study as several snapshots spread out over time to allow researchers to observe changes in market behavior and their underlying causes as time passes.

Cross-sectional studies are discrete studies that capture data within a particular context at a particular point in time. These kinds of studies are more appropriate for research inquiries that don't examine some form of development or evolution, such as concepts or phenomena that are generally static or unchanging over extended periods of time.

To determine which type of study would be more appropriate for your research inquiry, it's important to identify the object of inquiry that is being studied. Ask yourself the following questions when planning your study:

• Do you need an extended period of time to sufficiently capture the phenomenon?
• Is the sample of data collected likely to change over time?
• Is it feasible to commit time and resources to an extended study?

If you said yes to all of these questions, a longitudinal study would be suited to addressing your research questions . Otherwise, cross-sectional studies may be more appropriate for your research.

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A longitudinal study can provide many benefits potentially relevant to the research question you are looking to address. Here are three different advantages you might consider.

Abundance of data

In many cases, research rigor is served by collecting abundant data . Research approaches like thematic analysis and content analysis benefit from a large set of data that helps you identify the most frequently occurring phenomena within a research context. Large data sets collected through longitudinal studies can be useful for separating abundance from anecdotes.

Identification of patterns

Analyzing patterns often implies exploring how things interact sequentially or over time, which is best captured with longitudinal data. Think about, for example, how sports competitions and political elections take place over a year or even multiple years. Construction of ships and buildings can be a long and protracted process. Doctoral students can spend four or more years before earning their degree. A simple cross-sectional study in such contexts may not gather sufficient data captured over a period of time long enough to observe sequences of related events.

Observation of relationships

Certain relationships between different phenomena can only be observed longitudinally. The famous marshmallow test that asserted connections between behaviors in childhood and later life outcomes spawned decades of longitudinal study. Even if your research is much simpler, your research question might involve the observation of distant but related phenomena that only a longitudinal study can capture.

There are two types of longitudinal studies to choose from, primarily depending on what you are looking to examine. Keep in mind that longitudinal study design, no matter what type of study you might pursue, is a matter of sustaining a research inquiry over time to capture the necessary data. It's important that your decision-making process is both transparent and intentional for the sake of research rigor.

Cohort studies

A cohort study examines a group of people that share a common trait. This trait could be a similar age group, a common level of education, or a shared experience.

An example of a cohort study is one that looks to identify factors related to successful aging found in lifestyles among people of middle age. Such a study could observe a group of people, all of whom are similar in age, to identify a common range of lifestyles and activities that are applicable for others of the same age group.

Panel studies

The difference between a cohort study and a panel study is that panel studies collect data from within a general population, rather than a specific set of particular individuals with a common characteristic. The goal of a panel study is to examine a representative sample of a larger population rather than a specific subset of people.

A longitudinal survey that adopts a panel study model, for example, would randomly sample a population and send out questionnaires to the same sample of people over time. Such a survey could look at changes in everyday habits regarding spending or work-life balance and how they might be influenced by environmental or economic shifts from one period of time to the next.

Planning a prospective or future research study that is longitudinal requires careful attention to detail prior to conducting the study. By itself, a longitudinal study can be considered a repeated sequence of the same discrete study across different periods of time.

However, ensuring that multiple iterations of the same study are conducted repeatedly and rigorously is the challenge in longitudinal studies. With that in mind, let's look at some of the different research methods that might be employed in longitudinal research.

Observational research

Action research and ethnographies rely on longitudinal observations to provide sufficient depth to the cultural practices and interactions that are under study. In anthropological and sociological research, some phenomena are so complex or dynamic that they can only be observed longitudinally.

Organizational research, for example, employs longitudinal research to identify how people in the workplace or other similar settings interact with each other. This kind of research is useful for understanding how rapport is established and whether productivity increases as a result.

A longitudinal survey can address research questions that deal with opinions and perspectives that may change over time. Unlike a cross-sectional survey from a particular point in time, longitudinal surveys are administered repeatedly to the same group of people to collect data on changes or developments.

A personal wellness study, for example, might examine how healthy habits (or the lack thereof) affect health by asking respondents questions about their daily routine. By comparing their routines over time with information such as blood pressure, weight, and waist size, survey data on lifestyle routines can allow researchers to identify what habits can cause the greatest improvement in individual health.

Experiments

Various experimental studies, especially in medical research, can be longitudinal in nature. A longitudinal experiment usually collects data from a control group and an experimental group to observe the effects of a certain treatment on the same participants over a period of time.

This type of research is commonly employed to examine the effects of medical treatments on outcomes such as cardiovascular disease or diabetes. The requirements for governmental approval are incredibly stringent and call for rigorous data collection that establishes causality.

Needless to say, longitudinal studies tend to be time-consuming. The most obvious drawback of longitudinal studies is that they take up a significant portion of researchers' time and effort.

However, there are other disadvantages of longitudinal studies, particularly the likelihood of participant attrition. In other words, the more lengthy the study, the more likely it is that participants may drop out of the study. This is especially true when working with vulnerable or marginalized populations such as migrant workers or homeless people, populations that may not always be easy to contact for collecting data.

Over the course of time, the research context that a researcher studies may change with the appearance of new technologies, trends, or other developments that may not have been anticipated. While confounding influences are possible in any study, they are likely to be more abundant in studies on a longitudinal scale. As a result, it's important for the researcher to try to account for these influences when analyzing the data . It could even be worthwhile to examine how the appearance of that phenomenon or concept impacted a relevant outcome of interest in your area.

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

What is a Longitudinal Study?

A longitudinal study is defined as a research design that involves collecting data from the same subjects over an extended period to observe changes or trends over time. This type of study is particularly useful for investigating developmental patterns, tracking the progression of diseases, or analyzing the long-term effects of interventions. 

Unlike cross-sectional studies that capture a snapshot at a single point, longitudinal studies provide insights into how variables evolve over the course of months, years, or even decades.

In a longitudinal study, researchers follow a group of participants, repeatedly measuring and recording various variables of interest. This method allows for the identification of patterns, correlations, and causal relationships. 

Longitudinal studies can be prospective, where researchers begin with a cohort and follow them forward in time, or retrospective, where existing data is analyzed to understand historical changes. This comprehensive approach provides a deeper understanding of complex phenomena by capturing the nuances and dynamics of change over an extended period.

Key Characteristics of Longitudinal Studies

Key characteristics of longitudinal studies include:

• Time Dimension:

Longitudinal studies focus on the passage of time, collecting data at multiple points to observe changes, developments, or trends over an extended period. This time-centric approach distinguishes them from cross-sectional studies, which capture information at a single point in time.

• Repeated Measurements:

Participants in longitudinal studies are measured or observed multiple times. This repetitive data collection allows researchers to track individual trajectories, identify patterns, and analyze how variables evolve over time.

• Cohort Design:

Longitudinal studies often involve the creation of cohorts, groups of individuals sharing a common characteristic or experience. These cohorts are followed over time to explore specific research questions, such as the effects of an intervention, the progression of a disease, or changes in behavior.

