quantitative research characteristics objective

Quantitative Research Methods: Meaning and Characteristics

What are quantitative research methods? What is its definition, when are these research methods used, and what are its characteristics?

Table of Contents

When to use quantitative or qualitative research.

The methods used by researchers may either be quantitative or qualitative . The decision to select the method largely depends on the researcher’s judgment and the nature of the research topic . Some research topics are better studied using quantitative methods, while others are more appropriately explored using qualitative methods.

J. Pizarro has already described qualitative research in this site, so this article focuses on quantitative methods, its meaning and characteristics.

What are quantitative research methods?

The numbers used in statistical analysis originate from objective scales of measurement of the units of analysis called variables . Four types of measurement scale exist namely nominal, ordinal, ratio, and interval (see 4 Statistical Scales of Measurement ).

The data that will serve as the basis for explaining a phenomenon, therefore, can be gathered through surveys . Such surveys use instruments that require numerical inputs or direct measurements of parameters that characterize the subject of investigation (e.g. pH, dissolved oxygen, salinity, turbidity, and conductivity to measure water quality).

7 Characteristics of Quantitative Research Methods

Seven characteristics discriminate qualitative methods of research from qualitative ones. I enumerate the characteristics of quantitative research methods in the following list.

1. Contain Measurable Variables

2. use standardized research instruments, 3. assume a normal population distribution.

For more reliable data analysis of quantitative data, a normal population distribution curve is preferred over a non-normal distribution. This requires a large population, the numbers of which depend on how the characteristics of the population vary. This requires adherence to the principle of random sampling to avoid researcher bias in interpreting the results that defeat the purpose of the research.

4. Present Data in Tables, Graphs, or Figures

5. use repeatable method.

Researchers can repeat the quantitative method to verify or confirm the findings in another setting. This reinforces the validity of groundbreaking discoveries or findings, thus eliminating the possibility of spurious or erroneous conclusions.

6. Can Predict Outcomes

Quantitative models or formula derived from data analysis can predict outcomes. If-then scenarios can be constructed using complex mathematical computations with the aid of digital computers or computer-controlled robots commonly referred to as artificial intelligence or AI.

7. Use Measuring Devices

The characteristics of quantitative research methods listed in this article make this research approach popular among researchers. Using qualitative research methods, however, is appropriate on issues or problems that need not require quantification or exploratory in nature .

University of Southern California (2015). Quantitative methods. Retrieved on 3 January, 2015 from http://goo.gl/GMiwt

© 2015 January 3 P. A. Regoniel updated : 2020 October 26

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5 qualities of a good researcher, 5 time management strategies for researchers, how to write a scientific paper: 8 elements, about the author, patrick regoniel.

Dr. Regoniel, a hobbyist writer, served as consultant to various environmental research and development projects covering issues and concerns on climate change, coral reef resources and management, economic valuation of environmental and natural resources, mining, and waste management and pollution. He has extensive experience on applied statistics, systems modelling and analysis, an avid practitioner of LaTeX, and a multidisciplinary web developer. He leverages pioneering AI-powered content creation tools to produce unique and comprehensive articles in this website.

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

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Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques . Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Muijs, Daniel. Doing Quantitative Research in Education with SPSS . 2nd edition. London: SAGE Publications, 2010.

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Characteristics of Quantitative Research

Your goal in conducting quantitative research study is to determine the relationship between one thing [an independent variable] and another [a dependent or outcome variable] within a population. Quantitative research designs are either descriptive [subjects usually measured once] or experimental [subjects measured before and after a treatment]. A descriptive study establishes only associations between variables; an experimental study establishes causality.

Quantitative research deals in numbers, logic, and an objective stance. Quantitative research focuses on numeric and unchanging data and detailed, convergent reasoning rather than divergent reasoning [i.e., the generation of a variety of ideas about a research problem in a spontaneous, free-flowing manner].

Its main characteristics are :

• The data is usually gathered using structured research instruments.
• The results are based on larger sample sizes that are representative of the population.
• The research study can usually be replicated or repeated, given its high reliability.
• Researcher has a clearly defined research question to which objective answers are sought.
• All aspects of the study are carefully designed before data is collected.
• Data are in the form of numbers and statistics, often arranged in tables, charts, figures, or other non-textual forms.
• Project can be used to generalize concepts more widely, predict future results, or investigate causal relationships.
• Researcher uses tools, such as questionnaires or computer software, to collect numerical data.

The overarching aim of a quantitative research study is to classify features, count them, and construct statistical models in an attempt to explain what is observed.

  Things to keep in mind when reporting the results of a study using quantitative methods :

• Explain the data collected and their statistical treatment as well as all relevant results in relation to the research problem you are investigating. Interpretation of results is not appropriate in this section.
• Report unanticipated events that occurred during your data collection. Explain how the actual analysis differs from the planned analysis. Explain your handling of missing data and why any missing data does not undermine the validity of your analysis.
• Explain the techniques you used to "clean" your data set.
• Choose a minimally sufficient statistical procedure ; provide a rationale for its use and a reference for it. Specify any computer programs used.
• Describe the assumptions for each procedure and the steps you took to ensure that they were not violated.
• When using inferential statistics , provide the descriptive statistics, confidence intervals, and sample sizes for each variable as well as the value of the test statistic, its direction, the degrees of freedom, and the significance level [report the actual p value].
• Avoid inferring causality , particularly in nonrandomized designs or without further experimentation.
• Use tables to provide exact values ; use figures to convey global effects. Keep figures small in size; include graphic representations of confidence intervals whenever possible.
• Always tell the reader what to look for in tables and figures .

NOTE:   When using pre-existing statistical data gathered and made available by anyone other than yourself [e.g., government agency], you still must report on the methods that were used to gather the data and describe any missing data that exists and, if there is any, provide a clear explanation why the missing data does not undermine the validity of your final analysis.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Quantitative Research Methods. Writing@CSU. Colorado State University; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Basic Research Design for Quantitative Studies

Before designing a quantitative research study, you must decide whether it will be descriptive or experimental because this will dictate how you gather, analyze, and interpret the results. A descriptive study is governed by the following rules: subjects are generally measured once; the intention is to only establish associations between variables; and, the study may include a sample population of hundreds or thousands of subjects to ensure that a valid estimate of a generalized relationship between variables has been obtained. An experimental design includes subjects measured before and after a particular treatment, the sample population may be very small and purposefully chosen, and it is intended to establish causality between variables. Introduction The introduction to a quantitative study is usually written in the present tense and from the third person point of view. It covers the following information:

• Identifies the research problem -- as with any academic study, you must state clearly and concisely the research problem being investigated.
• Reviews the literature -- review scholarship on the topic, synthesizing key themes and, if necessary, noting studies that have used similar methods of inquiry and analysis. Note where key gaps exist and how your study helps to fill these gaps or clarifies existing knowledge.
• Describes the theoretical framework -- provide an outline of the theory or hypothesis underpinning your study. If necessary, define unfamiliar or complex terms, concepts, or ideas and provide the appropriate background information to place the research problem in proper context [e.g., historical, cultural, economic, etc.].

Methodology The methods section of a quantitative study should describe how each objective of your study will be achieved. Be sure to provide enough detail to enable the reader can make an informed assessment of the methods being used to obtain results associated with the research problem. The methods section should be presented in the past tense.

• Study population and sampling -- where did the data come from; how robust is it; note where gaps exist or what was excluded. Note the procedures used for their selection;
• Data collection – describe the tools and methods used to collect information and identify the variables being measured; describe the methods used to obtain the data; and, note if the data was pre-existing [i.e., government data] or you gathered it yourself. If you gathered it yourself, describe what type of instrument you used and why. Note that no data set is perfect--describe any limitations in methods of gathering data.
• Data analysis -- describe the procedures for processing and analyzing the data. If appropriate, describe the specific instruments of analysis used to study each research objective, including mathematical techniques and the type of computer software used to manipulate the data.

Results The finding of your study should be written objectively and in a succinct and precise format. In quantitative studies, it is common to use graphs, tables, charts, and other non-textual elements to help the reader understand the data. Make sure that non-textual elements do not stand in isolation from the text but are being used to supplement the overall description of the results and to help clarify key points being made. Further information about how to effectively present data using charts and graphs can be found here .

• Statistical analysis -- how did you analyze the data? What were the key findings from the data? The findings should be present in a logical, sequential order. Describe but do not interpret these trends or negative results; save that for the discussion section. The results should be presented in the past tense.

Discussion Discussions should be analytic, logical, and comprehensive. The discussion should meld together your findings in relation to those identified in the literature review, and placed within the context of the theoretical framework underpinning the study. The discussion should be presented in the present tense.

• Interpretation of results -- reiterate the research problem being investigated and compare and contrast the findings with the research questions underlying the study. Did they affirm predicted outcomes or did the data refute it?
• Description of trends, comparison of groups, or relationships among variables -- describe any trends that emerged from your analysis and explain all unanticipated and statistical insignificant findings.
• Discussion of implications – what is the meaning of your results? Highlight key findings based on the overall results and note findings that you believe are important. How have the results helped fill gaps in understanding the research problem?
• Limitations -- describe any limitations or unavoidable bias in your study and, if necessary, note why these limitations did not inhibit effective interpretation of the results.

Conclusion End your study by to summarizing the topic and provide a final comment and assessment of the study.

• Summary of findings – synthesize the answers to your research questions. Do not report any statistical data here; just provide a narrative summary of the key findings and describe what was learned that you did not know before conducting the study.
• Recommendations – if appropriate to the aim of the assignment, tie key findings with policy recommendations or actions to be taken in practice.
• Future research – note the need for future research linked to your study’s limitations or to any remaining gaps in the literature that were not addressed in your study.

Black, Thomas R. Doing Quantitative Research in the Social Sciences: An Integrated Approach to Research Design, Measurement and Statistics . London: Sage, 1999; Gay,L. R. and Peter Airasain. Educational Research: Competencies for Analysis and Applications . 7th edition. Upper Saddle River, NJ: Merril Prentice Hall, 2003; Hector, Anestine. An Overview of Quantitative Research in Composition and TESOL . Department of English, Indiana University of Pennsylvania; Hopkins, Will G. “Quantitative Research Design.” Sportscience 4, 1 (2000); "A Strategy for Writing Up Research Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper." Department of Biology. Bates College; Nenty, H. Johnson. "Writing a Quantitative Research Thesis." International Journal of Educational Science 1 (2009): 19-32; Ouyang, Ronghua (John). Basic Inquiry of Quantitative Research . Kennesaw State University.

Strengths of Using Quantitative Methods

Quantitative researchers try to recognize and isolate specific variables contained within the study framework, seek correlation, relationships and causality, and attempt to control the environment in which the data is collected to avoid the risk of variables, other than the one being studied, accounting for the relationships identified.

Among the specific strengths of using quantitative methods to study social science research problems:

• Allows for a broader study, involving a greater number of subjects, and enhancing the generalization of the results;
• Allows for greater objectivity and accuracy of results. Generally, quantitative methods are designed to provide summaries of data that support generalizations about the phenomenon under study. In order to accomplish this, quantitative research usually involves few variables and many cases, and employs prescribed procedures to ensure validity and reliability;
• Applying well established standards means that the research can be replicated, and then analyzed and compared with similar studies;
• You can summarize vast sources of information and make comparisons across categories and over time; and,
• Personal bias can be avoided by keeping a 'distance' from participating subjects and using accepted computational techniques .

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Limitations of Using Quantitative Methods

Quantitative methods presume to have an objective approach to studying research problems, where data is controlled and measured, to address the accumulation of facts, and to determine the causes of behavior. As a consequence, the results of quantitative research may be statistically significant but are often humanly insignificant.

Some specific limitations associated with using quantitative methods to study research problems in the social sciences include:

• Quantitative data is more efficient and able to test hypotheses, but may miss contextual detail;
• Uses a static and rigid approach and so employs an inflexible process of discovery;
• The development of standard questions by researchers can lead to "structural bias" and false representation, where the data actually reflects the view of the researcher instead of the participating subject;
• Results provide less detail on behavior, attitudes, and motivation;
• Researcher may collect a much narrower and sometimes superficial dataset;
• Results are limited as they provide numerical descriptions rather than detailed narrative and generally provide less elaborate accounts of human perception;
• The research is often carried out in an unnatural, artificial environment so that a level of control can be applied to the exercise. This level of control might not normally be in place in the real world thus yielding "laboratory results" as opposed to "real world results"; and,
• Preset answers will not necessarily reflect how people really feel about a subject and, in some cases, might just be the closest match to the preconceived hypothesis.

Research Tip

Finding Examples of How to Apply Different Types of Research Methods

SAGE publications is a major publisher of studies about how to design and conduct research in the social and behavioral sciences. Their SAGE Research Methods Online and Cases database includes contents from books, articles, encyclopedias, handbooks, and videos covering social science research design and methods including the complete Little Green Book Series of Quantitative Applications in the Social Sciences and the Little Blue Book Series of Qualitative Research techniques. The database also includes case studies outlining the research methods used in real research projects. This is an excellent source for finding definitions of key terms and descriptions of research design and practice, techniques of data gathering, analysis, and reporting, and information about theories of research [e.g., grounded theory]. The database covers both qualitative and quantitative research methods as well as mixed methods approaches to conducting research.

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Quantitative Research – Methods, Types and Analysis

Table of Contents

Quantitative Research

Quantitative research is a type of research that collects and analyzes numerical data to test hypotheses and answer research questions . This research typically involves a large sample size and uses statistical analysis to make inferences about a population based on the data collected. It often involves the use of surveys, experiments, or other structured data collection methods to gather quantitative data.

Quantitative Research Methods

Quantitative Research Methods are as follows:

Descriptive Research Design

Descriptive research design is used to describe the characteristics of a population or phenomenon being studied. This research method is used to answer the questions of what, where, when, and how. Descriptive research designs use a variety of methods such as observation, case studies, and surveys to collect data. The data is then analyzed using statistical tools to identify patterns and relationships.

Correlational Research Design

Correlational research design is used to investigate the relationship between two or more variables. Researchers use correlational research to determine whether a relationship exists between variables and to what extent they are related. This research method involves collecting data from a sample and analyzing it using statistical tools such as correlation coefficients.

Quasi-experimental Research Design

Quasi-experimental research design is used to investigate cause-and-effect relationships between variables. This research method is similar to experimental research design, but it lacks full control over the independent variable. Researchers use quasi-experimental research designs when it is not feasible or ethical to manipulate the independent variable.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This research method involves manipulating the independent variable and observing the effects on the dependent variable. Researchers use experimental research designs to test hypotheses and establish cause-and-effect relationships.

Survey Research

Survey research involves collecting data from a sample of individuals using a standardized questionnaire. This research method is used to gather information on attitudes, beliefs, and behaviors of individuals. Researchers use survey research to collect data quickly and efficiently from a large sample size. Survey research can be conducted through various methods such as online, phone, mail, or in-person interviews.

Quantitative Research Analysis Methods

Here are some commonly used quantitative research analysis methods:

Statistical Analysis

Statistical analysis is the most common quantitative research analysis method. It involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis can be used to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.

Regression Analysis

Regression analysis is a statistical technique used to analyze the relationship between one dependent variable and one or more independent variables. Researchers use regression analysis to identify and quantify the impact of independent variables on the dependent variable.

Factor Analysis

Factor analysis is a statistical technique used to identify underlying factors that explain the correlations among a set of variables. Researchers use factor analysis to reduce a large number of variables to a smaller set of factors that capture the most important information.

Structural Equation Modeling

Structural equation modeling is a statistical technique used to test complex relationships between variables. It involves specifying a model that includes both observed and unobserved variables, and then using statistical methods to test the fit of the model to the data.

Time Series Analysis

Time series analysis is a statistical technique used to analyze data that is collected over time. It involves identifying patterns and trends in the data, as well as any seasonal or cyclical variations.

Multilevel Modeling

Multilevel modeling is a statistical technique used to analyze data that is nested within multiple levels. For example, researchers might use multilevel modeling to analyze data that is collected from individuals who are nested within groups, such as students nested within schools.

Applications of Quantitative Research

Quantitative research has many applications across a wide range of fields. Here are some common examples:

• Market Research : Quantitative research is used extensively in market research to understand consumer behavior, preferences, and trends. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform marketing strategies, product development, and pricing decisions.
• Health Research: Quantitative research is used in health research to study the effectiveness of medical treatments, identify risk factors for diseases, and track health outcomes over time. Researchers use statistical methods to analyze data from clinical trials, surveys, and other sources to inform medical practice and policy.
• Social Science Research: Quantitative research is used in social science research to study human behavior, attitudes, and social structures. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform social policies, educational programs, and community interventions.
• Education Research: Quantitative research is used in education research to study the effectiveness of teaching methods, assess student learning outcomes, and identify factors that influence student success. Researchers use experimental and quasi-experimental designs, as well as surveys and other quantitative methods, to collect and analyze data.
• Environmental Research: Quantitative research is used in environmental research to study the impact of human activities on the environment, assess the effectiveness of conservation strategies, and identify ways to reduce environmental risks. Researchers use statistical methods to analyze data from field studies, experiments, and other sources.

Characteristics of Quantitative Research

Here are some key characteristics of quantitative research:

• Numerical data : Quantitative research involves collecting numerical data through standardized methods such as surveys, experiments, and observational studies. This data is analyzed using statistical methods to identify patterns and relationships.
• Large sample size: Quantitative research often involves collecting data from a large sample of individuals or groups in order to increase the reliability and generalizability of the findings.
• Objective approach: Quantitative research aims to be objective and impartial in its approach, focusing on the collection and analysis of data rather than personal beliefs, opinions, or experiences.
• Control over variables: Quantitative research often involves manipulating variables to test hypotheses and establish cause-and-effect relationships. Researchers aim to control for extraneous variables that may impact the results.
• Replicable : Quantitative research aims to be replicable, meaning that other researchers should be able to conduct similar studies and obtain similar results using the same methods.
• Statistical analysis: Quantitative research involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis allows researchers to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.
• Generalizability: Quantitative research aims to produce findings that can be generalized to larger populations beyond the specific sample studied. This is achieved through the use of random sampling methods and statistical inference.