• Prospective or Retrospective Nature:

Longitudinal studies can be prospective, where researchers start with a group of participants and follow them into the future, or retrospective, where existing data is analyzed to trace historical changes. 

• Temporal Sequences:

A significant strength of longitudinal studies is their ability to establish temporal sequences, aiding in the identification of cause-and-effect relationships. By observing changes over time, researchers can gain insights into the order and directionality of variables.

• Complex Data Analysis:

Analyzing longitudinal data requires sophisticated statistical techniques to account for the interdependence of observations within the same participant over time. Techniques such as mixed-effects models and growth curve analysis are commonly employed to handle this complexity.

• Participant Attrition:

Longitudinal studies often face challenges related to participant attrition, as maintaining engagement over an extended period can be difficult. Researchers must address issues such as dropouts to ensure the validity of their findings.

• Resource Intensiveness:

Conducting a longitudinal study demands substantial resources in terms of time, funding, and personnel. The commitment required underscores the importance of careful planning and strategic decision-making throughout the research process.

Types of Longitudinal Studies with Examples

There are various types of longitudinal studies, each designed to address specific research needs. 

Here are the key types of longitudinal studies with examples:

1. Prospective longitudinal study

In a prospective longitudinal study, researchers select a group of participants and follow them forward in time, collecting data at various intervals to observe changes or outcomes over the course of the study.

Example: Following a cohort of children from birth to adulthood to investigate the influence of early-life nutrition on cognitive development.

2. Retrospective Longitudinal Study:

In a retrospective longitudinal study, researchers analyze existing records or data to reconstruct a historical sequence of events or changes, even though the information was not originally collected for research purposes.

Example: Reviewing medical records to study the progression of a specific medical condition over the past several decades.

3. Panel Study:

A panel study involves repeatedly collecting data from the same group of individuals or “panel” at specific time intervals, allowing researchers to track changes within the group over time.

Example: Conducting surveys on the same set of households every year to study changes in income, spending habits, and lifestyle.

4. Cohort Sequential Study:

A cohort sequential study involves studying different age cohorts at the same time points, allowing researchers to examine both age-related changes and cohort-specific influences.

Example: Assessing cognitive abilities in individuals from different age groups every five years to understand how intelligence evolves across the lifespan.

Benefits of Longitudinal Studies

The benefits of longitudinal studies include:

• Temporal Understanding:

Longitudinal studies provide a detailed temporal perspective, allowing researchers to understand how variables change over time. This is crucial for uncovering developmental patterns, identifying trends, and capturing the dynamics of complex phenomena.

• Causal Inference:

By tracking changes over time, longitudinal studies facilitate the identification of causal relationships. Researchers can establish temporal sequences, helping discern cause-and-effect associations and contributing to a deeper understanding of the factors influencing outcomes.

• Individual Trajectories:

Longitudinal studies enable the tracking of individual trajectories, offering insights into the unique paths of participants. This personalized approach allows researchers to account for individual differences and identify factors influencing specific outcomes.

The repeated measurements in longitudinal studies provide rich datasets. Sophisticated statistical analyses, such as growth curve modeling, mixed-effects models, and trajectory analyses, can be employed to explore complex relationships and patterns within the data.

• Developmental Research:

Longitudinal studies are particularly valuable in developmental research, as they allow researchers to investigate changes across the lifespan. This is essential for understanding the factors that contribute to cognitive, emotional, and social development.

• Policy and Intervention Evaluation:

Researchers can assess the long-term impact of policies or interventions by conducting longitudinal studies. This is crucial for evaluating the effectiveness of interventions aimed at improving health, education, or other societal outcomes.

• Identifying Risk and Protective Factors:

Longitudinal studies help identify factors that contribute to positive outcomes (protective factors) or increase the risk of negative outcomes. This information is valuable for designing targeted interventions and preventive measures.

• Long-Term Trends and Patterns:

Longitudinal studies allow the observation of long-term trends and patterns, helping researchers and policymakers make informed decisions based on a comprehensive understanding of how variables evolve over extended periods.

• Scientific Rigor:

The rigorous design of longitudinal studies, with repeated measurements and careful participant tracking, enhances the scientific validity of findings. This design strengthens the reliability of results and contributes to the robustness of research conclusions.

• Holistic Understanding:

Longitudinal studies provide a holistic understanding of complex phenomena, capturing the interplay of various factors over time. This comprehensive approach is essential for gaining insights into the multifaceted nature of human development, behavior, and societal changes.

Best Practices for Longitudinal Studies in 2024

The best practices for conducting longitudinal studies in 2024 are:

1. Clear Research Objectives: Clearly define research objectives and hypotheses to guide the study. This ensures that data collection and analysis align with the study’s goals.

2. Robust Study Design: Develop a robust study design, considering factors such as cohort selection, measurement intervals, and data collection methods. A well-designed study minimizes bias and enhances the reliability of findings.

3. Comprehensive Participant Tracking: Implement effective participant tracking mechanisms to minimize attrition. Regular communication, incentives, and user-friendly data collection methods can enhance participant engagement and retention.

4. Ethical Considerations: Adhere to ethical guidelines in obtaining informed consent, protecting participant confidentiality, and addressing any potential risks or harm associated with the study. Ethical considerations are crucial in longitudinal research, given the extended duration of participant involvement.

5. Quality Data Collection: Use standardized instruments, train data collectors thoroughly, and employ quality control measures to maintain data integrity throughout the study.

6. Standardized Measurements: Use standardized measurements and assessment tools to facilitate comparability across time points. This enhances the reliability and validity of collected data.

7. Addressing Confounding Variables: Identify and account for potential confounding variables that could influence study outcomes. Controlling for these factors helps isolate the effects of variables of interest.

8. Data Management and Storage: Establish robust data management protocols, including secure storage, regular backups, and data encryption. Ensure compliance with data protection regulations to safeguard participant information.

9. Long-Term Funding and Resources: Secure long-term funding and allocate sufficient resources to sustain the study over its intended duration. Adequate resources are crucial for participant retention, data collection, and analysis.

10. Adaptability and Flexibility: Remain adaptable to changes in research needs or unforeseen circumstances. Flexibility in study design allows researchers to address unexpected challenges while maintaining the integrity of the study.

11. Communication with Participants: Maintain transparent and open communication with participants throughout the study. Provide regular updates on study progress, share relevant findings, and address any concerns or questions participants may have.

12. Collaboration and Interdisciplinary Approach: Foster collaboration and embrace an interdisciplinary approach when necessary. Involving experts from various fields can enhance the study’s depth and bring diverse perspectives to data analysis and interpretation.

13. Documentation and Protocols: Document study protocols comprehensively to ensure replicability and transparency. Clear documentation helps future researchers understand the study design and procedures.

14. Data Analysis Plan: Develop a detailed data analysis plan a priori. Clearly outline statistical methods, handling missing data, and addressing potential biases. This approach enhances the rigor and transparency of the study.

15. Dissemination of Findings: Share study findings through peer-reviewed publications, conferences, and other appropriate channels. Transparent reporting contributes to the scientific community’s knowledge base and facilitates the application of research outcomes.

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What is a longitudinal study?