Examples of Quantitative Research

Here are some examples of quantitative research in different fields:

• Market Research: A company conducts a survey of 1000 consumers to determine their brand awareness and preferences. The data is analyzed using statistical methods to identify trends and patterns that can inform marketing strategies.
• Health Research : A researcher conducts a randomized controlled trial to test the effectiveness of a new drug for treating a particular medical condition. The study involves collecting data from a large sample of patients and analyzing the results using statistical methods.
• Social Science Research : A sociologist conducts a survey of 500 people to study attitudes toward immigration in a particular country. The data is analyzed using statistical methods to identify factors that influence these attitudes.
• Education Research: A researcher conducts an experiment to compare the effectiveness of two different teaching methods for improving student learning outcomes. The study involves randomly assigning students to different groups and collecting data on their performance on standardized tests.
• Environmental Research : A team of researchers conduct a study to investigate the impact of climate change on the distribution and abundance of a particular species of plant or animal. The study involves collecting data on environmental factors and population sizes over time and analyzing the results using statistical methods.
• Psychology : A researcher conducts a survey of 500 college students to investigate the relationship between social media use and mental health. The data is analyzed using statistical methods to identify correlations and potential causal relationships.
• Political Science: A team of researchers conducts a study to investigate voter behavior during an election. They use survey methods to collect data on voting patterns, demographics, and political attitudes, and analyze the results using statistical methods.

How to Conduct Quantitative Research

Here is a general overview of how to conduct quantitative research:

• Develop a research question: The first step in conducting quantitative research is to develop a clear and specific research question. This question should be based on a gap in existing knowledge, and should be answerable using quantitative methods.
• Develop a research design: Once you have a research question, you will need to develop a research design. This involves deciding on the appropriate methods to collect data, such as surveys, experiments, or observational studies. You will also need to determine the appropriate sample size, data collection instruments, and data analysis techniques.
• Collect data: The next step is to collect data. This may involve administering surveys or questionnaires, conducting experiments, or gathering data from existing sources. It is important to use standardized methods to ensure that the data is reliable and valid.
• Analyze data : Once the data has been collected, it is time to analyze it. This involves using statistical methods to identify patterns, trends, and relationships between variables. Common statistical techniques include correlation analysis, regression analysis, and hypothesis testing.
• Interpret results: After analyzing the data, you will need to interpret the results. This involves identifying the key findings, determining their significance, and drawing conclusions based on the data.
• Communicate findings: Finally, you will need to communicate your findings. This may involve writing a research report, presenting at a conference, or publishing in a peer-reviewed journal. It is important to clearly communicate the research question, methods, results, and conclusions to ensure that others can understand and replicate your research.

When to use Quantitative Research

Here are some situations when quantitative research can be appropriate:

• To test a hypothesis: Quantitative research is often used to test a hypothesis or a theory. It involves collecting numerical data and using statistical analysis to determine if the data supports or refutes the hypothesis.
• To generalize findings: If you want to generalize the findings of your study to a larger population, quantitative research can be useful. This is because it allows you to collect numerical data from a representative sample of the population and use statistical analysis to make inferences about the population as a whole.
• To measure relationships between variables: If you want to measure the relationship between two or more variables, such as the relationship between age and income, or between education level and job satisfaction, quantitative research can be useful. It allows you to collect numerical data on both variables and use statistical analysis to determine the strength and direction of the relationship.
• To identify patterns or trends: Quantitative research can be useful for identifying patterns or trends in data. For example, you can use quantitative research to identify trends in consumer behavior or to identify patterns in stock market data.
• To quantify attitudes or opinions : If you want to measure attitudes or opinions on a particular topic, quantitative research can be useful. It allows you to collect numerical data using surveys or questionnaires and analyze the data using statistical methods to determine the prevalence of certain attitudes or opinions.

Purpose of Quantitative Research

The purpose of quantitative research is to systematically investigate and measure the relationships between variables or phenomena using numerical data and statistical analysis. The main objectives of quantitative research include:

• Description : To provide a detailed and accurate description of a particular phenomenon or population.
• Explanation : To explain the reasons for the occurrence of a particular phenomenon, such as identifying the factors that influence a behavior or attitude.
• Prediction : To predict future trends or behaviors based on past patterns and relationships between variables.
• Control : To identify the best strategies for controlling or influencing a particular outcome or behavior.

Quantitative research is used in many different fields, including social sciences, business, engineering, and health sciences. It can be used to investigate a wide range of phenomena, from human behavior and attitudes to physical and biological processes. The purpose of quantitative research is to provide reliable and valid data that can be used to inform decision-making and improve understanding of the world around us.

Advantages of Quantitative Research

There are several advantages of quantitative research, including:

• Objectivity : Quantitative research is based on objective data and statistical analysis, which reduces the potential for bias or subjectivity in the research process.
• Reproducibility : Because quantitative research involves standardized methods and measurements, it is more likely to be reproducible and reliable.
• Generalizability : Quantitative research allows for generalizations to be made about a population based on a representative sample, which can inform decision-making and policy development.
• Precision : Quantitative research allows for precise measurement and analysis of data, which can provide a more accurate understanding of phenomena and relationships between variables.
• Efficiency : Quantitative research can be conducted relatively quickly and efficiently, especially when compared to qualitative research, which may involve lengthy data collection and analysis.
• Large sample sizes : Quantitative research can accommodate large sample sizes, which can increase the representativeness and generalizability of the results.

Limitations of Quantitative Research

There are several limitations of quantitative research, including:

• Limited understanding of context: Quantitative research typically focuses on numerical data and statistical analysis, which may not provide a comprehensive understanding of the context or underlying factors that influence a phenomenon.
• Simplification of complex phenomena: Quantitative research often involves simplifying complex phenomena into measurable variables, which may not capture the full complexity of the phenomenon being studied.
• Potential for researcher bias: Although quantitative research aims to be objective, there is still the potential for researcher bias in areas such as sampling, data collection, and data analysis.
• Limited ability to explore new ideas: Quantitative research is often based on pre-determined research questions and hypotheses, which may limit the ability to explore new ideas or unexpected findings.
• Limited ability to capture subjective experiences : Quantitative research is typically focused on objective data and may not capture the subjective experiences of individuals or groups being studied.
• Ethical concerns : Quantitative research may raise ethical concerns, such as invasion of privacy or the potential for harm to participants.

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• What Is Quantitative Research? | Definition & Methods

What Is Quantitative Research? | Definition & Methods

Published on 4 April 2022 by Pritha Bhandari . Revised on 10 October 2022.

Quantitative research is the process of collecting and analysing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalise results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analysing non-numerical data (e.g. text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

• What is the demographic makeup of Singapore in 2020?
• How has the average temperature changed globally over the last century?
• Does environmental pollution affect the prevalence of honey bees?
• Does working from home increase productivity for people with long commutes?

Table of contents

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

• In descriptive research , you simply seek an overall summary of your study variables.
• In correlational research , you investigate relationships between your study variables.
• In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalised to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

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Once data is collected, you may need to process it before it can be analysed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualise your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalisations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardise data collection and generalise findings . Strengths of this approach include:

• Replication

Repeating the study is possible because of standardised data collection protocols and tangible definitions of abstract concepts.

• Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

• Large samples

Data from large samples can be processed and analysed using reliable and consistent procedures through quantitative data analysis.

• Hypothesis testing

Using formalised and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

• Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

• Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

• Structural bias

Despite standardised procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

• Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

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

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

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

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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.

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.

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What is Quantitative Research? Definition, Methods, Types, and Examples

If you’re wondering what is quantitative research and whether this methodology works for your research study, you’re not alone. If you want a simple quantitative research definition , then it’s enough to say that this is a method undertaken by researchers based on their study requirements. However, to select the most appropriate research for their study type, researchers should know all the methods available. 

Selecting the right research method depends on a few important criteria, such as the research question, study type, time, costs, data availability, and availability of respondents. There are two main types of research methods— quantitative research  and qualitative research. The purpose of quantitative research is to validate or test a theory or hypothesis and that of qualitative research is to understand a subject or event or identify reasons for observed patterns.   

Quantitative research methods  are used to observe events that affect a particular group of individuals, which is the sample population. In this type of research, diverse numerical data are collected through various methods and then statistically analyzed to aggregate the data, compare them, or show relationships among the data. Quantitative research methods broadly include questionnaires, structured observations, and experiments.  

Here are two quantitative research examples:  

• Satisfaction surveys sent out by a company regarding their revamped customer service initiatives. Customers are asked to rate their experience on a rating scale of 1 (poor) to 5 (excellent).  
• A school has introduced a new after-school program for children, and a few months after commencement, the school sends out feedback questionnaires to the parents of the enrolled children. Such questionnaires usually include close-ended questions that require either definite answers or a Yes/No option. This helps in a quick, overall assessment of the program’s outreach and success.  

Table of Contents

What is quantitative research ? 1,2

The steps shown in the figure can be grouped into the following broad steps:  

• Theory : Define the problem area or area of interest and create a research question.  
• Hypothesis : Develop a hypothesis based on the research question. This hypothesis will be tested in the remaining steps.  
• Research design : In this step, the most appropriate quantitative research design will be selected, including deciding on the sample size, selecting respondents, identifying research sites, if any, etc.
• Data collection : This process could be extensive based on your research objective and sample size.  
• Data analysis : Statistical analysis is used to analyze the data collected. The results from the analysis help in either supporting or rejecting your hypothesis.  
• Present results : Based on the data analysis, conclusions are drawn, and results are presented as accurately as possible.  

Quantitative research characteristics 4

• Large sample size : This ensures reliability because this sample represents the target population or market. Due to the large sample size, the outcomes can be generalized to the entire population as well, making this one of the important characteristics of quantitative research .  
• Structured data and measurable variables: The data are numeric and can be analyzed easily. Quantitative research involves the use of measurable variables such as age, salary range, highest education, etc.  
• Easy-to-use data collection methods : The methods include experiments, controlled observations, and questionnaires and surveys with a rating scale or close-ended questions, which require simple and to-the-point answers; are not bound by geographical regions; and are easy to administer.  
• Data analysis : Structured and accurate statistical analysis methods using software applications such as Excel, SPSS, R. The analysis is fast, accurate, and less effort intensive.  
• Reliable : The respondents answer close-ended questions, their responses are direct without ambiguity and yield numeric outcomes, which are therefore highly reliable.  
• Reusable outcomes : This is one of the key characteristics – outcomes of one research can be used and replicated in other research as well and is not exclusive to only one study.  

Quantitative research methods 5

Quantitative research methods are classified into two types—primary and secondary.  

Primary quantitative research method:

In this type of quantitative research , data are directly collected by the researchers using the following methods.

– Survey research : Surveys are the easiest and most commonly used quantitative research method . They are of two types— cross-sectional and longitudinal.   

->Cross-sectional surveys are specifically conducted on a target population for a specified period, that is, these surveys have a specific starting and ending time and researchers study the events during this period to arrive at conclusions. The main purpose of these surveys is to describe and assess the characteristics of a population. There is one independent variable in this study, which is a common factor applicable to all participants in the population, for example, living in a specific city, diagnosed with a specific disease, of a certain age group, etc. An example of a cross-sectional survey is a study to understand why individuals residing in houses built before 1979 in the US are more susceptible to lead contamination.  

->Longitudinal surveys are conducted at different time durations. These surveys involve observing the interactions among different variables in the target population, exposing them to various causal factors, and understanding their effects across a longer period. These studies are helpful to analyze a problem in the long term. An example of a longitudinal study is the study of the relationship between smoking and lung cancer over a long period.  

– Descriptive research : Explains the current status of an identified and measurable variable. Unlike other types of quantitative research , a hypothesis is not needed at the beginning of the study and can be developed even after data collection. This type of quantitative research describes the characteristics of a problem and answers the what, when, where of a problem. However, it doesn’t answer the why of the problem and doesn’t explore cause-and-effect relationships between variables. Data from this research could be used as preliminary data for another study. Example: A researcher undertakes a study to examine the growth strategy of a company. This sample data can be used by other companies to determine their own growth strategy.  

– Correlational research : This quantitative research method is used to establish a relationship between two variables using statistical analysis and analyze how one affects the other. The research is non-experimental because the researcher doesn’t control or manipulate any of the variables. At least two separate sample groups are needed for this research. Example: Researchers studying a correlation between regular exercise and diabetes.  

– Causal-comparative research : This type of quantitative research examines the cause-effect relationships in retrospect between a dependent and independent variable and determines the causes of the already existing differences between groups of people. This is not a true experiment because it doesn’t assign participants to groups randomly. Example: To study the wage differences between men and women in the same role. For this, already existing wage information is analyzed to understand the relationship.  

– Experimental research : This quantitative research method uses true experiments or scientific methods for determining a cause-effect relation between variables. It involves testing a hypothesis through experiments, in which one or more independent variables are manipulated and then their effect on dependent variables are studied. Example: A researcher studies the importance of a drug in treating a disease by administering the drug in few patients and not administering in a few.  

The following data collection methods are commonly used in primary quantitative research :  

• Sampling : The most common type is probability sampling, in which a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are—simple random, systematic, stratified, and cluster sampling.  
• Interviews : These are commonly telephonic or face-to-face.  
• Observations : Structured observations are most commonly used in quantitative research . In this method, researchers make observations about specific behaviors of individuals in a structured setting.  
• Document review : Reviewing existing research or documents to collect evidence for supporting the quantitative research .  
• Surveys and questionnaires : Surveys can be administered both online and offline depending on the requirement and sample size.

The data collected can be analyzed in several ways in quantitative research , as listed below:  

• Cross-tabulation —Uses a tabular format to draw inferences among collected data  
• MaxDiff analysis —Gauges the preferences of the respondents  
• TURF analysis —Total Unduplicated Reach and Frequency Analysis; helps in determining the market strategy for a business  
• Gap analysis —Identify gaps in attaining the desired results  
• SWOT analysis —Helps identify strengths, weaknesses, opportunities, and threats of a product, service, or organization  
• Text analysis —Used for interpreting unstructured data  

Secondary quantitative research methods :

This method involves conducting research using already existing or secondary data. This method is less effort intensive and requires lesser time. However, researchers should verify the authenticity and recency of the sources being used and ensure their accuracy.  

The main sources of secondary data are: 

• The Internet  
• Government and non-government sources  
• Public libraries  
• Educational institutions  
• Commercial information sources such as newspapers, journals, radio, TV  

When to use quantitative research 6  

Here are some simple ways to decide when to use quantitative research . Use quantitative research to:  

• recommend a final course of action  
• find whether a consensus exists regarding a particular subject  
• generalize results to a larger population  
• determine a cause-and-effect relationship between variables  
• describe characteristics of specific groups of people  
• test hypotheses and examine specific relationships  
• identify and establish size of market segments  

A research case study to understand when to use quantitative research 7  

Context: A study was undertaken to evaluate a major innovation in a hospital’s design, in terms of workforce implications and impact on patient and staff experiences of all single-room hospital accommodations. The researchers undertook a mixed methods approach to answer their research questions. Here, we focus on the quantitative research aspect.  

Research questions : What are the advantages and disadvantages for the staff as a result of the hospital’s move to the new design with all single-room accommodations? Did the move affect staff experience and well-being and improve their ability to deliver high-quality care?  

Method: The researchers obtained quantitative data from three sources:  

• Staff activity (task time distribution): Each staff member was shadowed by a researcher who observed each task undertaken by the staff, and logged the time spent on each activity.  
• Staff travel distances : The staff were requested to wear pedometers, which recorded the distances covered.  
• Staff experience surveys : Staff were surveyed before and after the move to the new hospital design.  

Results of quantitative research : The following observations were made based on quantitative data analysis:  

• The move to the new design did not result in a significant change in the proportion of time spent on different activities.  
• Staff activity events observed per session were higher after the move, and direct care and professional communication events per hour decreased significantly, suggesting fewer interruptions and less fragmented care.  
• A significant increase in medication tasks among the recorded events suggests that medication administration was integrated into patient care activities.  
• Travel distances increased for all staff, with highest increases for staff in the older people’s ward and surgical wards.  
• Ratings for staff toilet facilities, locker facilities, and space at staff bases were higher but those for social interaction and natural light were lower.  

Advantages of quantitative research 1,2

When choosing the right research methodology, also consider the advantages of quantitative research and how it can impact your study.  

• Quantitative research methods are more scientific and rational. They use quantifiable data leading to objectivity in the results and avoid any chances of ambiguity.  
• This type of research uses numeric data so analysis is relatively easier .  
• In most cases, a hypothesis is already developed and quantitative research helps in testing and validatin g these constructed theories based on which researchers can make an informed decision about accepting or rejecting their theory.  
• The use of statistical analysis software ensures quick analysis of large volumes of data and is less effort intensive.  
• Higher levels of control can be applied to the research so the chances of bias can be reduced.  
• Quantitative research is based on measured value s, facts, and verifiable information so it can be easily checked or replicated by other researchers leading to continuity in scientific research.  

Disadvantages of quantitative research 1,2

Quantitative research may also be limiting; take a look at the disadvantages of quantitative research. 

• Experiments are conducted in controlled settings instead of natural settings and it is possible for researchers to either intentionally or unintentionally manipulate the experiment settings to suit the results they desire.  
• Participants must necessarily give objective answers (either one- or two-word, or yes or no answers) and the reasons for their selection or the context are not considered.   
• Inadequate knowledge of statistical analysis methods may affect the results and their interpretation.  
• Although statistical analysis indicates the trends or patterns among variables, the reasons for these observed patterns cannot be interpreted and the research may not give a complete picture.  
• Large sample sizes are needed for more accurate and generalizable analysis .  
• Quantitative research cannot be used to address complex issues.  

Frequently asked questions on  quantitative research    

Q:  What is the difference between quantitative research and qualitative research? 1  

A:  The following table lists the key differences between quantitative research and qualitative research, some of which may have been mentioned earlier in the article.  

Q:  What is the difference between reliability and validity? 8,9    

A:  The term reliability refers to the consistency of a research study. For instance, if a food-measuring weighing scale gives different readings every time the same quantity of food is measured then that weighing scale is not reliable. If the findings in a research study are consistent every time a measurement is made, then the study is considered reliable. However, it is usually unlikely to obtain the exact same results every time because some contributing variables may change. In such cases, a correlation coefficient is used to assess the degree of reliability. A strong positive correlation between the results indicates reliability.  

Validity can be defined as the degree to which a tool actually measures what it claims to measure. It helps confirm the credibility of your research and suggests that the results may be generalizable. In other words, it measures the accuracy of the research.  

The following table gives the key differences between reliability and validity.  

Q:  What is mixed methods research? 10

A:  A mixed methods approach combines the characteristics of both quantitative research and qualitative research in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method. A mixed methods research design is useful in case of research questions that cannot be answered by either quantitative research or qualitative research alone. However, this method could be more effort- and cost-intensive because of the requirement of more resources. The figure 3 shows some basic mixed methods research designs that could be used.  

Thus, quantitative research is the appropriate method for testing your hypotheses and can be used either alone or in combination with qualitative research per your study requirements. We hope this article has provided an insight into the various facets of quantitative research , including its different characteristics, advantages, and disadvantages, and a few tips to quickly understand when to use this research method.  