Last updated

20 February 2023

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Longitudinal studies are common in epidemiology, economics, and medicine. People also use them in other medical and social sciences, such as to study customer trends. Researchers periodically observe and collect data from the variables without manipulating the study environment.

A company may conduct a tracking study, surveying a target audience to measure changes in attitudes and behaviors over time. The collected data doesn't change, and the time interval remains consistent. This longitudinal study can measure brand awareness, customer satisfaction , and consumer opinions and analyze the impact of an advertising campaign.

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• Types of longitudinal studies

There are two types of longitudinal studies: Cohort and panel studies.

Panel study

A panel study is a type of longitudinal study that involves collecting data from a fixed number of variables at regular but distant intervals. Researchers follow a group or groups of people over time. Panel studies are designed for quantitative analysis but are also usable for qualitative analysis .

A panel study may research the causes of age-related changes and their effects. Researchers may measure the health markers of a group over time, such as their blood pressure, blood cholesterol, and mental acuity. Then, they can compare the scores to understand how age positively or negatively correlates with these measures.

Cohort study

A cohort longitudinal study involves gathering information from a group of people with something in common, such as a specific trait or experience of the same event. The researchers observe behaviors and other details of the group over time. Unlike panel studies, you can pick a different group to test in cohort studies.

An example of a cohort study could be a drug manufacturer studying the effects on a group of users taking a new drug over a period. A drinks company may want to research consumers with common characteristics, like regular purchasers of sugar-free sodas. This will help the company understand trends within its target market.

• Benefits of longitudinal research

If you want to study the relationship between variables and causal factors responsible for certain outcomes, you should adopt a longitudinal approach to your investigation.

The benefits of longitudinal research over other research methods include the following:

Insights over time

It gives insights into how and why certain things change over time.

Better information

Researchers can better establish sequences of events and identify trends.

No recall bias

The participants won't have recall bias if you use a prospective longitudinal study. Recall bias is an error that occurs in a study if respondents don't wholly or accurately recall the details of their actions, attitudes, or behaviors.

Because variables can change during the study, researchers can discover new relationships or data points worth further investigation.

Small groups

Longitudinal studies don't need a large group of participants.

• Potential pitfalls

The challenges and potential pitfalls of longitudinal studies include the following:

A longitudinal survey takes a long time, involves multiple data collections , and requires complex processes, making it more expensive than other research methods.

Unpredictability

Because they take a long time, longitudinal studies are unpredictable. Unexpected events can cause changes in the variables, making earlier data potentially less valuable.

Slow insights

Researchers can take a long time to uncover insights from the study as it involves multiple observations.

Participants can drop out of the study, limiting the data set and making it harder to draw valid conclusions from the results.

Overly specific data

If you study a smaller group to reduce research costs, results will be less generalizable to larger populations versus a study with a larger group.

Despite these potential pitfalls, you can still derive significant value from a well-designed longitudinal study by uncovering long-term patterns and relationships.

• Longitudinal study designs

Longitudinal studies can take three forms: Repeated cross-sectional, prospective, and retrospective.

Repeated cross-sectional studies

Repeated cross-sectional studies are a type of longitudinal study where participants change across sampling periods. For example, as part of a brand awareness survey , you ask different people from the same customer population about their brand preferences. 

Prospective studies

A prospective study is a longitudinal study that involves real-time data collection, and you follow the same participants over a period. Prospective longitudinal studies can be cohort, where participants have similar characteristics or experiences. They can also be panel studies, where you choose the population sample randomly.

Retrospective studies

Retrospective studies are longitudinal studies that involve collecting data on events that some participants have already experienced. Researchers examine historical information to identify patterns that led to an outcome they established at the start of the study. Retrospective studies are the most time and cost-efficient of the three.

• How to perform a longitudinal study

When developing a longitudinal study plan, you must decide whether to collect your data or use data from other sources. Each choice has its benefits and drawbacks.

Using data from other sources

You can freely access data from many previous longitudinal studies, especially studies conducted by governments and research institutes. For example, anyone can access data from the 1970 British Cohort Study on the  UK Data Service website .

Using data from other sources saves the time and money you would have spent gathering data. However, the data is more restrictive than the data you collect yourself. You are limited to the variables the original researcher was investigating, and they may have aggregated the data, obscuring some details.

If you can't find data or longitudinal research that applies to your study, the only option is to collect it yourself.

Collecting your own data

Collecting data enhances its relevance, integrity, reliability, and verifiability. Your data collection methods depend on the type of longitudinal study you want to perform. For example, a retrospective longitudinal study collects historical data, while a prospective longitudinal study collects real-time data.

The only way to ensure relevant and reliable data is to use an effective and versatile data collection tool. It can improve the speed and accuracy of the information you collect.

What is a longitudinal study in research?

A longitudinal study is a research design that involves studying the same variables over time by gathering data continuously or repeatedly at consistent intervals.

What is an example of a longitudinal study?

An excellent example of a longitudinal study is market research to identify market trends. The organization's researchers collect data on customers' likes and dislikes to assess market trends and conditions. An organization can also conduct longitudinal studies after launching a new product to understand customers' perceptions and how it is doing in the market.

Why is it called a longitudinal study?

It’s a longitudinal study because you collect data over an extended period. Longitudinal data tracks the same type of information on the same variables at multiple points in time. You collect the data over repeated observations.

What is a longitudinal study vs. a cross-sectional study?

A longitudinal study follows the same people over an extended period, while a cross-sectional study looks at the characteristics of different people or groups at a given time. Longitudinal studies provide insights over an extended period and can establish patterns among variables.

Cross-sectional studies provide insights about a point in time, so they cannot identify cause-and-effect relationships.

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What is a Longitudinal Study?: Definition and Explanation

In this article, we’ll cover all you need to know about longitudinal research. 

Let’s take a closer look at the defining characteristics of longitudinal studies, review the pros and cons of this type of research, and share some useful longitudinal study examples. 

Content Index

What is a longitudinal study?

Types of longitudinal studies, advantages and disadvantages of conducting longitudinal surveys.

• Longitudinal studies vs. cross-sectional studies

Types of surveys that use a longitudinal study

Longitudinal study examples.

A longitudinal study is a research conducted over an extended period of time. It is mostly used in medical research and other areas like psychology or sociology. 

When using this method, a longitudinal survey can pay off with actionable insights when you have the time to engage in a long-term research project.

Longitudinal studies often use surveys to collect data that is either qualitative or quantitative. Additionally, in a longitudinal study, a survey creator does not interfere with survey participants. Instead, the survey creator distributes questionnaires over time to observe changes in participants, behaviors, or attitudes. 

Many medical studies are longitudinal; researchers note and collect data from the same subjects over what can be many years.

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Longitudinal studies are versatile, repeatable, and able to account for quantitative and qualitative data . Consider the three major types of longitudinal studies for future research:

Panel study: A panel survey involves a sample of people from a more significant population and is conducted at specified intervals for a more extended period. 

One of the panel study’s essential features is that researchers collect data from the same sample at different points in time. Most panel studies are designed for quantitative analysis , though they may also be used to collect qualitative data and unit of analysis .