References  

• Qualitative vs quantitative research: Differences, examples, & methods. Simply Psychology. Accessed Feb 28, 2023. https://simplypsychology.org/qualitative-quantitative.html#Quantitative-Research  
• Your ultimate guide to quantitative research. Qualtrics. Accessed February 28, 2023. https://www.qualtrics.com/uk/experience-management/research/quantitative-research/  
• The steps of quantitative research. Revise Sociology. Accessed March 1, 2023. https://revisesociology.com/2017/11/26/the-steps-of-quantitative-research/  
• What are the characteristics of quantitative research? Marketing91. Accessed March 1, 2023. https://www.marketing91.com/characteristics-of-quantitative-research/  
• Quantitative research: Types, characteristics, methods, & examples. ProProfs Survey Maker. Accessed February 28, 2023. https://www.proprofssurvey.com/blog/quantitative-research/#Characteristics_of_Quantitative_Research  
• Qualitative research isn’t as scientific as quantitative methods. Kmusial blog. Accessed March 5, 2023. https://kmusial.wordpress.com/2011/11/25/qualitative-research-isnt-as-scientific-as-quantitative-methods/  
• Maben J, Griffiths P, Penfold C, et al. Evaluating a major innovation in hospital design: workforce implications and impact on patient and staff experiences of all single room hospital accommodation. Southampton (UK): NIHR Journals Library; 2015 Feb. (Health Services and Delivery Research, No. 3.3.) Chapter 5, Case study quantitative data findings. Accessed March 6, 2023. https://www.ncbi.nlm.nih.gov/books/NBK274429/  
• McLeod, S. A. (2007).  What is reliability?  Simply Psychology. www.simplypsychology.org/reliability.html  
• Reliability vs validity: Differences & examples. Accessed March 5, 2023. https://statisticsbyjim.com/basics/reliability-vs-validity/  
• Mixed methods research. Community Engagement Program. Harvard Catalyst. Accessed February 28, 2023. https://catalyst.harvard.edu/community-engagement/mmr  

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S371 Social Work Research - Jill Chonody: What is Quantitative Research?

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Quantitative Research in the Social Sciences

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Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques . Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Muijs, Daniel. Doing Quantitative Research in Education with SPSS . 2nd edition. London: SAGE Publications, 2010.

Characteristics of Quantitative Research

Your goal in conducting quantitative research study is to determine the relationship between one thing [an independent variable] and another [a dependent or outcome variable] within a population. Quantitative research designs are either descriptive [subjects usually measured once] or experimental [subjects measured before and after a treatment]. A descriptive study establishes only associations between variables; an experimental study establishes causality.

Quantitative research deals in numbers, logic, and an objective stance. Quantitative research focuses on numberic and unchanging data and detailed, convergent reasoning rather than divergent reasoning [i.e., the generation of a variety of ideas about a research problem in a spontaneous, free-flowing manner].

Its main characteristics are :

• The data is usually gathered using structured research instruments.
• The results are based on larger sample sizes that are representative of the population.
• The research study can usually be replicated or repeated, given its high reliability.
• Researcher has a clearly defined research question to which objective answers are sought.
• All aspects of the study are carefully designed before data is collected.
• Data are in the form of numbers and statistics, often arranged in tables, charts, figures, or other non-textual forms.
• Project can be used to generalize concepts more widely, predict future results, or investigate causal relationships.
• Researcher uses tools, such as questionnaires or computer software, to collect numerical data.

The overarching aim of a quantitative research study is to classify features, count them, and construct statistical models in an attempt to explain what is observed.

  Things to keep in mind when reporting the results of a study using quantiative methods :

• Explain the data collected and their statistical treatment as well as all relevant results in relation to the research problem you are investigating. Interpretation of results is not appropriate in this section.
• Report unanticipated events that occurred during your data collection. Explain how the actual analysis differs from the planned analysis. Explain your handling of missing data and why any missing data does not undermine the validity of your analysis.
• Explain the techniques you used to "clean" your data set.
• Choose a minimally sufficient statistical procedure ; provide a rationale for its use and a reference for it. Specify any computer programs used.
• Describe the assumptions for each procedure and the steps you took to ensure that they were not violated.
• When using inferential statistics , provide the descriptive statistics, confidence intervals, and sample sizes for each variable as well as the value of the test statistic, its direction, the degrees of freedom, and the significance level [report the actual p value].
• Avoid inferring causality , particularly in nonrandomized designs or without further experimentation.
• Use tables to provide exact values ; use figures to convey global effects. Keep figures small in size; include graphic representations of confidence intervals whenever possible.
• Always tell the reader what to look for in tables and figures .

NOTE:   When using pre-existing statistical data gathered and made available by anyone other than yourself [e.g., government agency], you still must report on the methods that were used to gather the data and describe any missing data that exists and, if there is any, provide a clear explanation why the missing datat does not undermine the validity of your final analysis.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Quantitative Research Methods . Writing@CSU. Colorado State University; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Basic Research Designs for Quantitative Studies

Before designing a quantitative research study, you must decide whether it will be descriptive or experimental because this will dictate how you gather, analyze, and interpret the results. A descriptive study is governed by the following rules: subjects are generally measured once; the intention is to only establish associations between variables; and, the study may include a sample population of hundreds or thousands of subjects to ensure that a valid estimate of a generalized relationship between variables has been obtained. An experimental design includes subjects measured before and after a particular treatment, the sample population may be very small and purposefully chosen, and it is intended to establish causality between variables. Introduction The introduction to a quantitative study is usually written in the present tense and from the third person point of view. It covers the following information:

• Identifies the research problem -- as with any academic study, you must state clearly and concisely the research problem being investigated.
• Reviews the literature -- review scholarship on the topic, synthesizing key themes and, if necessary, noting studies that have used similar methods of inquiry and analysis. Note where key gaps exist and how your study helps to fill these gaps or clarifies existing knowledge.
• Describes the theoretical framework -- provide an outline of the theory or hypothesis underpinning your study. If necessary, define unfamiliar or complex terms, concepts, or ideas and provide the appropriate background information to place the research problem in proper context [e.g., historical, cultural, economic, etc.].

Methodology The methods section of a quantitative study should describe how each objective of your study will be achieved. Be sure to provide enough detail to enable the reader can make an informed assessment of the methods being used to obtain results associated with the research problem. The methods section should be presented in the past tense.

• Study population and sampling -- where did the data come from; how robust is it; note where gaps exist or what was excluded. Note the procedures used for their selection;
• Data collection – describe the tools and methods used to collect information and identify the variables being measured; describe the methods used to obtain the data; and, note if the data was pre-existing [i.e., government data] or you gathered it yourself. If you gathered it yourself, describe what type of instrument you used and why. Note that no data set is perfect--describe any limitations in methods of gathering data.
• Data analysis -- describe the procedures for processing and analyzing the data. If appropriate, describe the specific instruments of analysis used to study each research objective, including mathematical techniques and the type of computer software used to manipulate the data.

Results The finding of your study should be written objectively and in a succinct and precise format. In quantitative studies, it is common to use graphs, tables, charts, and other non-textual elements to help the reader understand the data. Make sure that non-textual elements do not stand in isolation from the text but are being used to supplement the overall description of the results and to help clarify key points being made. Further information about how to effectively present data using charts and graphs can be found here .

• Statistical analysis -- how did you analyze the data? What were the key findings from the data? The findings should be present in a logical, sequential order. Describe but do not interpret these trends or negative results; save that for the discussion section. The results should be presented in the past tense.

Discussion Discussions should be analytic, logical, and comprehensive. The discussion should meld together your findings in relation to those identified in the literature review, and placed within the context of the theoretical framework underpinning the study. The discussion should be presented in the present tense.

• Interpretation of results -- reiterate the research problem being investigated and compare and contrast the findings with the research questions underlying the study. Did they affirm predicted outcomes or did the data refute it?
• Description of trends, comparison of groups, or relationships among variables -- describe any trends that emerged from your analysis and explain all unanticipated and statistical insignificant findings.
• Discussion of implications – what is the meaning of your results? Highlight key findings based on the overall results and note findings that you believe are important. How have the results helped fill gaps in understanding the research problem?
• Limitations -- describe any limitations or unavoidable bias in your study and, if necessary, note why these limitations did not inhibit effective interpretation of the results.

Conclusion End your study by to summarizing the topic and provide a final comment and assessment of the study.

• Summary of findings – synthesize the answers to your research questions. Do not report any statistical data here; just provide a narrative summary of the key findings and describe what was learned that you did not know before conducting the study.
• Recommendations – if appropriate to the aim of the assignment, tie key findings with policy recommendations or actions to be taken in practice.
• Future research – note the need for future research linked to your study’s limitations or to any remaining gaps in the literature that were not addressed in your study.

Black, Thomas R. Doing Quantitative Research in the Social Sciences: An Integrated Approach to Research Design, Measurement and Statistics . London: Sage, 1999; Gay,L. R. and Peter Airasain. Educational Research: Competencies for Analysis and Applications . 7th edition. Upper Saddle River, NJ: Merril Prentice Hall, 2003; Hector, Anestine.  An Overview of Quantitative Research in Compostion and TESOL . Department of English, Indiana University of Pennsylvania; Hopkins, Will G. “Quantitative Research Design.” Sportscience 4, 1 (2000); A Strategy for Writing Up Research Results . The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Nenty, H. Johnson. "Writing a Quantitative Research Thesis." International Journal of Educational Science 1 (2009): 19-32; Ouyang, Ronghua (John). Basic Inquiry of Quantitative Research . Kennesaw State University.

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Quantitative Methods

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• Juwel Rana 2 , 3 , 4 ,
• Patricia Luna Gutierrez 5 &
• John C. Oldroyd 6  

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Quantitative analysis ; Quantitative research methods ; Study design

Quantitative method is the collection and analysis of numerical data to answer scientific research questions. Quantitative method is used to summarize, average, find patterns, make predictions, and test causal associations as well as generalizing results to wider populations. It allows us to quantify effect sizes, determine the strength of associations, rank priorities, and weigh the strength of evidence of effectiveness.

Introduction

This entry aims to introduce the most common ways to use numbers and statistics to describe variables, establish relationships among variables, and build numerical understanding of a topic. In general, the quantitative research process uses a deductive approach (Neuman 2014 ; Leavy 2017 ), extrapolating from a particular case to the general situation (Babones 2016 ).

In practical ways, quantitative methods are an approach to studying a research topic. In research, the...

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Babones S (2016) Interpretive quantitative methods for the social sciences. Sociology. https://doi.org/10.1177/0038038515583637

Balnaves M, Caputi P (2001) Introduction to quantitative research methods: an investigative approach. Sage, London

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Brenner PS (2020) Understanding survey methodology: sociological theory and applications. Springer, Boston

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Creswell JW (2014) Research design: qualitative, quantitative, and mixed methods approaches. Sage, London

Leavy P (2017) Research design. The Gilford Press, New York

Mertens W, Pugliese A, Recker J (2018) Quantitative data analysis, research methods: information, systems, and contexts: second edition. https://doi.org/10.1016/B978-0-08-102220-7.00018-2

Neuman LW (2014) Social research methods: qualitative and quantitative approaches. Pearson Education Limited, Edinburgh

Treiman DJ (2009) Quantitative data analysis: doing social research to test ideas. Jossey-Bass, San Francisco

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Department of Public Health, School of Health and Life Sciences, North South University, Dhaka, Bangladesh

Department of Biostatistics and Epidemiology, School of Health and Health Sciences, University of Massachusetts Amherst, MA, USA

Department of Research and Innovation, South Asia Institute for Social Transformation (SAIST), Dhaka, Bangladesh

Independent Researcher, Masatepe, Nicaragua

Patricia Luna Gutierrez

School of Behavioral and Health Sciences, Australian Catholic University, Fitzroy, VIC, Australia

John C. Oldroyd

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Rana, J., Gutierrez, P.L., Oldroyd, J.C. (2021). Quantitative Methods. In: Farazmand, A. (eds) Global Encyclopedia of Public Administration, Public Policy, and Governance. Springer, Cham. https://doi.org/10.1007/978-3-319-31816-5_460-1

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What Is Quantitative Research? Types, Characteristics & Methods

Market Research Specialist

Emma David, a seasoned market research professional, specializes in employee engagement, survey administration, and data management. Her expertise in leveraging data for informed decisions has positively impacted several brands, enhancing their market position.

Step into the fascinating world of quantitative research , where numbers reveal extraordinary insights!

By gathering and studying data in a systematic way, quantitative research empowers us to understand our ever-changing world better. It helps understand a problem or an already-formed hypothesis by generating numerical data. The results don’t end here, as you can process these numbers to get actionable insights that aid decision-making.

You can use quantitative research to quantify opinions, behaviors, attitudes, and other definitive variables related to the market, customers, competitors, etc. The research is conducted on a larger sample population to draw predictive, average, and pattern-based insights.

Here, we delve into the intricacies of this research methodology, exploring various quantitative methods, their advantages, and real-life examples that showcase their impact and relevance.

Ready to embark on a journey of discovery and knowledge? Let’s go!

What Is Quantitative Research?

Quantitative research is a method that uses numbers and statistics to test theories about customer attitudes and behaviors. It helps researchers gather and analyze data systematically to gain valuable insights and draw evidence-based conclusions about customer preferences and trends.

Researchers use online surveys, questionnaires , polls , and quizzes to question a large number of people to obtain measurable and bias-free data.

In technical terms, quantitative research is mainly concerned with discovering facts about social phenomena while assuming a fixed and measurable reality.

Offering numbers and stats-based insights, this research methodology is a crucial part of primary research and helps understand how well an organizational decision is going to work out.

Let’s consider an example.

Suppose your qualitative analysis shows that your customers are looking for social media-based customer support. In that case, quantitative analysis will help you see how many of your customers are looking for this support.

If 10% of your customers are looking for such a service, you might or might not consider offering this feature. But, if 40% of your regular customers are seeking support via social media, then it is something you just cannot overlook.

Characteristics of Quantitative Research

Quantitative research clarifies the fuzziness of research data from qualitative research analysis. With numerical insights, you can formulate a better and more profitable business decision.

Hence, quantitative research is more readily contestable, sharpens intelligent discussion, helps you see the rival hypotheses, and dynamically contributes to the research process.

Let us have a quick look at some of its characteristics.

1. Measurable Variables

The data collection methods in quantitative research are structured and contain items requiring measurable variables, such as age, number of family members, salary range, highest education, etc.

These structured data collection methods comprise polls, surveys, questionnaires, etc., and may have questions like the ones shown in the following image:

As you can see, all the variables are measurable. This ensures that the research is in-depth and provides less erroneous data for reliable, actionable insights.

2. Sample Size

No matter what data analysis methods for quantitative research are being used, the sample size is kept such that it represents the target market.

As the main aim of the research methodology is to get numerical insights, the sample size should be fairly large. Depending on the survey objective and scope, it might span hundreds of thousands of people.

3. Normal Population Distribution

To maintain the reliability of a quantitative research methodology, we assume that the population distribution curve is normal.

This type of population distribution curve is preferred over a non-normal distribution as the sample size is large, and the characteristics of the sample vary with its size.

This requires adhering to the random sampling principle to avoid the researcher’s bias in result interpretation. Any bias can ruin the fairness of the entire process and defeats the purpose of research.

4. Well-Structured Data Representation

Data analysis in quantitative research produces highly structured results and can form well-defined graphical representations. Some common examples include tables, figures, graphs, etc., that combine large blocks of data.

This way, you can discover hidden data trends, relationships, and differences among various measurable variables. This can help researchers understand the survey data and formulate actionable insights for decision-making.

5. Predictive Outcomes

Quantitative analysis of data can also be used for estimations and prediction outcomes. You can construct if-then scenarios and analyze the data for the identification of any upcoming trends or events.

However, this requires advanced analytics and involves complex mathematical computations. So, it is mostly done via quantitative research tools that come with advanced analytics capabilities.

Types of Quantitative Research Methods

Quantitative research is usually conducted using two methods. They are-

• Primary quantitative research methods
• Secondary quantitative research methods

1. Primary quantitative research methods

Primary quantitative research is the most popular way of conducting market research. The differentiating factor of this method is that the researcher relies on collecting data firsthand instead of relying on data collected from previous research.

There are multiple types of primary quantitative research. They can be distinguished based on three distinctive aspects, which are:

1.1. Techniques & Types of Studies:

• Survey Research

Surveys are the easiest, most common, and one of the most sought-after quantitative research techniques. The main aim of a survey is to widely gather and describe the characteristics of a target population or customers. Surveys are the foremost quantitative method preferred by both small and large organizations.

They help them understand their customers, products, and other brand offerings in a proper manner.

Surveys can be conducted using various methods, such as online polls, web-based surveys, paper questionnaires, phone calls, or face-to-face interviews. Survey research allows organizations to understand customer opinions, preferences, and behavior, making it crucial for market research and decision-making.

You can watch this quick video to learn more about creating surveys.

Watch: How to Create a Survey Using ProProfs Survey Maker

Surveys are of two types:

• Cross-Sectional Surveys Cross-sectional surveys are used to collect data from a sample of the target population at a specific point in time. Researchers evaluate various variables simultaneously to understand the relationships and patterns within the data.
• Cross-sectional surveys are popular in retail, small and medium-sized enterprises (SMEs), and healthcare industries, where they assess customer satisfaction, market trends, and product feedback.
• Longitudinal Surveys Longitudinal surveys are conducted over an extended period, observing changes in respondent behavior and thought processes.
• Researchers gather data from the same sample multiple times, enabling them to study trends and developments over time. These surveys are valuable in fields such as medicine, applied sciences, and market trend analysis.

Surveys can be distributed via various channels. Some of the most popular ones are listed below:

• Email: Sending surveys via email is a popular and effective method. People recognize your brand, leading to a higher response rate. With ProProfs Survey Maker’s in-mail survey-filling feature, you can easily send out and collect survey responses.
• Embed on a website: Boost your response rate by embedding the survey on your website. When visitors are already engaged with your brand, they are more likely to take the survey.
• Social media: Take advantage of social media platforms to distribute your survey. People familiar with your brand are likely to respond, increasing your response numbers.
• QR codes: QR codes store your survey’s URL, and you can print or publish these codes in magazines, signs, business cards, or any object to make it easy for people to access your survey.
• SMS survey: Collect a high number of responses quickly with SMS surveys. It’s a time-effective way to reach your target audience.

1.2. Correlational Research:

Correlational research aims to establish relationships between two or more variables.

Researchers use statistical analysis to identify patterns and trends in the data, but it does not determine causality between the variables. This method helps understand how changes in one variable may impact another.

Examples of correlational research questions include studying the relationship between stress and depression, fame and money, or classroom activities and student performance.

1.3. Causal-Comparative Research:

Causal-comparative research, also known as quasi-experimental research, seeks to determine cause-and-effect relationships between variables.