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Cohort Study: A cohort study samples a cohort (a group of people who typically experience the same event at a given point in time). Medical researchers tend to conduct cohort studies. Some might consider clinical trials similar to cohort studies. 

In cohort studies, researchers merely observe participants without intervention, unlike clinical trials in which participants undergo tests.

Retrospective study: A retrospective study uses already existing data, collected during previously conducted research with similar methodology and variables. 

While doing a retrospective study, the researcher uses an administrative database, pre-existing medical records, or one-to-one interviews.

As we’ve demonstrated, a longitudinal study is useful in science, medicine, and many other fields. There are many reasons why a researcher might want to conduct a longitudinal study. One of the essential reasons is, longitudinal studies give unique insights that many other types of research fail to provide. 

Advantages of longitudinal studies

• Greater validation: For a long-term study to be successful, objectives and rules must be established from the beginning. As it is a long-term study, its authenticity is verified in advance, which makes the results have a high level of validity.
• Unique data: Most research studies collect short-term data to determine the cause and effect of what is being investigated. Longitudinal surveys follow the same principles but the data collection period is different. Long-term relationships cannot be discovered in a short-term investigation, but short-term relationships can be monitored in a long-term investigation.
• Allow identifying trends: Whether in medicine, psychology, or sociology, the long-term design of a longitudinal study enables trends and relationships to be found within the data collected in real time. The previous data can be applied to know future results and have great discoveries.
• Longitudinal surveys are flexible: Although a longitudinal study can be created to study a specific data point, the data collected can show unforeseen patterns or relationships that can be significant. Because this is a long-term study, the researchers have a flexibility that is not possible with other research formats.

Additional data points can be collected to study unexpected findings, allowing changes to be made to the survey based on the approach that is detected.

Disadvantages of longitudinal studies

• Research time The main disadvantage of longitudinal surveys is that long-term research is more likely to give unpredictable results. For example, if the same person is not found to update the study, the research cannot be carried out. It may also take several years before the data begins to produce observable patterns or relationships that can be monitored.
• An unpredictability factor is always present It must be taken into account that the initial sample can be lost over time. Because longitudinal studies involve the same subjects over a long period of time, what happens to them outside of data collection times can influence the data that is collected in the future. Some people may decide to stop participating in the research. Others may not be in the correct demographics for research. If these factors are not included in the initial research design, they could affect the findings that are generated.
• Large samples are needed for the investigation to be meaningful To develop relationships or patterns, a large amount of data must be collected and extracted to generate results.
• Higher costs Without a doubt, the longitudinal survey is more complex and expensive. Being a long-term form of research, the costs of the study will span years or decades, compared to other forms of research that can be completed in a smaller fraction of the time.

Longitudinal studies vs. Cross-sectional studies

Longitudinal studies are often confused with cross-sectional studies. Unlike longitudinal studies, where the research variables can change during a study, a cross-sectional study observes a single instance with all variables remaining the same throughout the study. A longitudinal study may follow up on a cross-sectional study to investigate the relationship between the variables more thoroughly.

The design of the study is highly dependent on the nature of the research questions . Whenever a researcher decides to collect data by surveying their participants, what matters most are the questions that are asked in the survey.

Knowing what information a study should gather is the first step in determining how to conduct the rest of the study. 

With a longitudinal study, you can measure and compare various business and branding aspects by deploying surveys. Some of the classic examples of surveys that researchers can use for longitudinal studies are:

Market trends and brand awareness: Use a market research survey and marketing survey to identify market trends and develop brand awareness. Through these surveys, businesses or organizations can learn what customers want and what they will discard. This study can be carried over time to assess market trends repeatedly, as they are volatile and tend to change constantly.

Product feedback: If a business or brand launches a new product and wants to know how it is faring with consumers, product feedback surveys are a great option. Collect feedback from customers about the product over an extended time. Once you’ve collected the data, it’s time to put that feedback into practice and improve your offerings.

Customer satisfaction: Customer satisfaction surveys help an organization get to know the level of satisfaction or dissatisfaction among its customers. A longitudinal survey can gain feedback from new and regular customers for as long as you’d like to collect it, so it’s useful whether you’re starting a business or hoping to make some improvements to an established brand.

Employee engagement: When you check in regularly over time with a longitudinal survey, you’ll get a big-picture perspective of your company culture. Find out whether employees feel comfortable collaborating with colleagues and gauge their level of motivation at work.

Now that you know the basics of how researchers use longitudinal studies across several disciplines let’s review the following examples:

Example 1: Identical twins

Consider a study conducted to understand the similarities or differences between identical twins who are brought up together versus identical twins who were not. The study observes several variables, but the constant is that all the participants have identical twins.

In this case, researchers would want to observe these participants from childhood to adulthood, to understand how growing up in different environments influences traits, habits, and personality.

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Over many years, researchers can see both sets of twins as they experience life without intervention. Because the participants share the same genes, it is assumed that any differences are due to environmental analysis , but only an attentive study can conclude those assumptions.

Example 2: Violence and video games

A group of researchers is studying whether there is a link between violence and video game usage. They collect a large sample of participants for the study. To reduce the amount of interference with their natural habits, these individuals come from a population that already plays video games. The age group is focused on teenagers (13-19 years old).

The researchers record how prone to violence participants in the sample are at the onset. It creates a baseline for later comparisons. Now the researchers will give a log to each participant to keep track of how much and how frequently they play and how much time they spend playing video games. This study can go on for months or years. During this time, the researcher can compare video game-playing behaviors with violent tendencies. Thus, investigating whether there is a link between violence and video games.

Conducting a longitudinal study with surveys is straightforward and applicable to almost any discipline. With our survey software you can easily start your own survey today. 

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An Overview of the Design, Implementation, and Analyses of Longitudinal Studies on Aging

Anne b. newman.

Center for Aging and Population Health, Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania

Associated Data

Longitudinal studies have contributed substantially to understanding of aging and geriatric syndromes. These efforts have provided a base of knowledge of the critical factors to consider in designing and implementing new longitudinal studies in older adults. This review highlights some of the major considerations in planning and implementing this type of study. Longitudinal studies can assess change over time and specific disease endpoints. Such projects require multidisciplinary teams with expertise in the many health and contextual factors that must be considered. Recent advances in study design include the use of imaging and biomarkers to assess mechanisms and approaches that raise the ceiling on measurement and integrate assessment of exposures over time. Study implementation requires careful planning and monitoring to maintain fidelity to the scientific goals. Analysis of longitudinal data requires approaches that account for inevitable missing data. New studies should take advantage of the experience obtained from longitudinal studies on aging already conducted.

Longitudinal observational studies have played a major role in geriatric research and in defining the scope of many health concerns in older adults, their risk factors, and their natural history. Because all older adults have significant risk for death and disability, studies that include large samples of community-dwelling older adults have provided an important perspective on the scope of the problems facing an aging population. For example, early studies of disability from the Established Populations for the Epidemiologic Study of the Elderly 1 and the National Long Term Care Survey 2 have demonstrated the large burden of difficulty with functioning in daily life after age 65. More-focused efforts on identifying risk factors have shown that disability is multifactorial, and the more-common conditions that should be targeted to prevent disability have been identified. Furthermore, studies that assess risk broadly have illustrated the commonality of risk factors across geriatric syndromes. 3 Focused studies on specific age-related health conditions such as osteoporosis, 4 cardiovascular disease, 5 , 6 stroke, 7 and dementia 8 have not only been able to assess specific biological pathways that lead to adverse outcomes, but have also assessed the role of other conditions in exacerbating these common problems. Other important contributions have focused on social, behavioral, 9 and economic outcomes. 10 Current and future studies on older populations will be designed to better address this complexity by developing life-course approaches that address early changes, precipitants, and earlier stages of disability. 11 , 12

LONGITUDINAL STUDY DESIGN

The design of longitudinal studies on aging should focus on a set of primary questions and hypotheses while taking into account the important contributions of function, comorbid health conditions, and behavioral and environmental factors. By focusing on primary questions and hypotheses, other methodological concerns can be put into perspective, because it is far too costly and burdensome to measure all aspects of health to the same degree as is necessary to address the primary hypotheses. Design concerns can be classified into those addressing the target population, the exposures, the outcomes, and potential confounders. Cost and practicality may limit the degree of precision in measurement, driving questions back to the priorities determined by the primary questions. Thus, remaining focused on the primary study goals is critical for setting priorities.

To ensure the best design and ultimate productivity, the study’s scientific and administrative leader should assemble a team of investigators and staff who have the skills to contribute to successful design and implementation. This includes content experts in relevant diseases, disability, and aging processes important to the scientific questions, as well as methodological experts in sampling, measurement, and biostatistics. The administrative team must have expertise in budget, environment, and human resource issues. Staff need not have prior medical training but should be detail oriented and dedicated to maintaining fidelity to protocol.

The design of a longitudinal study will vary depending on whether the primary goal is to study changes over time or discrete outcomes. Changes over time generally require frequent contacts. Some outcomes such as stroke or cancer can be assessed using record review, whereas dementia requires in-person examinations. Generalizability needs to be weighed against maintaining follow-up, and these are often competing goals. The requirement for an extensive evaluation and years of follow-up can reduce participation rates. Tiered designs can be used to collect screening data to assess representativeness, with more-intensive data collection from a smaller sample. The internal validity of within-person analysis strengthens longitudinal designs. The sample size needed should be based on power calculations for the primary outcomes of interest and thus vary with the outcome rate. Many health outcomes occur at rate of a few percentage points per year in this age group. Thus, sample sizes of several thousand are often needed to have enough events to study within a reasonable time frame.

The target population will vary with the questions being asked. A fundamental question is what age is most appropriate? Today, 65-year-old people are generally healthier than ever and have low rates of disability and most major health events. Several studies have moved to age 70 to better target the problems of aging. 13 , 14 Conversely, interest in the origins of aging requires targeting earlier ages. For example, the Study of Women’s Health Across the Nation targeted women in the perimenopausal period to understand the role of hormonal change in early age-related processes. 15 , 16 Inclusion and exclusion criteria depend on the outcomes and the outcomes measurement criteria. For example, a study of mobility disability 17 excluded individuals using a cane to walk 400 m at baseline, because inability to walk independently was to be the primary study outcome. Recruitment of individuals within the full spectrum of health, including the frailest, 18 will increase generalizability, although having a large number of participants who have already experienced the outcome, reducing power for incidence studies, may offset this strategy. Careful consideration of the level of cognitive function required for participation can dictate the exclusion criteria for dementia. Regardless of the current level of cognitive function, all longitudinal studies of older adults should identify a potential proxy respondent in case of future compromised cognition of the participant. 19 Studies in the United States need to consider the diversity of the target population and whether to overrecruit subgroups to have adequate power within the groups. In most cases, additional resources are needed to reach minority populations. 20

Ethical concerns in longitudinal studies of older adults warrant special attention. Methods must be in place to establish competency for informed consent 21 and procedures put into place if proxy consent is needed at baseline (for studies of cognitively impaired individuals) or is anticipated for the future, which is the case for most long-term follow-up studies. Results from laboratory testing should be reviewed and reported to subjects if clinically significant findings are identified. Procedures should be designed for, and staff should be trained to attend to, patient safety in the use of any diagnostic or other testing equipment being employed in the study.

Longitudinal observational studies are often designed to assess multiple outcomes. Although it is important and efficient to assess more than a primary outcome, the choice of outcomes should be based on the primary hypothesis, and the study should be powered to address the most important questions. This will, in turn, dictate the sample size to be recruited, which is the major driving factor of the study’s cost and feasibility. Nevertheless, there is a huge scientific advantage to assessing other outcomes, in that risk–benefit ratios can be evaluated. The relationship between multiple health events can be assessed together to determine relative importance and contributions to individual-and societal-level outcomes, such as disability and healthcare utilization. For example the Women’s Health Initiative, 22 by including the observational component, was able to assess breast cancer, fracture, cardiovascular events, and cognitive decline because of the sample size recruited. 23 Together, the findings provide a rich picture of the role that these major conditions play in the functional health of older women. 23 - 26 Linkage of cohort data to the National Death Index, Medicare Beneficiary files, the Minimal Data Set, and other public use files can greatly expand opportunities for outcomes assessment. 27 - 32

Disability is an important outcome that has been assessed using a variety of methods, including self-report, professional assessments, and performance-based measures, such as gait speed and timed tests of specific tasks. Important observations regarding the natural history of exacerbations and remission in these outcomes has led to refinements in defining disability outcomes, such as requiring persistence over time, 14 task modification, 33 , 34 and direct assessment 35 with performance measures. These methods continue to evolve, and there is no consensus on a single approach to define disability outcomes. Recent studies on healthy aging outcomes have shown that there is tremendous variation in functioning that is well above the level designated as disabled. To capture the full spectrum of function and to detect early decline, the study designers should consider using instruments designed to capture a full range of function, including normal, high, and exceptional levels. 36

Exposures of interest should be considered together with the design of the outcomes. Major risk factors are usually identified from the literature or hypothesized from new information on etiology. Behavioral and biological factors should be considered. For example, studies of outcomes of vitamin D exposure should assess diet, sun exposure and season, and blood levels of vitamin D and diseases that can affect its metabolism. 37 Medications can be part of the exposure assessment, in that many medications can alter the primary risk factor being assessed. Examples include vitamin supplements and vitamin levels, lipid levels in the era of statin use, and blood pressure in light of antihypertensive use. Although “baseline” exposure assessments are usually conducted, increasing attention is being paid to including more of a life-course perspective and incorporating historical exposure information from self-report 38 or from other sources, such as geocoding. 39 , 40 Efforts to continue long-term follow-up of younger populations will provide the best estimates of life-course exposures in old age. 41 - 43

Potential confounders that should be considered are so numerous that they can greatly expand the cost and burden of studies in older adults. It is important first to rank all measures according to their role as primary outcomes or exposures so that potential confounders do not overtake resources. Most studies of older adults include measures of common psychosocial factors that can influence function, such as depression, social support, and cognition. Education and smoking history are risk factors for almost every adverse health outcome and should always be included. Age itself is usually assessed according to self-report, but studies of longevity show the importance of more-careful assessment and validation of even this apparently simple confounder. 44 Finally, medication, even if not related directly to the exposure or outcome, can be important to assess as a potential confounder, but collection of information on medications requires special expertise to code them in a way that is useful and accessible for analysis. 45 - 47

Blood laboratory testing is often a major component of a longitudinal study. Most large cohort studies have invested in setting up banks of stored serum, plasma, cells, and deoxyribonucleic acid. Blood tests can be used to define clinical health status, as in determining fasting blood sugar to classify diabetes mellitus, but have been most valuable for allowing for later evaluation of important biomarkers and for genetic testing. As novel markers emerge, stored specimens can be analyzed in a cost-effective case–cohort design.