Researchers analyze how an independent variable influences a dependent variable, but they do not manipulate the independent variable. Instead, they observe and compare different groups to draw conclusions.

Causal-comparative research is useful in situations where it’s not ethical or feasible to conduct true experiments.

Examples of questions for this type of research include analyzing the effect of training programs on employee performance, studying the influence of customer support on client retention, investigating the impact of supply chain efficiency on cost reduction, etc.

1.4. Experimental Research:

Experimental research is based on testing theories to validate or disprove them. Researchers conduct experiments and manipulate variables to observe their impact on the outcomes.

This type of research is prevalent in natural and social sciences, and it is a powerful method to establish cause-and-effect relationships. By randomly assigning participants to experimental and control groups, researchers can draw more confident conclusions.

Examples of experimental research include studying the effectiveness of a new drug, the impact of teaching methods on student performance, or the outcomes of a marketing campaign.

2. Data collection methodologies

After defining research objectives, the next significant step in primary quantitative research is data collection. This involves using two main methods: sampling and conducting surveys or polls.

2.1Sampling methods:

In quantitative research, there are two primary sampling methods: Probability and Non-probability sampling.

2.2Probability Sampling

In probability sampling, researchers use the concept of probability to create samples from a population. This method ensures that every individual in the target audience has an equal chance of being selected for the sample.

There are four main types of probability sampling:

• Simple random sampling: Here, the elements or participants of a sample are selected randomly, and this technique is used in studies that are conducted over considerably large audiences. It works well for large target populations.
• Stratified random sampling: In this method, the entire population is divided into strata or groups, and the sample members get chosen randomly from these strata only. It is always ensured that different segregated strata do not overlap with each other.
• Cluster sampling: Here, researchers divide the population into clusters, often based on geography or demographics. Then, random clusters are selected for the sample.
• Systematic sampling: In this method, only the starting point of the sample is randomly chosen. All the other participants are chosen using a fixed interval. Researchers calculate this interval by dividing the size of the study population by the target sample size.

2.3Non-probability Sampling

Non-probability sampling is a method where the researcher’s knowledge and experience guide the selection of samples. This approach doesn’t give all members of the target population an equal chance of being included in the sample.

There are five non-probability sampling models:

• Convenience sampling: The elements or participants are chosen on the basis of their nearness to the researcher. The people in close proximity can be studied and analyzed easily and quickly, as there is no other selection criterion involved. Researchers simply choose samples based on what is most convenient for them.
• Consecutive sampling: Similar to convenience sampling, researchers select samples one after another over a significant period. They can opt for a single participant or a group of samples to conduct quantitative research in a consecutive manner for a significant period of time. Once this is over, they can conduct the research from the start.
• Quota sampling: With quota sampling, researchers use their understanding of target traits and personalities to form groups (strata). They then choose samples from each stratum based on their own judgment.
• Snowball sampling: This method is used where the target audiences are difficult to contact and interviewed for data collection. Researchers start with a few participants and then ask them to refer others, creating a snowball effect.
• Judgmental sampling: In judgmental sampling, researchers rely solely on their experience and research skills to handpick samples that they believe will be most relevant to the study.

3. Data analysis techniques

To analyze the quantitative data accurately, you’ll need to use specific statistical methods such as:

• SWOT Analysis: This stands for Strengths, Weaknesses, Opportunities, and Threats analysis. Organizations use SWOT analysis to evaluate their performance internally and externally. It helps develop effective improvement strategies.
• Conjoint Analysis: This market research method uncovers how individuals make complex purchasing decisions. It involves considering trade-offs in their daily activities when choosing from a list of product/service options.
• Cross-tabulation: A preliminary statistical market analysis method that reveals relationships, patterns, and trends within various research study parameters.
• TURF Analysis: Short for Totally Unduplicated Reach and Frequency Analysis, this method helps analyze the reach and frequency of favorable communication sources. It provides insights into the potential of a target market.
• By using these statistical techniques and inferential statistics methods like confidence intervals and margin of error, you can draw meaningful insights from your primary quantitative research that you can use in making informed decisions.

2. Secondary Quantitative Research Methods

• Secondary quantitative research, also known as desk research, is a valuable method that uses existing data, called secondary data.
• Instead of collecting new data, researchers analyze and combine already available information to enhance their research. This approach involves gathering quantitative data from various sources such as the internet, government databases, libraries, and research reports.
• Secondary quantitative research plays a crucial role in validating data collected through primary quantitative research. It helps reinforce or challenge existing findings.

Here are five commonly used secondary quantitative research methods:

A. Data Available on the Internet:

The Internet has become a vast repository of data, making it easier for researchers to access a wealth of information. Online databases, websites, and research repositories provide valuable quantitative data for researchers to analyze and validate their primary research findings.

B. Government and Non-Government Sources:

Government agencies and non-government organizations often conduct extensive research and publish reports. These reports cover a wide range of topics, providing researchers with reliable and comprehensive data for quantitative analysis.

C. Public Libraries:

While less commonly used in the digital age, public libraries still hold valuable research reports, historical data, and publications that can contribute to quantitative research.

D. Educational Institutions:

Educational institutions frequently conduct research on various subjects. Their research reports and publications can serve as valuable sources of information for researchers, validating and supporting primary quantitative research outcomes.

E. Commercial Information Sources:

Commercial sources such as local newspapers, journals, magazines, and media outlets often publish relevant data on economic trends, market research, and demographic analyses. Researchers can access this data to supplement their own findings and draw better conclusions.

Advantages of Quantitative Research Methods

Quantitative research data is often standardized and can be easily used to generalize findings for making crucial business decisions and uncover insights to supplement the qualitative research findings.

Here are some core benefits this research methodology offers.

Direct Result Comparison

As the studies can be replicated for different cultural settings and different times, even with different groups of participants, they tend to be extremely useful. Researchers can compare the results of different studies in a statistical manner and arrive at comprehensive conclusions for a broader understanding.

Replication

Researchers can repeat the study by using standardized data collection protocols over well-structured data sets. They can also apply tangible definitions of abstract concepts to arrive at different conclusions for similar research objectives with minor variations.

Large Samples

As the research data comes from large samples, the researchers can process and analyze the data via highly reliable and consistent analysis procedures. They can arrive at well-defined conclusions that can be used to make the primary research more thorough and reliable.

Hypothesis Testing

This research methodology follows standardized and established hypothesis testing procedures. So, you have to be careful while reporting and analyzing your research data , and the overall quality of results gets improved.

Proven Examples of Quantitative Research Methods

Below, we discuss two excellent examples of quantitative research methods that were used by highly distinguished business and consulting organizations. Both examples show how different types of analysis can be performed with qualitative approaches and how the analysis is done once the data is collected.

1. STEP Project Global Consortium / KPMG 2019 Global Family Business survey

This research utilized quantitative methods to identify ways that kept the family businesses sustainably profitable with time.

The study also identified the ways in which the family business behavior changed with demographic changes and had “why” and “how” questions. Their qualitative research methods allowed the KPMG team to dig deeper into the mindsets and perspectives of the business owners and uncover unexpected research avenues as well.

It was a joint effort in which STEP Project Global Consortium collected 26 cases, and KPMG collected 11 cases.

The research reached the stage of data analysis in 2020, and the analysis process spanned over 4 stages.

The results, which were also the reasons why family businesses tend to lose their strength with time, were found to be:

• Family governance
• Family business legacy

2. EY Seren Teams Research 2020

This is yet another commendable example of qualitative research where the EY Seren Team digs into the unexplored depths of human behavior and how it affected their brand or service expectations.

The research was done across 200+ sources and involved in-depth virtual interviews with people in their homes, exploring their current needs and wishes. It also involved diary studies across the entire UK customer base to analyze human behavior changes and patterns.

The study also included interviews with professionals and design leaders from a wide range of industries to explore how COVID-19 transformed their industries. Finally, quantitative surveys were conducted to gain insights into the EY community after every 15 days.

The insights and results were:

• A culture of fear, daily resilience, and hopes for a better world and a better life – these were the macro trends.
• People felt massive digitization to be a resourceful yet demanding aspect as they have to adapt every day.
• Some people wished to have a new world with lots of possibilities, and some were looking for a new purpose.

8 Best Practices to Conduct Quantitative Research

Here are some best practices to keep in mind while conducting quantitative research:

1. Define Research Objectives

There can be many ways to collect data via quantitative research methods that are chosen as per the research objective and scope. These methods allow you to build your own observations regarding any hypotheses – unknown, entirely new, or unexplained. 

You can hypothesize a proof and build a prediction of outcomes supporting the same. You can also create a detailed stepwise plan for data collection, analysis, and testing. 

Below, we explore quantitative research methods and discuss some examples to enhance your understanding of them.

2. Keep Your Questions Simple

The surveys are meant to reach people en-masse, and that includes a wide demographic range with recipients from all walks of life. Asking simple questions will ensure that they grasp what’s being asked easily.

3. Develop a Solid Research Design

Choose an appropriate research design that aligns with your objectives, whether it’s experimental, quasi-experimental, or correlational. You also need to pay attention to the sample size and sampling technique such that it represents the target population accurately.

4. Use Reliable & Valid Instruments

It’s crucial to select or develop measurement instruments such as questionnaires, scales, or tests that have been validated and are reliable. Before proceeding with the main study, pilot-test these instruments on a small sample to assess their effectiveness and make any necessary improvements.

5. Ensure Data Quality

Implement data collection protocols to minimize errors and bias during data gathering. Double-check data entries and cleaning procedures to eliminate any inconsistencies or missing values that may affect the accuracy of your results. For instance, you might regularly cross-verify data entries to identify and correct any discrepancies.

6. Employ Appropriate Data Analysis Techniques

Select statistical methods that match the nature of your data and research questions. Whether it’s regression analysis, t-tests, ANOVA, or other techniques, using the right approach is important for drawing meaningful conclusions. Utilize software tools like SPSS or R for data analysis to ensure the accuracy and reproducibility of your findings.

7. Interpret Results Objectively

Present your findings in a clear and unbiased manner. Avoid making unwarranted causal claims, especially in correlational studies. Instead, focus on describing the relationships and patterns observed in your data.

8. Address Ethical Considerations

Prioritize ethical considerations throughout your research process. Obtain informed consent from participants, ensuring their voluntary participation and confidentiality of data. Comply with ethical guidelines and gain approval from a governing body if necessary.

Enhance Your Quantitative Research With Cutting-Edge Software

While no single research methodology can produce 100% reliable results, you can always opt for a hybrid research method by opting for the methods that are most relevant to your objective.

This understanding comes gradually as you learn how to implement the correct combination of qualitative and quantitative research methods for your research projects. For the best results, we recommend investing in smart, efficient, and scalable research tools that come with delightful reporting and advanced analytics to make every research initiative a success.

These software tools, such as ProProfs Survey Maker, come with pre-built survey templates and question libraries and allow you to create a high-converting survey in just a few minutes.

So, choose the best research partner, create the right research plan, and gather insights that drive sustainable growth for your business.

About the author

Emma David is a seasoned market research professional with 8+ years of experience. Having kick-started her journey in research, she has developed rich expertise in employee engagement, survey creation and administration, and data management. Emma believes in the power of data to shape business performance positively. She continues to help brands and businesses make strategic decisions and improve their market standing through her understanding of research methodologies.

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Quantitative research: Definition, characteristics, benefits, limitations, and best practices

Quantitative research characteristics

Benefits and limitations, best practices for quantitative research.

Researchers use different research methods as research is carried out for various purposes. Two main forms of research, qualitative and quantitative, are widely used in different fields. While qualitative research involves using non-numeric data, quantitative research is the opposite and utilizes non-numeric data. Although quantitative research data may not offer deeper insights into the issue, it is the best practice in some instances, especially if you need to collect data from a large sample group. Quantitative research is used in various fields, including sociology, politics, psychology, healthcare, education, economics, and marketing.

Earl R. Babbie notes: "Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques. Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon."

Below are some of the characteristics of quantitative research.

Large sample size

The ability to use larger sample sizes is undoubtedly one of the biggest perks of quantitative research.

Measurability

Due to its quantitative nature, the data gathered through quantitative data collection methods is easily measurable.

Close-ended questions

Quantitative research utilizes close-ended questions, which can be both beneficial and disadvantageous.

Reusability

Since it doesn't involve open-ended questions, quantitative research results can be used in other similar research projects.

Reliability

Quantitative data is considered more reliable since it is usually free of researcher bias.

Generalization

Quantitative research uses larger sample sizes, so it is assumed that it can be generalized easily.

Since quantitative research relies on data that can be measured, there are a lot of benefits offered by quantitative methods.

Quantitative research benefits

• Easier to analyze

Analyzing numeric data is easier; in that context, quantitative research can bring large amounts of data in a short period. There is numerous quantitative data analysis software that lets the researcher analyze the data fast.

• Allows using large sample sizes

Quantitative research involves using close-ended questions or simple "yes and no" questions. Therefore, it is easier to analyze quantitative data. In that sense, it can be distributed to practically as many people as you can. A large sample size usually means more accurate research results.

• More engaging

As quantitative research questions don't feature open-ended questions, participants are more eager to respond to questions. With open-ended qualitative questions, participants sometimes need to write a wall of text, and that is undesirable for many of them. It is easier to choose "yes or no" as it doesn't require much effort. A more engaging research survey means more feedback.  

• Less biased and more accurate

Qualitative research uses open-ended questions, and since the feedback is often open to interpretation, researchers might be biased when analyzing the data. That is not the case with quantitative research, as it involves answers to preset questions. Less biased data means more accurate data.

• Needs less time and effort

In all stages of research, quantitative research requires much less time and effort when compared with qualitative research. With different software, it is possible to create, send and analyze a huge volume of quantitative data in just a few clicks. Unlike qualitative in-depth interviews that usually require participants to be in a specific office, quantitative research isn't geographically bound to any location and can be carried out online.

Quantitative research limitations

• Limited information on the subject - 

Using close-ended questions means there isn't much to interpret. It doesn't allow the researcher to get answers to "why" questions. If you want to get in-depth information on the subject, you need to carry out qualitative research.

• Can be costly

Although it allows the researcher to reach a higher sample size, finding a large number of participants is expensive, considering you have to pay each participant.

• Difficulty in confirming the feedback

Quantitative research doesn't usually involve observing participants or talking with them about their answers; therefore, it is difficult to guess if the data gathered from them is accurate all the time. With qualitative methods, you get a chance to observe participants and ask follow-up questions to confirm their answers.

What kind of research do you need?

It may sound too obvious, but you may want to think about the type of research you need to carry out before you start with one. Sometimes quantitative research is not the best practice for a given subject, and you may need to go with qualitative research.  

Clear research goals

Setting a research goal is the first thing every researcher does before setting out to carry out actual research. The success of the research hugely depends on the clearly defined research goals. In other words, it's a make or break point for most research projects. Having confusing research goals is what usually fails the entire project and results in a loss of time and money.

Use user-friendly structure

When creating your surveys and questionnaires, use a user-friendly layout and keep it simple, so it's more engaging for the users. A lot of software offers simple survey templates that you can use effectively.

Choose the right sample

Although quantitative research allows the research to use large sample sizes, it is essential to choose the right sample group. The sample group you're trying to get feedback from may not represent your target audience. Therefore, think twice before allocating resources to gathering data from them.

Pay attention to questions

Quantitative research uses closed-ended questions, which means you need to be very careful with the questions you choose. One of the benefits of quantitative research is that it gives you the ability to predetermine the questions, so you need to use this chance and think about the best possible questions you may use for a better result. With quantitative research questions, you usually don't get a chance to ask follow-up questions.

Let your bias out of the research

We already mentioned that quantitative research is less biased than qualitative research, but it doesn't mean that it's completely free of bias. In this form of research, bias comes with specifically designed questions. The researcher may frame the questions in a way that the feedback may reflect what the researcher wants. In that sense, it is important to leave all the biased questions out you feel can alter the end result of the research.

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Home Market Research

Quantitative Research: What It Is, Practices & Methods

Quantitative research involves analyzing and gathering numerical data to uncover trends, calculate averages, evaluate relationships, and derive overarching insights. It’s used in various fields, including the natural and social sciences. Quantitative data analysis employs statistical techniques for processing and interpreting numeric data.

Research designs in the quantitative realm outline how data will be collected and analyzed with methods like experiments and surveys. Qualitative methods complement quantitative research by focusing on non-numerical data, adding depth to understanding. Data collection methods can be qualitative or quantitative, depending on research goals. Researchers often use a combination of both approaches to gain a comprehensive understanding of phenomena.

What is Quantitative Research?

Quantitative research is a systematic investigation of phenomena by gathering quantifiable data and performing statistical, mathematical, or computational techniques. Quantitative research collects statistically significant information from existing and potential customers using sampling methods and sending out online surveys , online polls , and questionnaires , for example.

One of the main characteristics of this type of research is that the results can be depicted in numerical form. After carefully collecting structured observations and understanding these numbers, it’s possible to predict the future of a product or service, establish causal relationships or Causal Research , and make changes accordingly. Quantitative research primarily centers on the analysis of numerical data and utilizes inferential statistics to derive conclusions that can be extrapolated to the broader population.

An example of a quantitative research study is the survey conducted to understand how long a doctor takes to tend to a patient when the patient walks into the hospital. A patient satisfaction survey can be administered to ask questions like how long a doctor takes to see a patient, how often a patient walks into a hospital, and other such questions, which are dependent variables in the research. This kind of research method is often employed in the social sciences, and it involves using mathematical frameworks and theories to effectively present data, ensuring that the results are logical, statistically sound, and unbiased.

Data collection in quantitative research uses a structured method and is typically conducted on larger samples representing the entire population. Researchers use quantitative methods to collect numerical data, which is then subjected to statistical analysis to determine statistically significant findings. This approach is valuable in both experimental research and social research, as it helps in making informed decisions and drawing reliable conclusions based on quantitative data.

Quantitative Research Characteristics

Quantitative research has several unique characteristics that make it well-suited for specific projects. Let’s explore the most crucial of these characteristics so that you can consider them when planning your next research project:

• Structured tools: Quantitative research relies on structured tools such as surveys, polls, or questionnaires to gather quantitative data . Using such structured methods helps collect in-depth and actionable numerical data from the survey respondents, making it easier to perform data analysis.
• Sample size: Quantitative research is conducted on a significant sample size  representing the target market . Appropriate Survey Sampling methods, a fundamental aspect of quantitative research methods, must be employed when deriving the sample to fortify the research objective and ensure the reliability of the results.
• Close-ended questions: Closed-ended questions , specifically designed to align with the research objectives, are a cornerstone of quantitative research. These questions facilitate the collection of quantitative data and are extensively used in data collection processes.
• Prior studies: Before collecting feedback from respondents, researchers often delve into previous studies related to the research topic. This preliminary research helps frame the study effectively and ensures the data collection process is well-informed.
• Quantitative data: Typically, quantitative data is represented using tables, charts, graphs, or other numerical forms. This visual representation aids in understanding the collected data and is essential for rigorous data analysis, a key component of quantitative research methods.
• Generalization of results: One of the strengths of quantitative research is its ability to generalize results to the entire population. It means that the findings derived from a sample can be extrapolated to make informed decisions and take appropriate actions for improvement based on numerical data analysis.

Quantitative Research Methods

Quantitative research methods are systematic approaches used to gather and analyze numerical data to understand and draw conclusions about a phenomenon or population. Here are the quantitative research methods:

• Primary quantitative research methods
• Secondary quantitative research methods

Primary Quantitative Research Methods

Primary quantitative research is the most widely used method of conducting market research. The distinct feature of primary research is that the researcher focuses on collecting data directly rather than depending on data collected from previously done research. Primary quantitative research design can be broken down into three further distinctive tracks and the process flow. They are:

A. Techniques and Types of Studies

There are multiple types of primary quantitative research. They can be distinguished into the four following distinctive methods, which are:

01. Survey Research

Survey Research is fundamental for all quantitative outcome research methodologies and studies. Surveys are used to ask questions to a sample of respondents, using various types such as online polls, online surveys, paper questionnaires, web-intercept surveys , etc. Every small and big organization intends to understand what their customers think about their products and services, how well new features are faring in the market, and other such details.

By conducting survey research, an organization can ask multiple survey questions , collect data from a pool of customers, and analyze this collected data to produce numerical results. It is the first step towards collecting data for any research. You can use single ease questions . A single-ease question is a straightforward query that elicits a concise and uncomplicated response.

This type of research can be conducted with a specific target audience group and also can be conducted across multiple groups along with comparative analysis . A prerequisite for this type of research is that the sample of respondents must have randomly selected members. This way, a researcher can easily maintain the accuracy of the obtained results as a huge variety of respondents will be addressed using random selection. 

Traditionally, survey research was conducted face-to-face or via phone calls. Still, with the progress made by online mediums such as email or social media, survey research has also spread to online mediums.There are two types of surveys , either of which can be chosen based on the time in hand and the kind of data required:

Cross-sectional surveys: Cross-sectional surveys are observational surveys conducted in situations where the researcher intends to collect data from a sample of the target population at a given point in time. Researchers can evaluate various variables at a particular time. Data gathered using this type of survey is from people who depict similarity in all variables except the variables which are considered for research . Throughout the survey, this one variable will stay constant.

• Cross-sectional surveys are popular with retail, SMEs, and healthcare industries. Information is garnered without modifying any parameters in the variable ecosystem.
• Multiple samples can be analyzed and compared using a cross-sectional survey research method.
• Multiple variables can be evaluated using this type of survey research.
• The only disadvantage of cross-sectional surveys is that the cause-effect relationship of variables cannot be established as it usually evaluates variables at a particular time and not across a continuous time frame.

Longitudinal surveys: Longitudinal surveys are also observational surveys , but unlike cross-sectional surveys, longitudinal surveys are conducted across various time durations to observe a change in respondent behavior and thought processes. This time can be days, months, years, or even decades. For instance, a researcher planning to analyze the change in buying habits of teenagers over 5 years will conduct longitudinal surveys.

• In cross-sectional surveys, the same variables were evaluated at a given time, and in longitudinal surveys, different variables can be analyzed at different intervals.
• Longitudinal surveys are extensively used in the field of medicine and applied sciences. Apart from these two fields, they are also used to observe a change in the market trend analysis , analyze customer satisfaction, or gain feedback on products/services.
• In situations where the sequence of events is highly essential, longitudinal surveys are used.
• Researchers say that when research subjects need to be thoroughly inspected before concluding, they rely on longitudinal surveys.

02. Correlational Research

A comparison between two entities is invariable. Correlation research is conducted to establish a relationship between two closely-knit entities and how one impacts the other, and what changes are eventually observed. This research method is carried out to give value to naturally occurring relationships, and a minimum of two different groups are required to conduct this quantitative research method successfully. Without assuming various aspects, a relationship between two groups or entities must be established.

Researchers use this quantitative research design to correlate two or more variables using mathematical analysis methods. Patterns, relationships, and trends between variables are concluded as they exist in their original setup. The impact of one of these variables on the other is observed, along with how it changes the relationship between the two variables. Researchers tend to manipulate one of the variables to attain the desired results.

Ideally, it is advised not to make conclusions merely based on correlational research. This is because it is not mandatory that if two variables are in sync that they are interrelated.

Example of Correlational Research Questions :

• The relationship between stress and depression.
• The equation between fame and money.
• The relation between activities in a third-grade class and its students.

03. Causal-comparative Research

This research method mainly depends on the factor of comparison. Also called quasi-experimental research , this quantitative research method is used by researchers to conclude the cause-effect equation between two or more variables, where one variable is dependent on the other independent variable. The independent variable is established but not manipulated, and its impact on the dependent variable is observed. These variables or groups must be formed as they exist in the natural setup. As the dependent and independent variables will always exist in a group, it is advised that the conclusions are carefully established by keeping all the factors in mind.

Causal-comparative research is not restricted to the statistical analysis of two variables but extends to analyzing how various variables or groups change under the influence of the same changes. This research is conducted irrespective of the type of relationship that exists between two or more variables. Statistical analysis plan is used to present the outcome using this quantitative research method.

Example of Causal-Comparative Research Questions:

• The impact of drugs on a teenager. The effect of good education on a freshman. The effect of substantial food provision in the villages of Africa.

04. Experimental Research

Also known as true experimentation, this research method relies on a theory. As the name suggests, experimental research is usually based on one or more theories. This theory has yet to be proven before and is merely a supposition. In experimental research, an analysis is done around proving or disproving the statement. This research method is used in natural sciences. Traditional research methods are more effective than modern techniques.

There can be multiple theories in experimental research. A theory is a statement that can be verified or refuted.

After establishing the statement, efforts are made to understand whether it is valid or invalid. This quantitative research method is mainly used in natural or social sciences as various statements must be proved right or wrong.

• Traditional research methods are more effective than modern techniques.
• Systematic teaching schedules help children who struggle to cope with the course.
• It is a boon to have responsible nursing staff for ailing parents.

B. Data Collection Methodologies

The second major step in primary quantitative research is data collection. Data collection can be divided into sampling methods and data collection using surveys and polls.

01. Data Collection Methodologies: Sampling Methods

There are two main sampling methods for quantitative research: Probability and Non-probability sampling .

Probability sampling: A theory of probability is used to filter individuals from a population and create samples in probability sampling . Participants of a sample are chosen by random selection processes. Each target audience member has an equal opportunity to be selected in the sample.

There are four main types of probability sampling:

• Simple random sampling: As the name indicates, simple random sampling is nothing but a random selection of elements for a sample. This sampling technique is implemented where the target population is considerably large.
• Stratified random sampling: In the stratified random sampling method , a large population is divided into groups (strata), and members of a sample are chosen randomly from these strata. The various segregated strata should ideally not overlap one another.
• Cluster sampling: Cluster sampling is a probability sampling method using which the main segment is divided into clusters, usually using geographic segmentation and demographic segmentation parameters.
• Systematic sampling: Systematic sampling is a technique where the starting point of the sample is chosen randomly, and all the other elements are chosen using a fixed interval. This interval is calculated by dividing the population size by the target sample size.

Non-probability sampling: Non-probability sampling is where the researcher’s knowledge and experience are used to create samples. Because of the researcher’s involvement, not all the target population members have an equal probability of being selected to be a part of a sample.

There are five non-probability sampling models:

• Convenience sampling: In convenience sampling , elements of a sample are chosen only due to one prime reason: their proximity to the researcher. These samples are quick and easy to implement as there is no other parameter of selection involved.
• Consecutive sampling: Consecutive sampling is quite similar to convenience sampling, except for the fact that researchers can choose a single element or a group of samples and conduct research consecutively over a significant period and then perform the same process with other samples.
• Quota sampling: Using quota sampling , researchers can select elements using their knowledge of target traits and personalities to form strata. Members of various strata can then be chosen to be a part of the sample as per the researcher’s understanding.
• Snowball sampling: Snowball sampling is conducted with target audiences who are difficult to contact and get information. It is popular in cases where the target audience for analysis research is rare to put together.
• Judgmental sampling: Judgmental sampling is a non-probability sampling method where samples are created only based on the researcher’s experience and research skill .

02. Data collection methodologies: Using surveys & polls

Once the sample is determined, then either surveys or polls can be distributed to collect the data for quantitative research.

Using surveys for primary quantitative research

A survey is defined as a research method used for collecting data from a pre-defined group of respondents to gain information and insights on various topics of interest. The ease of survey distribution and the wide number of people it can reach depending on the research time and objective makes it one of the most important aspects of conducting quantitative research.

Fundamental levels of measurement – nominal, ordinal, interval, and ratio scales

Four measurement scales are fundamental to creating a multiple-choice question in a survey. They are nominal, ordinal, interval, and ratio measurement scales without the fundamentals of which no multiple-choice questions can be created. Hence, it is crucial to understand these measurement levels to develop a robust survey.

Use of different question types

To conduct quantitative research, close-ended questions must be used in a survey. They can be a mix of multiple question types, including multiple-choice questions like semantic differential scale questions , rating scale questions , etc.

Survey Distribution and Survey Data Collection

In the above, we have seen the process of building a survey along with the research design to conduct primary quantitative research. Survey distribution to collect data is the other important aspect of the survey process. There are different ways of survey distribution. Some of the most commonly used methods are:

• Email: Sending a survey via email is the most widely used and effective survey distribution method. This method’s response rate is high because the respondents know your brand. You can use the QuestionPro email management feature to send out and collect survey responses.
• Buy respondents: Another effective way to distribute a survey and conduct primary quantitative research is to use a sample. Since the respondents are knowledgeable and are on the panel by their own will, responses are much higher.
• Embed survey on a website: Embedding a survey on a website increases a high number of responses as the respondent is already in close proximity to the brand when the survey pops up.
• Social distribution: Using social media to distribute the survey aids in collecting a higher number of responses from the people that are aware of the brand.
• QR code: QuestionPro QR codes store the URL for the survey. You can print/publish this code in magazines, signs, business cards, or on just about any object/medium.
• SMS survey: The SMS survey is a quick and time-effective way to collect a high number of responses.
• Offline Survey App: The QuestionPro App allows users to circulate surveys quickly, and the responses can be collected both online and offline.

Survey example

An example of a survey is a short customer satisfaction (CSAT) survey that can quickly be built and deployed to collect feedback about what the customer thinks about a brand and how satisfied and referenceable the brand is.

Using polls for primary quantitative research

Polls are a method to collect feedback using close-ended questions from a sample. The most commonly used types of polls are election polls and exit polls . Both of these are used to collect data from a large sample size but using basic question types like multiple-choice questions.

C. Data Analysis Techniques

The third aspect of primary quantitative research design is data analysis . After collecting raw data, there must be an analysis of this data to derive statistical inferences from this research. It is important to relate the results to the research objective and establish the statistical relevance of the results.

Remember to consider aspects of research that were not considered for the data collection process and report the difference between what was planned vs. what was actually executed.

It is then required to select precise Statistical Analysis Methods , such as SWOT, Conjoint, Cross-tabulation, etc., to analyze the quantitative data.

• SWOT analysis: SWOT Analysis stands for the acronym of Strengths, Weaknesses, Opportunities, and Threat analysis. Organizations use this statistical analysis technique to evaluate their performance internally and externally to develop effective strategies for improvement.
• Conjoint Analysis: Conjoint Analysis is a market analysis method to learn how individuals make complicated purchasing decisions. Trade-offs are involved in an individual’s daily activities, and these reflect their ability to decide from a complex list of product/service options.
• Cross-tabulation: Cross-tabulation is one of the preliminary statistical market analysis methods which establishes relationships, patterns, and trends within the various parameters of the research study.
• TURF Analysis: TURF Analysis , an acronym for Totally Unduplicated Reach and Frequency Analysis, is executed in situations where the reach of a favorable communication source is to be analyzed along with the frequency of this communication. It is used for understanding the potential of a target market.

Inferential statistics methods such as confidence interval, the margin of error, etc., can then be used to provide results.

Secondary Quantitative Research Methods

Secondary quantitative research or desk research is a research method that involves using already existing data or secondary data. Existing data is summarized and collated to increase the overall effectiveness of the research.

This research method involves collecting quantitative data from existing data sources like the internet, government resources, libraries, research reports, etc. Secondary quantitative research helps to validate the data collected from primary quantitative research and aid in strengthening or proving, or disproving previously collected data.

The following are five popularly used secondary quantitative research methods:

• Data available on the internet: With the high penetration of the internet and mobile devices, it has become increasingly easy to conduct quantitative research using the internet. Information about most research topics is available online, and this aids in boosting the validity of primary quantitative data.
• Government and non-government sources: Secondary quantitative research can also be conducted with the help of government and non-government sources that deal with market research reports. This data is highly reliable and in-depth and hence, can be used to increase the validity of quantitative research design.
• Public libraries: Now a sparingly used method of conducting quantitative research, it is still a reliable source of information, though. Public libraries have copies of important research that was conducted earlier. They are a storehouse of valuable information and documents from which information can be extracted.
• Educational institutions: Educational institutions conduct in-depth research on multiple topics, and hence, the reports that they publish are an important source of validation in quantitative research.
• Commercial information sources: Local newspapers, journals, magazines, radio, and TV stations are great sources to obtain data for secondary quantitative research. These commercial information sources have in-depth, first-hand information on market research, demographic segmentation, and similar subjects.

Quantitative Research Examples

Some examples of quantitative research are:

• A customer satisfaction template can be used if any organization would like to conduct a customer satisfaction (CSAT) survey . Through this kind of survey, an organization can collect quantitative data and metrics on the goodwill of the brand or organization in the customer’s mind based on multiple parameters such as product quality, pricing, customer experience, etc. This data can be collected by asking a net promoter score (NPS) question , matrix table questions, etc. that provide data in the form of numbers that can be analyzed and worked upon.
• Another example of quantitative research is an organization that conducts an event, collecting feedback from attendees about the value they see from the event. By using an event survey , the organization can collect actionable feedback about the satisfaction levels of customers during various phases of the event such as the sales, pre and post-event, the likelihood of recommending the organization to their friends and colleagues, hotel preferences for the future events and other such questions.

What are the Advantages of Quantitative Research?

There are many advantages to quantitative research. Some of the major advantages of why researchers use this method in market research are:

Collect Reliable and Accurate Data:

Quantitative research is a powerful method for collecting reliable and accurate quantitative data. Since data is collected, analyzed, and presented in numbers, the results obtained are incredibly reliable and objective. Numbers do not lie and offer an honest and precise picture of the conducted research without discrepancies. In situations where a researcher aims to eliminate bias and predict potential conflicts, quantitative research is the method of choice.

Quick Data Collection:

Quantitative research involves studying a group of people representing a larger population. Researchers use a survey or another quantitative research method to efficiently gather information from these participants, making the process of analyzing the data and identifying patterns faster and more manageable through the use of statistical analysis. This advantage makes quantitative research an attractive option for projects with time constraints.

Wider Scope of Data Analysis:

Quantitative research, thanks to its utilization of statistical methods, offers an extensive range of data collection and analysis. Researchers can delve into a broader spectrum of variables and relationships within the data, enabling a more thorough comprehension of the subject under investigation. This expanded scope is precious when dealing with complex research questions that require in-depth numerical analysis.

Eliminate Bias:

One of the significant advantages of quantitative research is its ability to eliminate bias. This research method leaves no room for personal comments or the biasing of results, as the findings are presented in numerical form. This objectivity makes the results fair and reliable in most cases, reducing the potential for researcher bias or subjectivity.

In summary, quantitative research involves collecting, analyzing, and presenting quantitative data using statistical analysis. It offers numerous advantages, including the collection of reliable and accurate data, quick data collection, a broader scope of data analysis, and the elimination of bias, making it a valuable approach in the field of research. When considering the benefits of quantitative research, it’s essential to recognize its strengths in contrast to qualitative methods and its role in collecting and analyzing numerical data for a more comprehensive understanding of research topics.

Best Practices to Conduct Quantitative Research

Here are some best practices for conducting quantitative research:

• Differentiate between quantitative and qualitative: Understand the difference between the two methodologies and apply the one that suits your needs best.
• Choose a suitable sample size: Ensure that you have a sample representative of your population and large enough to be statistically weighty.
• Keep your research goals clear and concise: Know your research goals before you begin data collection to ensure you collect the right amount and the right quantity of data.
• Keep the questions simple: Remember that you will be reaching out to a demographically wide audience. Pose simple questions for your respondents to understand easily.

Quantitative Research vs Qualitative Research

Quantitative research and qualitative research are two distinct approaches to conducting research, each with its own set of methods and objectives. Here’s a comparison of the two:

Quantitative Research

• Objective: The primary goal of quantitative research is to quantify and measure phenomena by collecting numerical data. It aims to test hypotheses, establish patterns, and generalize findings to a larger population.
• Data Collection: Quantitative research employs systematic and standardized approaches for data collection, including techniques like surveys, experiments, and observations that involve predefined variables. It is often collected from a large and representative sample.
• Data Analysis: Data is analyzed using statistical techniques, such as descriptive statistics, inferential statistics, and mathematical modeling. Researchers use statistical tests to draw conclusions and make generalizations based on numerical data.
• Sample Size: Quantitative research often involves larger sample sizes to ensure statistical significance and generalizability.
• Results: The results are typically presented in tables, charts, and statistical summaries, making them highly structured and objective.
• Generalizability: Researchers intentionally structure quantitative research to generate outcomes that can be helpful to a larger population, and they frequently seek to establish causative connections.
• Emphasis on Objectivity: Researchers aim to minimize bias and subjectivity, focusing on replicable and objective findings.

Qualitative Research

• Objective: Qualitative research seeks to gain a deeper understanding of the underlying motivations, behaviors, and experiences of individuals or groups. It explores the context and meaning of phenomena.
• Data Collection: Qualitative research employs adaptable and open-ended techniques for data collection, including methods like interviews, focus groups, observations, and content analysis. It allows participants to express their perspectives in their own words.
• Data Analysis: Data is analyzed through thematic analysis, content analysis, or grounded theory. Researchers focus on identifying patterns, themes, and insights in the data.
• Sample Size: Qualitative research typically involves smaller sample sizes due to the in-depth nature of data collection and analysis.
• Results: Findings are presented in narrative form, often in the participants’ own words. Results are subjective, context-dependent, and provide rich, detailed descriptions.
• Generalizability: Qualitative research does not aim for broad generalizability but focuses on in-depth exploration within a specific context. It provides a detailed understanding of a particular group or situation.
• Emphasis on Subjectivity: Researchers acknowledge the role of subjectivity and the researcher’s influence on the Research Process . Participant perspectives and experiences are central to the findings.