STUDY IMPLEMENTATION

Once a study is designed, numerous procedures must be put into place to ensure that the data are collected with fidelity to the scientific goals. These steps can take weeks to months or even longer. In multicenter observational cohort studies, it is typical to spend a year or more developing and beginning to implement the study design before actually launching the study. This planning phase should include finalizing the protocol and writing a manual of operations and procedures. All data collection forms should be pretested before the data entry systems are designed, and the system for entry should be in place before the study begins. Time is also needed to hire and train staff, to lay the groundwork for recruitment, and to be sure that the institutional review board has addressed and approved all human subject concerns.

Fielding a study that is to be conducted over the long term requires special attention to measurement. The scientific rationale of each measure, including its role as an outcome, mediator, or potential confounder, should be spelled out in the operations manual. Reproducibility, even if documented in the literature, should be tested in the specific cohort and setting, especially for longitudinal studies that include measurement of change over time. Measurement error can bias associations with change over time, and this analytical concern can be mitigated with adjustment for measurement error per se. 48 All measures should be pretested individually and as a package to work out the study flow. Regular tracking of major measurements through study logs, with commentary and review of all procedures at regular staff meetings, is critical for the identification of potential problems and solutions. Manuals should be revised as needed and staff retrained and certified at least annually.

Data entry can go smoothly when forms are well designed, when staff members complete them without error, and when reports are set up and reviewed regularly for quality control. Keeping up with data entry and running quality checks daily will avoid future recalls and reduce edits. Backlogs of data entry make it more difficult to identify and correct errors in form completion. Real-time data entry and edits are more feasible with software programs that build in range checks and logic.

A successful longitudinal study is proactive in retaining participants. Numerous aspects of study operation lead to successful retention. Suggested methods include keeping to the requested study visit date and duration as agreed to during enrollment and respecting participants’ time. An exit interview should be conducted with every participant to explain follow-up plans and expectations. Regular contacts for follow-up, newsletters, and birthday and holiday cards maintain the relationship between participants and staff. Finally, it is critical that alternative methods be provided to obtain follow-up, including telephone methods, 49 home and nursing home visits, and proxy interviews. 50 As older adults become more impaired, there is inevitable dropout from full participation. Alternative methods that include home visits, telephone interviews, and proxy interviews can lead to high levels of retention for major morbidity and mortality. 50

DATA ANALYSIS

Once the data are in hand, numerous analytical concerns will arise. Missing data are “a given” in longitudinal studies of older adults because of unanticipated illness and death. Methods should be in place to ensure that this is kept to a minimum. 51 Other analytical concerns in longitudinal studies include measurement error; protocol drift over time; migration in equipment specifications and software that affect estimates of change over time; determining changes that are nonlinear, with curvilinear or threshold effects; and substantial biological variability over time. These should be considered in the study design. Analysis of an outcome might be enhanced if time-dependent covariates can be considered. This level of detail of data also needs to be in place in the study design. The analysis should take into account the previously discussed matters of variability and fluctuations over time.

Given the many challenges of conducting longitudinal studies in older adults, it may seem impossible to do it well; such studies are challenging. Successful studies require leadership, teamwork, and excellent communication. Ultimately, prioritizing the primary focus of each study and applying the best science will optimize success. Lessons learned from previous and ongoing longitudinal studies outlined in this review should be helpful in the design of future longitudinal studies.

Supplementary Material

Supplementary 1, acknowledgments.

Sponsor’s Role: None.

Author Contributions: Dr. Newman was the sole author of the manuscript.

Conflict of Interest: Dr. Newman is supported by Grant AG-023629 from the National Institute on Aging (NIA).

Longitudinal Research Designs

Cite this chapter.

• Elizabeth Piper Deschenes  

648 Accesses

4 Citations

The major goals and objectives of longitudinal research are the following: to quantify trends in human behavior, to describe the progression of life events, to identify patterns of behavioral change, to test theory, and to justify interventions to prevent human and societal ills. The primary use of longitudinal research has been to study the development and natural history of events in the life course. This type of design is often regarded as superior to a cross-sectional design because it enables processes and causes of change within individuals and among individuals to be identified. Longitudinal research is useful in testing theory because it allows the examination of causal hypotheses. For example, the researcher can examine the relationship between school failure and delinquency rates and determine if those who fail are more likely to be delinquent than those who succeed in school. Longitudinal research is also useful in describing the progression of life events, such as the effect of marriage or becoming unemployed on offending. Does unemployment lead to an increase in criminal behavior? Is there a greater likelihood of desistance from crime after one is married? These questions are just two of the many that are best answered with a longitudinal design.

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Longitudinal Qualitative Research

Longitudinal Research

Longitudinal Study Designs

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Deschenes, E.P. (1990). Longitudinal Research Designs. In: Kempf, K.L. (eds) Measurement Issues in Criminology. Springer, New York, NY. https://doi.org/10.1007/978-1-4613-9009-1_7

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Frequently asked questions

What is the difference between a longitudinal study and a cross-sectional 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.

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.

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 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.

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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What is a Longitudinal Study? Definition, Types & Examples

Kate Williams

Last Updated: 16 February 2024

12 min read

Table Of Contents

What is a Longitudinal Study?

• Types of Longitudinal Studies?

Pros and Cons of Longitudinal Research Design

Examples of longitudinal surveys.

Sonia was conflicted. A few months ago, a survey from a grocery delivery app had asked her if she preferred normal eggs or the free-range ones.

She was financially stressed and couldn’t afford to pay more for free-range eggs, so she picked the normal ones.

But last night, she had watched a popular documentary on Netflix about how hens were treated in cages and now felt much more strongly about wanting to buy free-range eggs.

There was no way for Sonia to communicate this new preference to her grocery delivery app.

But that’s the thing about consumer trends. They are constantly shifting, and one survey taken years ago is not going to give you an accurate picture of the shifts in trends.

That’s why your business needs to understand what a longitudinal study is.

At times, a one-off survey simply isn’t enough to give you the data you need. If you need to observe certain trends, behaviors, or preferences over time, you can use a longitudinal study.

The simplest way to understand what is a longitudinal study is to think of it as a survey taken over time. The passing of time could influence the responses of the same person to the same question. Like Sonia, her preferences for eggs changed since she watched the documentary. That’s the kind of thing that longitudinal research design measures.

As for a formal definition, a longitudinal study is a research method that involves repeated observations of the same variable (e.g. a set of people) over some time. The observations over a period of time might be undertaken in the form of an online survey. It can be tremendously useful in a variety of fields to be able to observe behavior or trends over time.

Longitudinal studies are used in fields like:

• Clinical psychology to measure a patient’s thoughts over time
• Market research to observe consumer trends
• Political polling and sociology, observing life events and societal shifts over time
• Longitudinal research design is also used in medicine to discover predictors of certain diseases

We are dealing with nuanced changes over time here, and surveys excel at capturing these shifts in attitudes, behaviors, and experiences. Unlike one-time snapshots, surveys repeated over time enable you to track trends and understand how variables evolve. Plus, it is cost-effective and flexible in terms of reach!

For instance, SurveySparrow’s Recurring Surveys let you schedule and automate the entire process.

With this feature, you can share periodic surveys at any frequency that you set. Also, give a slight nudge to those silent respondents over a friendly reminder via email. The best part? The platform’s conversational surveys reap a higher response rate.

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Types of Longitudinal Studies

When talking about what is a longitudinal study, we cannot go without also discussing the types of longitudinal research design. There are different studies based on your needs. When you understand all three types of longitudinal studies, you’ll be able to pick out the one that’s best suited to your needs.

Panel Study

When we want to find out trends in a larger population, we often use a sample size to survey. A panel study simply observes that sample size over time. By doing so, panel studies can identify cultural shifts and new trends in a larger population.

Panel studies are designed for quantitative analysis. Through the data from online surveys, you can identify common patterns in the responses from your sample (which remain the same over time). A comprehensive dashboard will help you make informed decisions.

But what’s the need to visualize?

In panel studies, the same set of people must be studied over time. If you pick a different sample, variations in individual preferences could skew your results.

Observing the same set of people can make sure that what you’re observing is a change over time. Visualizing the change over time will give you a clear idea of the trends and patterns, resulting in informed and effective decision-making.

Cohort Study

A longitudinal cohort study is one in which we study people who share a single characteristic over a period of time. Cohort studies are regularly conducted by medical researchers to ascertain the effects of a new drug or the symptoms of a disease.

In cohort studies, the behaviors of the selected group of people are observed over time to find patterns and trends. Often, these studies can go on for years. They can also be particularly useful for ascertaining consumer trends if you’re trying to research consumers with a specific common characteristic. An example of such a study would be observing the choice of cereal for kids who go to Sunshine Elementary School over time.

If you’re confused between panel studies and cohort studies, don’t worry. The one key difference between cohort studies and panel studies is that the same set of people has to be observed in the latter. In cohort studies, you can pick a different sample of the same demographic to study over time.

Retrospective Study

A retrospective longitudinal study is when you take pre-existing data from previous online surveys and other research. The objective here is to put your results in a larger timeline and observe the variation in results over time. What makes retrospective studies longitudinal is simply the fact that they’re aimed at revealing trends over time.

When understanding what is a longitudinal study, it’ll be well worth your while to look into retrospective studies. For your company, retrospective longitudinal studies can reveal crucial insights without you having to spend a single dime. Since these studies depend on existing data, they not only don’t cost much themselves but also improve the returns from your earlier research efforts.

How can retrospective longitudinal studies be useful to you? Let’s assume, for example, that you conduct an employee engagement survey every year. If your organization has done these surveys for the past 10 years, you now have more than enough material to conduct a retrospective study. You can then find out how employee engagement at your company has varied over time.

Like with every research method , longitudinal studies have their advantages and disadvantages. While trying to understand what is a longitudinal study, it is important to get the particular ways in which they’re useful, and situations in which they’re not. Let’s go over some of the major pros and cons of longitudinal surveys.

Advantages of Longitudinal Studies

• Rigorous Insights : A one-off online survey, no matter how well designed, is only so rigorous. Even though the results are often useful, sometimes you need more rigor in your surveys. A longitudinal survey, by observing respondents over time, can offer more rigorous results.
• Long-term Data : When thinking about what is a longitudinal study, it is crucial to understand that it is best used for a specific type of data collection. When you need to understand trends over the longer term, longitudinal studies are best suited to that task.
• Discover Trends : Most companies, in one way or another, rely on trends they estimate will be relevant in the future. Longitudinal studies can be great at finding out those trends and capitalizing on them before the competition.
• Open To Surprises : When designing an online survey, it is very tough to allow for surprises. Mostly, you get what you ask for. With longitudinal surveys, you’re allowing for the possibility that you might spot patterns you didn’t imagine could exist. Longitudinal studies are more flexible in that regard and allow us to discover the unexpected.

Disadvantages of Longitudinal Studies

• Higher Costs : Because longitudinal research needs to be conducted over time, and in some cases with the same set of people, they end up being costlier than one-off surveys. From conducting the observations to analyzing the data, it can add up financially. Using a cost-effective online survey tool like Surveysparrow can be one way to reduce costs.
• More Demanding : One of the biggest challenges you can face while conducting a survey is to get enough respondents. Even for normal online surveys, it can be tough to get people to take your survey. Longitudinal surveys are far more demanding, so it is unlikely that anyone will participate without strong incentives.
• Unpredictability : While unpredictability can sometimes be a good thing, at times it can also lead the whole exercise astray. The success of a longitudinal study depends not just on the resources you invest in it, but also on the respondents who have to participate in a long-term commitment. Things can go wrong when respondents are suddenly unavailable. That’s why there’s always an element of unpredictability with longitudinal surveys.
• Time-Consuming : Unlike simple online surveys, you don’t get the results instantly with longitudinal surveys. They require a certain vision, and you have to be patient enough to see it through to get your desired results.

Longitudinal surveys have been used by researchers and businesses for a long time now, so there is no dearth of examples. Let’s walk through a few of them so you can better understand what is a longitudinal survey.

Australia’s ‘45 and Up’ Survey

There is no better example to understand what longitudinal research is than the 45 and Up study being conducted in Australia. It aims to understand healthy aging and has 250,000 participants who are aged 45 or older. The idea is to get a better idea of Australians’ health as they age.

Such a study needed to be a longitudinal survey since you can only understand the effects of aging en masse by considering the results over time. The results from this study are being used in areas like cardiovascular research and preventable hospitalizations.

Smoking and Lung Cancer

To understand the effects of smoking, you need to be able to assess its consequences over time. The British Doctors Study, which ran from 1951 to 2001, yielded results that strongly indicated the link between smoking and lung cancer. If not for longitudinal research methods, we might never have known.

Even though the research was first published in 1956, the study went on for almost half a century after that. When thinking about what is a longitudinal study, we must also consider that these studies give results while they’re ongoing. Conclusively proving the link between smoking and cancer required a robust, longitudinal survey.

Growing Up In Ireland

Started in 2006, Growing Up In Ireland is a longitudinal study conducted by the Irish government to understand what children’s life looks like in different age brackets. One cohort that the study started following at 9 years of age is now 23. The long-term study can yield interesting results by following a set of children throughout their childhood.

The thing to remember when thinking about what a longitudinal study is is that they can have broad objectives. You can go in without really knowing what you’re trying to find and what that might lead to. You can then use the surprises along the way to generate actionable insights.