Researchers choose between quantitative and qualitative research methods based on their research objectives and the nature of the research question. Each approach has its advantages and drawbacks, and the decision between them hinges on the particular research objectives and the data needed to address research inquiries effectively.

Quantitative research is a structured way of collecting and analyzing data from various sources. Its purpose is to quantify the problem and understand its extent, seeking results that someone can project to a larger population.

Companies that use quantitative rather than qualitative research typically aim to measure magnitudes and seek objectively interpreted statistical results. So if you want to obtain quantitative data that helps you define the structured cause-and-effect relationship between the research problem and the factors, you should opt for this type of research.

At QuestionPro , we have various Best Data Collection Tools and features to conduct investigations of this type. You can create questionnaires and distribute them through our various methods. We also have sample services or various questions to guarantee the success of your study and the quality of the collected data.

Quantitative research is a systematic and structured approach to studying phenomena that involves the collection of measurable data and the application of statistical, mathematical, or computational techniques for analysis.

Quantitative research is characterized by structured tools like surveys, substantial sample sizes, closed-ended questions, reliance on prior studies, data presented numerically, and the ability to generalize findings to the broader population.

The two main methods of quantitative research are Primary quantitative research methods, involving data collection directly from sources, and Secondary quantitative research methods, which utilize existing data for analysis.

1.Surveying to measure employee engagement with numerical rating scales. 2.Analyzing sales data to identify trends in product demand and market share. 4.Examining test scores to assess the impact of a new teaching method on student performance. 4.Using website analytics to track user behavior and conversion rates for an online store.

1.Differentiate between quantitative and qualitative approaches. 2.Choose a representative sample size. 3.Define clear research goals before data collection. 4.Use simple and easily understandable survey questions.

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Methodology

• What Is Quantitative Observation? | Definition & Examples

What Is Quantitative Observation? | Definition & Examples

Published on March 24, 2023 by Tegan George . Revised on June 22, 2023.

Quantitative observation is a research method that involves measuring and quantifying characteristics of a phenomenon. It hinges upon gathering numerical data, such as measurements or counts, that can be expressed in terms of a quantitative value.

Measuring the length of a flower’s stem, counting the number of bees in a hive, or recording the temperature of a greenhouse are all examples of quantitative observations. These types of observations are typically objective, meaning that they can be replicated and verified by other observers using the same measurement techniques.

Quantitative observations are often used in scientific research for data collection and hypothesis testing , but they are also commonly used in everyday life to help make decisions or solve problems based on numerical information.

Table of contents

When to use quantitative observation, examples of quantitative observation, types of quantitative observations, advantages and disadvantages of quantitative observations, other interesting articles, frequently asked questions about quantitative observations.

Quantitative observation is a type of observational study . It is often used to gather and analyze numerical data to answer a research question or to make informed decisions.

A quantitative observation could be a good fit for your research if:

• You want to measure the effectiveness of an intervention. Quantitative observation can be used to determine whether a specific intervention or treatment has a measurable impact on a particular outcome, such as changes in health status, academic performance, or work productivity.
• You want to make informed decisions about a given phenomenon. Quantitative observation can provide numerical data that can be used to make informed decisions in various fields such as finance, marketing, and public policy.
• You want to compare groups or populations at a macro level. Quantitative observation can be used to compare groups or populations based on numerical data, such as income, education level, or health outcomes. It can also be used to identify patterns and trends in data over time or across different groups or populations.

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Quantitative observation is a great starting method to measure the effects of an input on a phenomenon.

Quantitative observation is often also used to compare the effectiveness of an intervention.

Two groups of students will be randomly assigned : one group (the control group ) will receive instruction using the traditional teaching method, while the other group will receive instruction using the new teaching method. The same test will be given to both groups before and after the instruction period.

There are several types of quantitative observation. Here are some of the most common ones to help you choose the best fit for your research.

Overall, quantitative observation research projects can provide a structured and rigorous approach to gathering and analyzing data, which can lead to more objective and precise results. However, while there are many advantages to using this method, there are also several potential disadvantages.

Advantages of quantitative observations

• Quantitative observations are objective and systematic . As they hinge upon the collection of numerical data that can be analyzed using statistical methods , this allows for an objective and systematic approach to data analysis. This helps to ensure the reliability and validity of the results.
• Quantitative observation research can fairly easily incorporate large sample sizes , which allows for a broader representation of the population being studied. This can increase the generalizability of the results.
• Quantitative observation research projects can be easily replicated , which allows other researchers to test the validity of the results and to build upon the research.

Disadvantages of quantitative observations

• Quantitative observations suffer from limited scope , as they only measure variables that can be quantified and/or standardized. This runs the risk of excluding important variables, in particular, ones that are difficult to quantify—such as emotions or personal experiences.
• Quantitative observation research projects may focus on numerical data at the expense of a more detailed understanding of the context in which the data was collected. This can result in an oversimplified or incomplete understanding of the phenomenon being studied. A reliance on standardized data collection methods can limit the ability to adapt to changing circumstances or unexpected results.
• Even though quantitative observation aims to be objective, there is still a risk of research bias introduced on the part of the researcher, such as selection bias , omitted variable bias , or information bias .

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

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.

Quantitative observations involve measuring or counting something and expressing the result in numerical form, while qualitative observations involve describing something in non-numerical terms, such as its appearance, texture, or color.

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 .

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What Are The Characteristics Of Quantitative Research? Characteristics Of Quantitative Research In A Nutshell

The characteristics of quantitative research contribute to methods that use statistics as the basis for making generalizations about something. These generalizations are constructed from data that is used to find patterns and averages and test causal relationships.

To assist in this process, key quantitative research characteristics include:

• The use of measurable variables.
• Standardized research instruments.
• Random sampling of participants.
• Data presentation in tables, graphs, or figures.
• The use of a repeatable method.
• The ability to predict outcomes and causal relationships.
• Close-ended questioning. 

Each characteristic also discriminates quantitative research from qualitative research, which involves the collecting and analyzing of non-numerical data such as text, video, or audio.

With that said, let’s now take a look at each of the characteristics in more detail.

But let’s first look at the importance of quantitative research and when it does matter!

Table of Contents

Importance of quantitative research

In the context of a business that wants to learn more about its market, customers, or competitors, quantitative research is a powerful tool that provides objective, data-based insights, trends, predictions, and patterns.

To clarify the importance of quantitative research as a method, we’ll discuss some of its key benefits to businesses below.

Before a company can develop a marketing strategy or even a single campaign, it must perform research to either confirm or deny a hypothesis it has around an ideal buyer or the target audience.

Before the proliferation of the internet, quantitative data collection was more cumbersome, less exhaustive, and normally occurred face to face.

Today, the ease with which companies can perform quantitative research is impressive – so much so that some would hesitate to even call it research.

Many businesses conduct questionnaires and surveys to have more control over how they test hypotheses, but any business with a Google Analytics account can passively collect data on key metrics such as bounce rate, discovery keywords, and value per visit.

The key thing to remember here is that there is little scope for uncertainty among the research data. Questionnaires ask closed-ended questions with no room for ambiguity and the validity of bounce rate data will never be up for debate.

Objective representation

Fundamentally speaking, quantitative research endeavors to establish the strength or significance of causal relationships.

There is an emphasis on objective measurement based on numerical, statistical, and mathematical data analysis or manipulation.

Quantitative research is also used to produce unbiased, logical, and statistical results that are representative of the population from which the sample is drawn.

In a marketer’s case, the population is usually the target audience of a product or service.

But in any case, organizations are dependent on quantitative data as it provides detailed, accurate, and relevant information on the problem at hand.

When it comes time to either prove or disprove the hypothesis, companies can either move forward with robust data or drop their current line of research and start afresh.

Versatility of quantitative statistical analysis

On the subject of proving a hypothesis are the statistical analyses a business must perform to arrive at the answer.

Fortunately, there are numerous techniques a company can employ depending on the context and the goals of the research. 

These include:

Conjoint analysis

Used to identify the value of attributes that influence purchase decisions, such as cost, benefits, or features.

Unsurprisingly, this analysis is used in product pricing, product launch, and market placement initiatives.

GAP analysis

An analysis that determines the discrepancy that exists between the actual and desired performance of a product or service.

MaxDiff analysis

A simpler version of the conjoint analysis that marketers use to analyze customer preferences related to brand image, preferences, activities, and also product features.

This is also known as “best-worst” scaling.

TURF analysis

TURF, which stands for total unduplicated reach and frequency, is used to ascertain the particular combination of products and services that will yield the highest number of sales.

The use of measurable variables

During quantitative research, data gathering instruments measure various characteristics of a population. 

These characteristics, which are called measurables in a study, may include age, economic status, or the number of dependents.

Standardized research instruments

Standardized and pre-tested data collection instruments include questionnaires, surveys, and polls. Alternatively, existing statistical data may be manipulated using computational techniques to yield new insights.

Standardization of research instruments ensures the data is accurate, valid, and reliable. Instruments should also be tested first to determine if study participant responses satisfy the intent of the research or its objectives.

Random sampling of participants

Quantitative data analysis assumes a normal distribution curve from a large population. 

Random sampling should be used to gather data, a technique in which each sample has an equal probability of being chosen. Randomly chosen samples are unbiased and are important in making statistical inferences and conclusions.

Here are a few random sampling techniques.

True random sampling

Some consider true random sampling to be the gold standard when it comes to probabilistic studies. While it may not be useful in every situation or context, it is one of the most useful for enormous databases.

The method involves assigning numbers to a population of available study participants and then having a random number generator select them. This ensures that each individual in a study pool has an equal chance of being solicited for feedback.

Systematic sampling

Systematic sampling is similar to true random sampling but is more suited to smaller populations. In this technique, the sample is selected by randomly choosing a starting point in the population and then selecting every n th individual after that. 

For example, if a researcher wanted to sample every twentieth person from a list of customers, they would randomly select one customer as the starting point and then sample every twentieth customer thereafter.

Cluster sampling

In cluster sampling, the population is divided into clusters or groups and a random sample of clusters is selected. After which, all members of the selected clusters are included in the sample. 

If a HR team wanted to survey employees of a large organization, they might randomly select several departments as clusters, and then survey all the employees within those departments.

Cluster sampling can also be useful for businesses that have customers or products distributed over wide geographic areas.

To that end, cluster sampling is often used when the population is too large or too dispersed to sample individually. While it may be more efficient to sample clusters, the approach may be less precise if there is variability between them.

Data presentation in tables, graphs, and figures

The results of quantitative research can sometimes be difficult to decipher, particularly for those not involved in the research process.

Tables, graphs, and figures help synthesize the data in a way that is understandable for key stakeholders. They should demonstrate or define relationships, trends, or differences in the data presented.

Take McKinsey Global Institute (MGI), for example, the business and research arm of McKinsey & Company.

Established in 1990, MGI combines the disciplines of economics and management to examine the macroeconomic forces that influence business strategy and public policy. 

Based on this analysis , MGI periodically releases reports covering more than 20 countries and 30 industries around six key themes: natural resources, labor markets, productivity and growth , the evolution of global financial markets, the economic impact of technology and innovation , and urbanization.

MGI’s mission is to “ provide leaders in the commercial, public, and social sectors with the facts and insights on which to base management and policy decisions .” To carry out this mission , McKinsey’s data presentation is key. 

In one article that argued against the deglobalization trend , McKinsey skilfully used graphs and bar charts to synthesize quantitative data related to the global flow of intangibles, services, and students.

The company also used an 80-cell matrix and color-coded scale to show the share of domestic consumption met by inflows for various geographic regions.

The use of a repeatable method

Quantitative research methods should be repeatable.

This means the method can be applied by other researchers in a different context to verify or confirm a particular outcome.

Replicable research outcomes afford researchers greater confidence in the results. Replicability also reduces the chances that the research will be influenced by selection biases and confounding variables.

The ability to predict outcomes and causal relationships

Data analysis can be used to create formulas that predict outcomes and investigate causal relationships.

As hinted at earlier, data are also used to make broad or general inferences about a large population.

Causal relationships, in particular, can be described by so-called “if-then” scenarios, which can be modeled using complex, computer-driven mathematical functions.

Close-ended questioning

Lastly, quantitative research requires that the individuals running the study choose their questions wisely.

Since the study is based on quantitative data, it is imperative close-ended questions are asked.

These are questions that can only be answered by selecting from a limited number of options. 

Questions may be dichotomous, with a simple “yes” or “no” or “true” or “false” answer.

However, many studies also incorporate multiple-choice questions based on a rating scale, Likert scale, checklist, or order ranking system.

Sample size

Sample size is a critical consideration in quantitative research as it impacts the reliability of the results.

In business quantitative research, sample size refers to the number of participants or data points included in a study, and it is vital that the sample size is appropriate for the research questions being addressed.

A sample size that is too small can lead to unreliable conclusions since it will not accurately represent the study population.

Conversely, a sample size that is too large can lead to unnecessary expenses and time constraints.

In general, however, larger sample sizes tend to increase the precision and reliability of study conclusions.

This is because they reduce the impact of random variation and increase the power to detect statistically significant differences or relationships. However, larger sample sizes also require more resources and time to collect and analyze data.

As a consequence, it is important for businesses to select a sample size that balances factors such as the research question, population size, variability of the data, and statistical power.

Four real-world examples of quantitative research

Now that we’ve described some key quantitative research examples, let’s go ahead and look at some real-world examples.

1 – A Quantitative Study of the Impact of Social Media Reviews on Brand Perception

In 2015, Neha Joshi undertook quantitative research as part of her thesis at The City University of New York.

The thesis aimed to determine the impact of social media reviews on brand perception with a particular focus on YouTube and Yelp.

Joshi analyzed the impact of 942 separate YouTube smartphone reviews to develop a statistical model to predict audience response and engagement on any given video.

The wider implications of the study involved using customer reviews as a feedback mechanism to improve brand perception.

2 – A Quantitative Study of Teacher Perceptions of Professional Learning Communities’ Context, Process, and Content

Daniel R. Johnson from Seton Hall University in New Jersey, USA, analyzed the effectiveness of the teacher training model known as Professional Learning Communities (PLC).

Specifically, Johnson wanted to research the impact of the model as perceived by certified educators across three specific areas: content, process, and context.

There was a dire need for this research since there was little quantitative data on an approach that was becoming increasingly popular at the government, state, and district levels.

Data were collected using Standard Inventory Assessment (SAI) surveys which were online, anonymous, and incorporated a Likert scale response system.

3 – A Quantitative Study of Course Grades and Retention Comparing Online and Face-to-Face Classes

This research was performed by Vickie A. Kelly as part of her Doctor of Education in Educational Leadership at Baker University in Kansas, USA.

Kelly wanted to know whether distance education and Internet-driven instruction were as effective a learning tool when compared to traditional face-to-face instruction.

A total of 885 students were selected for the research sample to answer the following two questions:

• Is there a statistically significant difference between the grades of face-to-face students and the grades of online students?
• Is there a statistically significant difference between course content retention in face-to-face students and online students?

In both cases, there was no significant difference, which suggested that distance education as a learning tool was as effective as face-to-face education.

4 – A quantitative research of consumer’s attitude towards food products advertising

At the University of Bucharest, Romania, Mirela-Cristina Voicu wanted to research consumer attitudes toward traditional forms of advertising such as television, radio, and print.

She reasoned that consumer attitudes toward advertising impacted attitudes toward the product or brand itself, with a positive attitude potentially driving purchase intent.

To determine whether there was a link between these factors, 385 consumers in the Bucharest area were interviewed and asked to fill out a questionnaire.

Voicu ensured the sample was representative of the broader population in terms of two variables: age and gender.

The quantitative study results found that 70% of participants considered traditional forms of advertising to be saturated.

In other words, they did not have a positive attitude toward the advertised brand or product.

However, consumer attitudes toward  food advertising  were much more positive, with 61% of participants categorizing their attitudes as either favorable or very favorable in the questionnaire. 

Quantitative vs. Qualitative Research

As the story goes, “data is the new oil,” yes, but what data?

Indeed, while quantitative research can be extremely powerful, it must be balanced with qualitative research .

Several qualitative methods might help enrich the quantitative data.

It’s critical to understand that quantitative data might be very effective in the short term.

Yet, it might not tell us anything in the long term.

For that, we need to use human judgment, intuition, and understanding of context.

In what we can label as second-order thinking .

Only by building qualitative understanding within quantitative methods combined with second-order effect thinking; can you leverage the best of the two worlds!

For instance, take the interesting case of how Amazon has integrated both quantitative and qualitative data into its business strategy .

This is part of Jeff Bezos’ “Day One” Mindset .

That enabled Amazon to understand when it makes sense to leverage quantitative vs. qualitative data .

As  Jeff Bezos explained in 2006:

“ Many of the important decisions we make at Amazon.com can be made with data. There is a right answer or a wrong answer, a better answer or a worse answer, and math tells us which is which. These are our favorite kinds of decisions.”

As our shareholders know, we have made a decision to continuously and significantly lower prices for customers year after year as our efficiency and scale make it possible.

Indeed, this was the core tenet of Amazon’s flywheel .

And Jeff Bezos also explained:

This is an example of a very important decision that cannot be made in a math-based way. In fact, when we lower prices, we go against the math that we can do, which always says that the smart move is to raise prices.

Indeed, as Jeff Bezos further explained:

We have significant data related to price elasticity. With fair accuracy, we can predict that a price reduction of a certain percentage will result in an increase in units sold of a certain percentage. With rare exceptions, the volume increase in the short term is never enough to pay for the price decrease. 

In short, optimization tools leveraging quantitative analysis are quire effective in the short-term and relation to first-order effects activities.

However, in many cases, that doesn’t tell you anything when it comes to its second-order long-term consequences!

Jeff Bezos explained that extremely well:

However, our quantitative understanding of elasticity is short-term. We can estimate what a price reduction will do this week and this quarter. But we cannot numerically estimate the effect that consistently lowering prices will have on our  business  over five years or ten years or more. 

And he introduced the difference between quantitative data vs. human judgment, which is a qualitative measure!

Our judgment is that relentlessly returning efficiency improvements and scale economies to customers in the form of lower prices creates a virtuous cycle that leads over the long term to a much larger dollar amount of free  cash  flow, and thereby to a much more valuable Amazon.com.

He highlighted how long-term, unpredictable and counterintuitive bets were the result of human judgement:

We’ve made similar judgments around Free Super Saver Shipping and Amazon Prime, both of which are expensive in the short term and—we believe—important and valuable in the long term.

Quantitative research examples 

There is a lot of discussion around the ideal length of social media posts online, and much of it is anecdotal or pure conjecture at best.