Wrapping Up

If you started out wondering what is a longitudinal study, we hope that we’ve addressed that question and more in this article. If you want to create a longitudinal survey, don’t forget to first plan out your survey. A retrospective study, like we just talked about, can also be a great solution to your problems.

Here at SurveySparrow, we love surveys of all kinds. For certain types of questions, you need to conduct longitudinal surveys, and we’re here to support you through the process. With our online templates and intuitive UI, conducting a longitudinal survey will be much easier.

What we love about recurring surveys is the surprising results they can yield. That is really what drives us at Surveysparrow, that you might find something in the results you didn’t expect, and it might change the course of your company for the better.

Take it for a spin and see for yourself. It won’t even cost you a penny!

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Product Marketing Manager at SurveySparrow

Excels in empowering visionary companies through storytelling and strategic go-to-market planning. With extensive experience in product marketing and customer experience management, she is an accomplished author, podcast host, and mentor, sharing her expertise across diverse platforms and audiences.

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An introduction to different types of study design

Posted on 6th April 2021 by Hadi Abbas

Study designs are the set of methods and procedures used to collect and analyze data in a study.

Broadly speaking, there are 2 types of study designs: descriptive studies and analytical studies.

Descriptive studies

• Describes specific characteristics in a population of interest
• The most common forms are case reports and case series
• In a case report, we discuss our experience with the patient’s symptoms, signs, diagnosis, and treatment
• In a case series, several patients with similar experiences are grouped.

Analytical Studies

Analytical studies are of 2 types: observational and experimental.

Observational studies are studies that we conduct without any intervention or experiment. In those studies, we purely observe the outcomes.  On the other hand, in experimental studies, we conduct experiments and interventions.

Observational studies

Observational studies include many subtypes. Below, I will discuss the most common designs.

Cross-sectional study:

• This design is transverse where we take a specific sample at a specific time without any follow-up
• It allows us to calculate the frequency of disease ( p revalence ) or the frequency of a risk factor
• This design is easy to conduct
• For example – if we want to know the prevalence of migraine in a population, we can conduct a cross-sectional study whereby we take a sample from the population and calculate the number of patients with migraine headaches.

Cohort study:

• We conduct this study by comparing two samples from the population: one sample with a risk factor while the other lacks this risk factor
• It shows us the risk of developing the disease in individuals with the risk factor compared to those without the risk factor ( RR = relative risk )
• Prospective : we follow the individuals in the future to know who will develop the disease
• Retrospective : we look to the past to know who developed the disease (e.g. using medical records)
• This design is the strongest among the observational studies
• For example – to find out the relative risk of developing chronic obstructive pulmonary disease (COPD) among smokers, we take a sample including smokers and non-smokers. Then, we calculate the number of individuals with COPD among both.

Case-Control Study:

• We conduct this study by comparing 2 groups: one group with the disease (cases) and another group without the disease (controls)
• This design is always retrospective
•  We aim to find out the odds of having a risk factor or an exposure if an individual has a specific disease (Odds ratio)
•  Relatively easy to conduct
• For example – we want to study the odds of being a smoker among hypertensive patients compared to normotensive ones. To do so, we choose a group of patients diagnosed with hypertension and another group that serves as the control (normal blood pressure). Then we study their smoking history to find out if there is a correlation.

Experimental Studies

• Also known as interventional studies
• Can involve animals and humans
• Pre-clinical trials involve animals
• Clinical trials are experimental studies involving humans
• In clinical trials, we study the effect of an intervention compared to another intervention or placebo. As an example, I have listed the four phases of a drug trial:

I:  We aim to assess the safety of the drug ( is it safe ? )

II: We aim to assess the efficacy of the drug ( does it work ? )

III: We want to know if this drug is better than the old treatment ( is it better ? )

IV: We follow-up to detect long-term side effects ( can it stay in the market ? )

• In randomized controlled trials, one group of participants receives the control, while the other receives the tested drug/intervention. Those studies are the best way to evaluate the efficacy of a treatment.

Finally, the figure below will help you with your understanding of different types of study designs.

References (pdf)

You may also be interested in the following blogs for further reading:

An introduction to randomized controlled trials

Case-control and cohort studies: a brief overview

Cohort studies: prospective and retrospective designs

Prevalence vs Incidence: what is the difference?

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No Comments on An introduction to different types of study design

you are amazing one!! if I get you I’m working with you! I’m student from Ethiopian higher education. health sciences student

Very informative and easy understandable

You are my kind of doctor. Do not lose sight of your objective.

Wow very erll explained and easy to understand

I’m Khamisu Habibu community health officer student from Abubakar Tafawa Balewa university teaching hospital Bauchi, Nigeria, I really appreciate your write up and you have make it clear for the learner. thank you

well understood,thank you so much

Well understood…thanks

Simply explained. Thank You.

Thanks a lot for this nice informative article which help me to understand different study designs that I felt difficult before

That’s lovely to hear, Mona, thank you for letting the author know how useful this was. If there are any other particular topics you think would be useful to you, and are not already on the website, please do let us know.

it is very informative and useful.

thank you statistician

Fabulous to hear, thank you John.

Thanks for this information

Thanks so much for this information….I have clearly known the types of study design Thanks

That’s so good to hear, Mirembe, thank you for letting the author know.

Very helpful article!! U have simplified everything for easy understanding

I’m a health science major currently taking statistics for health care workers…this is a challenging class…thanks for the simified feedback.

That’s good to hear this has helped you. Hopefully you will find some of the other blogs useful too. If you see any topics that are missing from the website, please do let us know!

Hello. I liked your presentation, the fact that you ranked them clearly is very helpful to understand for people like me who is a novelist researcher. However, I was expecting to read much more about the Experimental studies. So please direct me if you already have or will one day. Thank you

Dear Ay. My sincere apologies for not responding to your comment sooner. You may find it useful to filter the blogs by the topic of ‘Study design and research methods’ – here is a link to that filter: https://s4be.cochrane.org/blog/topic/study-design/ This will cover more detail about experimental studies. Or have a look on our library page for further resources there – you’ll find that on the ‘Resources’ drop down from the home page.

However, if there are specific things you feel you would like to learn about experimental studies, that are missing from the website, it would be great if you could let me know too. Thank you, and best of luck. Emma

Great job Mr Hadi. I advise you to prepare and study for the Australian Medical Board Exams as soon as you finish your undergrad study in Lebanon. Good luck and hope we can meet sometime in the future. Regards ;)

You have give a good explaination of what am looking for. However, references am not sure of where to get them from.

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• Chapter 7. Longitudinal studies

Clinical follow up studies

• Chapter 1. What is epidemiology?
• Chapter 2. Quantifying disease in populations
• Chapter 3. Comparing disease rates
• Chapter 4. Measurement error and bias
• Chapter 5. Planning and conducting a survey
• Chapter 6. Ecological studies
• Chapter 8. Case-control and cross sectional studies
• Chapter 9. Experimental studies
• Chapter 10. Screening
• Chapter 11. Outbreaks of disease
• Chapter 12. Reading epidemiological reports
• Chapter 13. Further reading

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