To cut through the noise and arrive at data-driven conclusions, brand building platform Buffer teamed up with analytics software company SumAll.

In this example, the research involved tabulating and quantifying social media engagement as a factor of post length.

Posts encompassed a variety of social media updates, such as tweets, blog posts, Facebook posts, and headlines. The study determined:

• The optimal width of a paragraph (140 characters).
• The optimal length of a domain name (8 characters).
• The optimal length of a hashtag (6 characters).
• The optimal length of an email subject (28 to 39 characters), and
• The optimal duration of a podcast (22 minutes) and YouTube video (3 minutes).

Where SumAll sourced its quantitative data varied according to the type of social media post.

To determine the optimal width of a paragraph, the company referenced social media guru Derek Halpern who himself analyzed data from two separate academic studies.

To determine the optimal length of an email subject line, SumAll referenced a 2012 study by Mailer Mailer that analyzed 1.2 billion email messages to identify trends.

Tallwave is a customer experience design company that performs quantitative research for clients and identifies potential trends. 

In the wake of COVID-19, the company wanted to know whether consumer trends the pandemic spurred would continue after restrictions were lifted.

These trends included buy online, pick-up in-store (BOPIS), and blended, cook-at-home restaurant meals. 

Tallwave also wanted to learn more about consumer expectations around branded communication.

In a post-pandemic world, were health and safety precautions more important than the inconvenience they caused?

Would customers abandon digital experiences and flock back to brick-and-mortar stores? Indeed, was it wise to continue to invest in infrastructure the customer didn’t want?

To collect quantitative data, Tallwave surveyed 1,010 individuals across the United States aged 24 and over in April 2021.

Consumers were asked various questions on their behaviors, perceptions, and needs pre and post-pandemic. 

The company found that while customer behavior did change as a result of COVID-19, it had not changed to the extent predicted. Some of the key findings include:

• Convenience trumps all – while many brands continued to focus on health and safety, customers still value convenience above all else. Safety-related needs were the next most important for all age brackets (except Gen Z).
• The role of digital experiences – most survey participants who used a company’s digital experience viewed that company more favorably. This proved that in a post-COVID world, the flexibility for consumers to choose their own “adventure” is paramount.
• The accessibility of digital experiences – the survey data also showed that interest in digital experiences declined with age starting with the 45-54 year bracket. Since 66% of those aged 55 and older reported no desire to continue with online experiences after COVID-19, Tallwave argued that increasing digital literacy would drive greater adoption and engagement over the long term.

Additional Case Studies

Examples of Business Scenarios Using Quantitative Research :

• A company launching a new product conducts surveys to identify which age group is most interested in their product.
• A retail store uses conjoint analysis to determine the optimal price point for a new item.
• A beverage company tests various flavors and uses rating scales to determine which new flavor to launch.
• An e-commerce site analyzes click-through rates to optimize the layout of their product pages.
• A startup uses surveys to measure how many consumers are aware of their brand after a marketing campaign.
• A company conducts an online poll to gauge the effectiveness of their recent TV commercial.
• A tech firm analyzes past sales data to predict the number of units they will sell in the next quarter.
• A corporation uses standardized questionnaires to gauge employee satisfaction and identify areas of improvement.
• A manufacturing company analyzes lead times and delivery speeds to optimize their supply chain processes.
• A retail chain reviews sales data to determine the optimal shelf placement for products to maximize sales.
• An airline analyzes frequent flyer data to understand patterns and introduce loyalty rewards.
• A financial institution uses quantitative analysis to predict stock market trends.
• A supermarket uses sales data to understand which products sell best during promotional events.
• A restaurant reviews time-tracking data to optimize shift schedules during peak hours.
• A software company uses surveys to gather feedback on a new feature they’ve introduced.
• Businesses analyze macroeconomic indicators to forecast market conditions.
• Retailers review sales and inventory data to predict restocking needs.
• A hotel chain uses quantitative research to determine the best locations for new hotels based on travel and occupancy data.
• A company reviews market share data to understand their position relative to competitors.
• A service-based company analyzes call center data to reduce wait times and improve customer service.

Key takeaways

• The characteristics of quantitative research contribute to methods that use statistics as the basis for making generalizations about something.
• In a quantitative study, measurable variables are analyzed using standardized research instruments. Importantly, data must be sampled randomly from a large, representative population to avoid biases.
• Quantitative research data should also be presented in tables and graphs to make key findings more digestible for non-technical stakeholders. Methods must also be repeatable in different contexts to ensure greater outcome confidence and validity.

Key Highlights of Quantitative Research Characteristics:

• Quantitative research uses statistics to make generalizations based on measurable variables.
• Standardized research instruments like questionnaires and surveys are used for data collection.
• Random sampling of participants ensures unbiased results from a larger population.
• Data is presented in tables, graphs, or figures for better understanding.
• The research method is repeatable for verification and validity.
• It allows for predicting outcomes and causal relationships.
• Close-ended questioning is used to gather specific and structured responses.

Importance of Quantitative Research:

• Provides objective, data-based insights, trends, predictions, and patterns for businesses.
• Helps in developing marketing strategies and understanding the target audience.
• Focuses on objective measurement and producing unbiased results.
• Offers versatility in statistical analysis techniques for various research goals.

Real-world Examples of Quantitative Research:

• Impact of Social Media Reviews on Brand Perception.
• Teacher Perceptions of Professional Learning Communities.
• Comparison of Course Grades and Retention in Online vs. Face-to-Face Classes.
• Consumer Attitudes Towards Food Product Advertising.

Qualitative vs. Quantitative Research:

• Qualitative research involves non-numerical data and focuses on understanding human behavior and attitudes.
• Quantitative research relies on measurable variables and statistics to make broad inferences.
• The combination of both methods allows for a comprehensive understanding of complex phenomena.

Sample Size Considerations:

• The sample size is critical in quantitative research to ensure reliable results.
• Larger sample sizes increase precision and reduce the impact of random variation.
• Properly balanced sample sizes are essential for valid and statistically significant conclusions.

Main Points

• Involves statistical analysis for making generalizations based on measurable variables.
• Uses standardized research instruments like surveys and questionnaires.
• Requires random sampling for unbiased representation from a larger population.
• Presents data through tables, graphs, or figures for visualization.
• Should follow a repeatable method for validation and reliability.
• Enables prediction of outcomes and identification of causal relationships.
• Utilizes close-ended questions to gather specific responses.
• Offers data-driven insights, patterns, trends, and predictions.
• Informs business strategies, marketing decisions, and audience understanding.
• Provides objective measurement and representation of trends.
• Enables informed decision-making through statistical analysis .
• Examines social media impact on brand perception.
• Investigates teacher perceptions of professional learning communities.
• Compares online and face-to-face class effectiveness.
• Studies consumer attitudes towards food product advertising.
• Qualitative research focuses on understanding human behavior through non-numerical data.
• Quantitative research emphasizes measurable variables and statistical analysis .
• Combining both methods offers a comprehensive understanding of complex phenomena.
• Sample size is crucial for reliable and accurate results.
• Larger samples enhance precision and reduce random variation impact.
• Balanced sample sizes ensure valid and statistically significant findings.

Read Also: Quantitative vs. Qualitative Research .

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Qualitative vs Quantitative Research Methods & Data Analysis

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What is the difference between quantitative and qualitative?

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research , on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography.

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis .

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded .

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Mixed methods research
• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

REVIEW article

Research progress of ultrasound in accurate evaluation of cartilage injury in osteoarthritis.

• 1 Orthopedics and Sports Medicine Center, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
• 2 Gusu School, Nanjing Medical University, Suzhou, China

Osteoarthritis (OA) is a prevalent cause of joint algesia, loss of function, and disability in adults, with cartilage injury being its core pathological manifestation. Since cartilage damage is non-renewable, the treatment outcome in the middle and late stages of OA is unsatisfactory, which can be minimized by changing lifestyle and other treatment modalities if diagnosed and managed in the early stages, indicating the importance of early diagnosis and monitoring of cartilage injury. Ultrasound technology has been used for timely diagnosis and even cartilage injury treatment, which is convenient and safe for the patient owing to no radiation exposure. Studies have demonstrated the effectiveness of ultrasound and its various quantitative ultrasound parameters, like ultrasound roughness index (URI), reflection coefficient (R), apparent integrated backscatter (AIB), thickness, and ultrasound elastography, in the early and accurate assessment of OA cartilage pathological changes, including surface and internal tissue, hardness, and thickness. Although many challenges are faced in the clinical application of this technology in diagnosis, ultrasound and ultrasound-assisted techniques offer a lot of promise for detecting early cartilage damage in OA. In this review, we have discussed the evaluation of ultrasonic cartilage quantitative parameters for early pathological cartilage changes.

1 Introduction

Osteoarthritis (OA) is a common and prevalent skeletal degenerative condition, where cartilage injury and degree of damage are regarded as the prime pathological changes incurred ( 1 ). Changes in the shape and structure of the cartilage surface and the components of cartilage in joints are essential symptoms and diagnostic bases for evaluating cartilage injury. Additionally, the severity of cartilage injury is an essential reference for various scoring systems, including the International Cartilage Repair Society (ICRS). Applying cartilage repair treatments like self-chondrocyte implantation and self-osteochondral transplantation, as well as OA management medicines, necessitates a more accurate and objective evaluation of articular cartilage and subchondral bone integrity ( 2 , 3 ). Therefore, accurate and sensitive evaluation of cartilage injury, real-time monitoring, changes in cartilage status assessment, and timely adoption of corresponding treatment measures are vital factors in OA diagnosis and treatment, which are pivotal for preventing late-stage complications ( 4 ) ( Figure 1 ).

Figure 1 . Ultrasound assessment of OA cartilage injury diagram (A) In the early stages of cartilage damage, URI, R, and IRC can identify the surface roughness of the cartilage. (B) R, AIB can reflect the abnormal collagen network organization and composition in the early stage of cartilage injury. (C) Rbone, IRC, and AIB are related to the surface area of trabecular bone, which can reflect the degeneration of subchondral bone. (D) Elastic ultrasound can distinguish between normal cartilage and pathological cartilage. (E) Ultrasound can detect cartilage thickness and defect degree. (F) Calculate a schematic diagram of quantitative ultrasound parameters. d i ,d 1 , and d 2 are the lengths from the transducer to the surface. (G) Cartilage schematic diagram under ultrasound elastography. Reprinted from Clinical Anatomy, Vol. 32, Sonoelastography of the knee joint, Akkaya M, Cay N, Gursoy S, Simsek ME, Tahta M, Dogan M, et al., Pages 99-104, doi: 10.1002/ca.23300 ( 5 ), this diagram has been authorized. (H) Ultrasound measurement of cartilage thickness schematic diagram. A represents the thickness of cartilage.

Ultrasound (US) is a safe, non-radiation, low-cost, and widely used technique for diagnosing musculoskeletal diseases and can give information about synovitis, joint effusion, periarticular soft tissues, and bony cortical abnormalities in peripheral OA joints ( 4 , 6 – 8 ). Due to the high content of water and the absence of inner acoustic interfaces, the cartilage presents as hypoechoic or anechoic bands. Divided by two sharp hyperechoic interfaces of the cartilage-bone interface and synovial space-cartilage interface ( 9 ), the main characteristics of healthy patient joint cartilage are low echo or anechoic and clear cartilage-bone interface and synovial fluid-cartilage interface. OA patients exhibit unevenly scattered echo bands on the surface or middle part of the tissue due to surface and internal degeneration such as decreased water content and fibrous degeneration. Mechanical damage results in joint cartilage damage and loss ( Figure 2 ). Lately, studies have demonstrated that US-assisted technology can quantitatively detect cartilage changes and can disclose early cartilage pathologies or evaluate cartilage damage, which can measure cartilage thickness ( 6 , 11 , 12 ). In early osteoarthritis, the loss of proteoglycans and the destruction of surface collagen lead to fibrosis and softening of the soft bone surface ( 13 , 14 ). Quantitative ultrasound parameters can provide information on surface fibrosis of articular cartilage, reflecting the destruction of surface collagen and the loss of proteoglycans, which helps to distinguish between normal and degenerative articular cartilage in the early stages of osteoarthritis ( 15 , 16 ). Ultrasound elastography, as a new US imaging method, can detect articular cartilage softening before structural changes in knee osteoarthritis(KOA) and distinguish pathological cartilage from normal cartilage in the early stage of osteoarthritis ( 17 , 18 ). It can detect changes in the hardness of articular cartilage before structural changes in knee osteoarthritis, which helps to achieve the goal of early diagnosis of OA. Cartilage thickness is an important indicator for describing the development and progression of osteoarthritis. Detecting cartilage thickness and the degree of cartilage damage is crucial for evaluating the progression and treatment response of OA ( 13 , 19 ). Quantitative ultrasound parameters, ultrasound elastography, and ultrasound detection of soft bone thickness play a crucial role in the early diagnosis and treatment of OA and are crucial for preventing late complications and slowing down disease progression. Systematic articles on US-based evaluation of cartilage injuries still need to be included. This review aims to provide information about the US application in assessing OA cartilage injury and puts forward some suggestions for progress in this field.

Figure 2 . Ultrasound images of normal and damaged cartilage tissue in mode B. (A) is classified as normal cartilage. Articular cartilage shows hyperechoic, sharply defined interfaces. (B) is classified as pathological cartilage. Articular cartilage appears thinner and shows less defined interfaces. Reprinted from Annals of the Rheumatic Diseases, Vol. 68, Ultrasound Validity in the Measurement of Knee Cartilage Thickness, Naredo E, Acebes C, Moller I, Canillas F, de Agustin JJ, de Miguel E, et al., Pages 1322-1327, doi: 10.1136/ard.2008.090738 ( 10 ), this diagram has been authorized.

2 Application of ultrasound in cartilage injury assessment

2.1 quantitative ultrasound parameters have the potential to become measurement tools for the quantitative analysis of articular cartilage.

The quantitative ultrasound parameters for evaluating cartilage injury assessment include the ultrasound roughness index (URI), reflection coefficient (R), cartilage-bone interface reflection coefficient (Rbone), apparent integrated backscatter (AIB), and integrated ultrasonic reflection coefficient (IRC). The early pathological manifestations of OA cartilage injury include surface fibrillation ( 13 ) and tissue swelling ( 14 ), due to the reduction in proteoglycan on the surface of cartilage in joints and the destruction of the surface collagen network. A study reported that the above-stated quantitative ultrasound parameters of cartilage could sensitively detect mechanical degeneration, roughness changes of the cartilage surface and spontaneous fibrous fibrillation, enzymatic destruction of the surface collagen network, and degeneration of the subchondral bone, with an ability to distinguish between normal and degenerative articular cartilage in the initial stages of OA ( Table 1 ).

Table 1 . Measurement methods for partial quantitative ultrasound parameters reported in the literature.

The URI can monitor the cartilage surface microstructure and describe the morphological changes, where R and AIB of the cartilage surface are sensitive to the change in collagen content and structure. Similarly, R is also used to describe the characteristics of cartilage tissue ( 20 ), and cartilage surface R depicts the acoustic parameter in cartilage enzyme degradation ( 21 ). Furthermore, the AIB is sensitive to alterations in the number and direction of the collagen network ( 22 ); a drop in R and IRC, as well as a rise in URI, can diagnose enhanced cartilage surface roughness. Similarly, a decreased R and IRC on the cartilage surface also depicts enzyme-induced surface collagen network degeneration. Besides cartilage health assessment, US has also been shown to be sensitive to subchondral bone degeneration ( 23 ). The R and IRC of the cartilage-bone interface were significantly correlated with the trabecular bone’s surface volume ratio and trabecular thickness. In the initial phases of OA, the bone around the joint is prone to change, including increased subchondral bone thickness, decreased subchondral trabecular bone mass, and the progression of calcified cartilage areas ( 24 ).

Moreover, quantitative ultrasound parameters are also helpful for accurately grading OA cartilage damage and viewing variations in the cartilage and internal tissues. Studies have demonstrated that increased URI is associated with an increased OA grade ( 14 , 25 , 26 ). With the progression of OA grading, the cartilage surface gets unequal and unpolished. Similarly, OA cartilage R decreases significantly compared to normal, whereas a decreased R significantly decreases with OA development. The increased surface roughness results in diffused reflection, reducing the echo amplitude ( 14 ). Additionally, with OA development, the cartilage softens, and the composition and framework of articular cartilage gradually change from the surface to the deep section. Since soft cartilage absorbs more transmission ultrasonic energy, the R-value decreases. As the OA stage increased, so did the R-value of the cartilage-bone interface, which was significantly higher than normal cartilage. Furthermore, the IRC was also strongly related to the early OARSI grade, where an increased IRC in OA was related to the R of the cartilage surface due to the destructive interference of incoherent waves scattered by surface fibrillation. An increased AIB might indicate abnormal organization and composition of the collagen network ( 16 ) since the AIB slope of early OARSI grading increased, whereas the AIB slope of degenerative cartilage samples was higher than that of healthy cartilage samples. The increased AIB slope in degenerated cartilage could be attributed to the collagen network rearrangement since the disorganized structure of diseased cartilage leads to greater backscatter than the deep vertical arrangement of fibers in normal cartilage ( 27 ).

The study reported that the cartilage surface R might be a more effective indicator than the URI and the cartilage-bone interface R to distinguish early OA grading ( 28 ). Many studies have also shown that the surface roughness index and R strongly correlate with the pathological evaluation of articular cartilage ( 14 ). In summary, quantitative ultrasound parameters can be used as a helpful assessment technique for quantitative articular cartilage assessment ( 14 , 16 , 29 ). They have also been applied for the quantitative diagnosis of cartilage lesions in vivo and in vitro , demonstrating the feasibility of in vivo US ( Table 2 ).

Table 2 . Study on quantitative ultrasound parameters in measuring cartilage injury.

2.2 Ultrasound elastography provides elastic information

2.2.1 the main classification and application of ultrasound elastography.

The World Federation of Ultrasound Medicine and Biology has defined it as strain and shear wave imaging according to the measurement of elastography, where the former depicts tissue deformation when the probe exerts pressure on the tissue along the propagation direction of the ultrasonic beam (including manual squeezing and acoustic radiation force pulse technique(ARFI)), while the latter is obtained by comparing the echo signals before and after compression ( 36 ). Strain imaging uses strain ratio to evaluate the deformation ability of tissue, where an increased strain ratio indicates softening, while acoustic elastography is based on shear wave technology (including transient elastography (TE) and ARFI), which excites the tissue to produce shear waves followed by measuring shear wave velocity. The hardness can be classified based on measuring the shear wave velocity, or Young’s modulus. The ARFI method does not depend on the compression applied to the surface and can be used to evaluate deeper-position organs ( 37 ). Ultrasound elastography uses color maps to evaluate the tissue’s deformability, where a change in color from blue to red indicates softening, overcoming the weakness of subjectivity of manual palpation, providing new elastic diagnostic information, expanding the scope of clinical application, could detect deep lesions and superficial masses, and has also been applied in cartilage injury ( Table 3 ).

Table 3 . The calculation method of tissue stiffness is evaluated by elastography technology ( 38 ).

2.2.2 Application of ultrasound elastography in other diseases

Strain imaging has been applied for lesion detection in various tissues ( 39 ), such as the auxiliary diagnosis of thyroid nodules ( 40 ) and focal pancreatic lesions ( 41 ), and has unique advantages in the diagnosis of autoimmune pancreatitis ( 42 ). It can also be used to help identify acute and chronic deep vein thrombosis ( 43 ) and to assist in the identification of suspicious lymph nodes during lymph node puncture ( 44 ). Recent studies have found that strain elastography is also reliable for monitoring relative knee ligament stiffness ( 45 ). Sahan MH ( 46 ) used strain elastography to assist in measuring cartilage elasticity and evaluating variations in cartilage hardness in the initial phases of OA.

Shear wave imaging is mainly used to diagnose mild fibrosis or cirrhosis ( 47 ), and TE has mostly been utilized to assess liver stiffness measures (LSM) in individuals suffering from long-term viral hepatitis or additional illnesses, with more representative results of liver parenchymal stiffness compared to liver biopsy ( 39 ). The TE uses an external ‘punching machine’ with controllable vibration to produce shear waves, measure the average shear wave velocity in the region, and convert it into Young’s modulus; hence, the TE standardization technique was specifically used for measuring liver tissue hardness rather than imaging ( 38 ). Shear wave elastography based on ARFI techniques can help diagnose the staging of liver fibrosis, detect and characterize focal liver lesions ( 48 ), and diagnose benign and malignant thyroid nodules ( 49 ), especially in the presence of chronic autoimmune thyroiditis ( 50 ). It has also been used for the gastrointestinal tract ( 51 ), heart ( 52 ), blood vessels, and musculoskeletal ( 53 ). Further, it can also be used to improve the accuracy of gastrointestinal tumor staging, assist in making a diagnosis of benign and malignant lymph nodes among individuals with primary cancer, improve the diagnosis of carotid plaque vulnerability ( 54 ), evaluate the directional mechanics of the heart and cartilage ( 52 ), quantify the mechanical properties of false vocal cords in normal individuals, and evaluate the symmetry of false vocal cords ( 55 ). It also has the inherent advantage of diagnosing and treating neurological diseases such as Parkinson’s disease ( 56 ), carpal tunnel syndrome ( 57 ), chronic stroke ( 58 ), and multiple sclerosis ( 59 ). Furthermore, it concentrates on the transverse waves created within the tissue, which can be employed for patients with ascites surrounding the liver and is more effective for obese people ( 60 , 61 ).

2.2.3 Ultrasound elastography distinguishes pathological cartilage from normal cartilage

The health and maintenance of articular cartilage highly depend on appropriate mechanical loading. In animal and human studies, both high loads and low physical activity have led to cartilage thinning and softening ( 62 – 64 ). Due to gravity forces, the body weight load may show different elastography features in normal and pathological conditions compared to the joints of the upper limb. The hardness of cartilage changes before cartilage structure changes in the early stage of KOA ( 65 ); hence, it is important to evaluate cartilage elasticity ( 17 ). Strain elasticity imaging induces echo signal movement around the tissue, which the probe stresses to exert stable and regular pressure on the target tissue. The strain rate is obtained by contrasting the echo signals prior to and following pressure, where a higher strain ( 38 ) ensures more excellent material elasticity.

In a distal femoral cartilage evaluation study, real-time elastography was objectively used to evaluate tissue elasticity, where the diseased cartilage area’s median strain value was substantially greater than healthy cartilage ( 5 ). Similarly, another study demonstrated that elastography might be an effective tool for displaying diseased cartilage and being used to distinguish diseased cartilage from normal cartilage ( 18 ), where the median strain value of the pathological femoral cartilage area was significantly higher than normal cartilage. In ultrasound elastography, blue coding of normal cartilage tissue shows typical echoless imaging characteristics, which are excellent clarity, devoid of focal defects, smooth bone surface, and unchanged thickness compared with adjacent tissues. In contrast, the pathological cartilage tissue coding showed irregular color changes from blue to red. In an event where US shows no difference in cartilage thickness, real-time elastography can be utilized to determine the change in cartilage hardness by calculating the strain ratio of the region. This technique can be used to forecast the degenerative changes in the knee joint after anterior cruciate ligament reconstruction ( 66 ). Shear wave elastography is a reliable, harmless, and acceptable technique for evaluating pathological cartilage ( 17 , 67 ), where the shear wave value is correlated with the cartilage US score. The faster the shear wave speed or the greater Young’s modulus, the lower the elasticity of the tissue and the higher the hardness. Different hardness levels can identify normal or abnormal tissues ( 36 ). Therefore, elastography can be used as an early detection method for evaluating OA cartilage injury ( 68 ).

2.3 Ultrasound is a reliable tool for quantifying cartilage thickness

For measuring cartilage thickness, US is a dependable, unbiased, and objective technology ( 6 ). It includes the measurement of the articular cartilage thickness of the knee, wrist, shoulder, and metacarpophalangeal joint. Measurement of early alterations in femoral cartilage thickness following ACL reconstruction helps evaluate and prevent the occurrence of KOA ( 29 ). Articular cartilage thickness of metacarpophalangeal (MCP) and proximal interphalangeal (PIP) joints were measured to assist early identification and monitoring of bone erosion and cartilage injury in rheumatoid arthritis ( 69 ), as well as to measure the cartilage thickness of juvenile knee joint to assist the diagnosis of juvenile idiopathic arthritis ( 70 ). Prenatal US examination of fetal nasal soft tissue thickness and nasal bone length can effectively reduce the birth rate of fetuses with Down’s syndrome, thus having high accuracy and clinical application value for screening fetuses with Down’s syndrome ( 71 , 72 ).

Due to mechanical damage, late-stage OA patients are characterized by joint cartilage damage, loss, and thinning of thickness. US recognition of changes in cartilage status is crucial for evaluating the effectiveness of strategies to reduce the risk of development and progression of KOA ( 4 , 10 ). OA patients with cartilage defects face accelerated progression of OA ( 19 ) since cartilage thickness is an essential index for detecting the occurrence and development of OA, where detection and quantification of cartilage thickness and damage are crucial for evaluating OA’s progression and treatment. The main characteristics of articular cartilage are hypoechoic, anechoic, and clear cartilage bone and synovial cartilage interfaces. The high echo lines between the surface of the cartilage and synovial fluid are called “interface signs”. Identifying the cartilage bone and synovial fluid cartilage interfaces is particularly important for measuring cartilage thickness ( 9 , 10 ). A significant number of studies in the literature suggest that US is a feasible clinical tool for assessing cartilage thickness, which has been found to be consistent with in vitro animal studies and autopsy thickness values and highly correlated with cartilage thickness measured by MRI ( 10 , 73 ). Similarly, it was found in vivo that ultrasonography can accurately measure cartilage thickness as well as the scope of damaged cartilage in the knee joint ( 74 ). Arthroscopic US can avoid bone occlusion in the joint and thoroughly evaluate the entire cartilage in the joint; in addition, it can accurately measure the thickness of the cartilage in the case of extremely thin cartilage, assessing the degree of regional cartilage damage relative to the thickness of the entire articular cartilage ( 75 ). A significant correlation between US and arthroscopy has shown that US has an excellent predictive value in detecting the severity of cartilage degeneration and can also detect early pathological changes in articular cartilage.

Currently, the determination of cartilage thickness using US primarily relies on image segmentation or original radio frequency (RF) signal analysis. Although static US scans provide high-resolution and high-quality images, cartilage data analysis faces challenges due to low contrast, high-level speckle noise, and various imaging issues in US images. There is an urgent need for accurate, stable, and fully automated methods to enhance US images and segment cartilage, thus enhancing the widespread utility of US imaging techniques. Various technologies have been developed to address this need, including multipurpose beta-optimized recursive histogram equalization (MBORBHE), the random walker (RW) algorithm, the local statistical level set method (LSLSM), and deep learning methods ( 6 , 76 – 78 ). For instance, MBORBHE is utilized to enhance cartilage regions in US images, preserving essential information such as brightness shifts and contrast enhancement. However, this method may inadvertently enhance the soft tissue interface, potentially affecting cartilage segmentation and thickness measurement. The RW algorithm is employed for automatic cartilage segmentation, although it is susceptible to changes in anatomical structure ( 6 ). Another approach involves using the LSLSM to segment cartilage from two-dimensional knee joint US data. While it yields promising results, post-processing of the segmented image using connected component labels is necessary ( 77 ). Deep learning frameworks, such as convolutional neural networks, are employed to regress the cartilage interface distance field, delineate cartilage interfaces, and calculate cartilage thickness ( 76 ). Furthermore, the original RF signal is tracked using peak detection algorithms to analyze surface displacement and calculate cartilage thickness ( 78 ). Active or passive movements during US evaluation can also be used to observe the flow of synovial fluid within focal cartilage defects that are almost invisible in static US imaging, significantly improving the sensitivity and specificity of the examination. ACL injury is a key risk factor for the development of KOA, and imaging of this ligament under static US is difficult, but dynamic US can help confirm structural lesions. Dynamic US can help better visualize and simulate different anatomical structures in daily life, which is helpful for the diagnosis of OA complications such as synovitis and joint effusion. It plays an increasingly important role in evaluating joint cartilage tissue ( 79 , 80 ) ( Table 4 ).

Table 4 . Study on the measurement of cartilage thickness by ultrasound.

3 Conclusion and future perspectives

US has many characteristics that make it valuable in evaluating OA cartilage damage. Quantitative ultrasound parameters can detect early collagen fracture of articular cartilage and rough and uneven articular cartilage surfaces, which can be utilized to assess the integrity of articular cartilage and provide helpful information for quantitative ultrasound diagnosis of early OA. As a novel ultrasonic imaging method, elastography is more promising than traditional US, where additional imaging can provide valuable information on articular cartilage elasticity to clinicians. Elastography also finds applications in determining tissue properties, structure, and function. Currently, it is shown in the initial rhinoplasty and revision nasal surgery that strain ultrasound elastography can assist in the selection of the correct tissue for cartilage transplantation. It is foreseeable that more advanced US technologies will continue to rapidly evolve in the coming years. US can accurately measure cartilage thickness, degree, and depth of cartilage defects, enhance accuracy in clinical OA classification, and improve and evaluate OA’s progress and treatment response by detecting and monitoring the therapeutic effect of cartilage injury. In recent times, there has been a pervasive utilization of three-dimensional US imaging technology, addressing the constraints associated with two-dimensional imaging for the observation of cartilage’s three-dimensional structure. This advancement facilitates comprehensive and volumetric cartilage imaging, furnishing practitioners with augmented data for precise morphological and functional assessments. Moreover, the ongoing evolution of artificial intelligence (AI) technology within the realm of US cartilage imaging, encompassing the training of deep learning models, holds promise for automating the analysis and diagnosis of cartilage imaging data, thereby enhancing diagnostic precision and workflow efficiency. US is a rapidly growing technology with enormous possibilities for future clinical applications. We have shown in our overview that US can be employed in basic studies of articular cartilage to evaluate early histopathology, elasticity, thickness, degree of changes, and defects in articular cartilage, which play a crucial role in detecting early bone and joint disorders. Although there are still many challenges in the development of US diagnostic tools, they play an increasingly important role in the diagnosis of cartilage injuries.

Author contributions

HZ: Writing – original draft. EN: Writing – review & editing. LL: Writing – review & editing. JZ: Writing – review & editing. ZS: Writing – review & editing. XY: Writing – review & editing. YH: Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was funded by the Jiangsu Science and Technology Department (BK20211083, BE2022737), the Jiangsu Graduate Student Cultivation Innovative Engineering Graduate Research and Practice Innovation Program (SJCX23_0683), and the Suzhou Health Commission (GSWS2020078, SZXK202111).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2024.1420049/full#supplementary-material

Abbreviations

US, ultrasound; OA, osteoarthritis; URI, ultrasound roughness index; R, reflection coefficient; AIB, apparent integrated backscatter; TE, transient elastography.

1. Kim S, Han S, Kim Y, Kim HS, Gu YR, Kang D, et al. Tankyrase inhibition preserves osteoarthritic cartilage by coordinating cartilage matrix anabolism via effects on SOX9 parylation. Nat Commun . (2019) 10:4898. doi: 10.1038/s41467-019-12910-2

PubMed Abstract | Crossref Full Text | Google Scholar

2. Chimutengwende-Gordon M, Donaldson J, Bentley G. Current solutions for the treatment of chronic articular cartilage defects in the knee. EFORT Open Rev . (2020) 5:156–63. doi: 10.1302/2058-5241.5.190031

3. Vonk LA, van Dooremalen SFJ, Liv N, Klumperman J, Coffer PJ, Saris DBF, et al. Mesenchymal stromal/stem cell-derived extracellular vesicles promote human cartilage regeneration in vitro. Theranostics . (2018) 8:906–20. doi: 10.7150/thno.20746

4. Schmitz RJ, Wang HM, Polprasert DR, Kraft RA, Pietrosimone BG. Evaluation of knee cartilage thickness: A comparison between ultrasound and magnetic resonance imaging methods. Knee . (2017) 24:217–23. doi: 10.1016/j.knee.2016.10.004

5. Akkaya M, Cay N, Gursoy S, Simsek ME, Tahta M, Dogan M, et al. Sonoelastography of the knee joint. Clin Anat . (2019) 32:99–104. doi: 10.1002/ca.23300

6. Desai P, Hacihaliloglu I. Knee-cartilage segmentation and thickness measurement from 2d ultrasound. J Imaging . (2019) 5:43–59. doi: 10.3390/jimaging5040043

7. Shi W, Kanamoto T, Aihara M, Oka S, Kuroda S, Nakai T, et al. Articular surface integrity assessed by ultrasound is associated with biological characteristics of articular cartilage in early-stage degeneration. Sci Rep . (2022) 12:11970. doi: 10.1038/s41598-022-16248-6

8. Ricci V, Ricci C, Gervasoni F, Cocco G, Andreoli A, Özçakar L. From histoanatomy to sonography in myofascial pain syndrome. Am J Phys Med Rehabil . (2023) 102:92–7. doi: 10.1097/phm.0000000000001975

9. Tamborrini G, Hügle T, Ricci V, Filippou G. Ultrasound imaging in crystal arthropathies: A pictorial review. Reumatismo . (2023) 75:167-75. doi: 10.4081/reumatismo.2023.1583

10. Naredo E, Acebes C, Moller I, Canillas F, de Agustin JJ, de Miguel E, et al. Ultrasound validity in the measurement of knee cartilage thickness. Ann Rheum Dis . (2009) 68:1322–7. doi: 10.1136/ard.2008.090738

11. Yagi M, Taniguchi M, Tateuchi H, Hirono T, Yamagata M, Umehara J, et al. Relationship between individual forces of each quadriceps head during low-load knee extension and cartilage thickness and knee pain in women with knee osteoarthritis. Clin Biomech (Bristol Avon) . (2022) 91:105546. doi: 10.1016/j.clinbiomech.2021.105546

12. Güvener O, Dağ F, Çimen ÖB, Özçakar L. Ultrasound assessment of distal femoral cartilage thickness measurements after walking/jogging in subjects with pes planus. Knee . (2022) 39:161–7. doi: 10.1016/j.knee.2022.09.007

13. Nieminen HJ, Zheng Y, Saarakkala S, Wang Q, Toyras J, Huang Y, et al. Quantitative assessment of articular cartilage using high-frequency ultrasound: research findings and diagnostic prospects. Crit Rev BioMed Eng . (2009) 37:461–94. doi: 10.1615/critrevbiomedeng.v37.i6.20

14. Niu HJ, Wang Q, Wang YX, Li DY, Fan YB, Chen WF. Ultrasonic reflection coefficient and surface roughness index of oa articular cartilage: relation to pathological assessment. BMC Musculoskelet Disord . (2012) 13:34. doi: 10.1186/1471-2474-13-34

15. Sorriento A, Cafarelli A, Valenza G, Ricotti L. Ex-vivo quantitative ultrasound assessment of cartilage degeneration. Annu Int Conf IEEE Eng Med Biol Soc . (2021) 2021:2976–80. doi: 10.1109/EMBC46164.2021.9630198

16. Lye TH, Gachouch O, Renner L, Elezkurtaj S, Cash H, Messroghli D, et al. Quantitative ultrasound assessment of early osteoarthritis in human articular cartilage using a high-frequency linear array transducer. Ultrasound Med Biol . (2022) 48:1429–40. doi: 10.1016/j.ultrasmedbio.2022.03.006

17. Deng W, Lin M, Yu S, Liang H, Zhang Z, Liu C. Quantifying region-specific elastic properties of distal femoral articular cartilage: A shear-wave elastography study. Appl Bionics Biomech . (2022) 2022:9406863. doi: 10.1155/2022/9406863

18. Cay N, Ipek A, Isik C, Unal O, Kartal MG, Arslan H, et al. Strain ratio measurement of femoral cartilage by real-time elastosonography: preliminary results. Eur Radiol . (2015) 25:987–93. doi: 10.1007/s00330-014-3497-y

19. Everhart JS, Abouljoud MM, Flanigan DC. Role of full-thickness cartilage defects in knee osteoarthritis (Oa) incidence and progression: data from the OA initiative. J Orthop Res . (2019) 37:77–83. doi: 10.1002/jor.24140

20. Saarakkala S, Toyras J, Hirvonen J, Laasanen MS, Lappalainen R, Jurvelin JS. Ultrasonic quantitation of superficial degradation of articular cartilage. Ultrasound Med Biol . (2004) 30:783–92. doi: 10.1016/j.ultrasmedbio.2004.03.005

21. Nieminen HJ, Toyras J, Rieppo J, Nieminen MT, Hirvonen J, Korhonen R, et al. Real-time ultrasound analysis of articular cartilage degradation in vitro. Ultrasound Med Biol . (2002) 28:519–25. doi: 10.1016/s0301-5629(02)00480-5

22. Cherin E, Saied A, Pellaumail B, Loeuille D, Laugier P, Gillet P, et al. Assessment of rat articular cartilage maturation using 50-MHz quantitative ultrasonography. Osteoarthritis Cartilage . (2001) 9:178–86. doi: 10.1053/joca.2000.0374

23. Liukkonen J, Hirvasniemi J, Joukainen A, Penttila P, Viren T, Saarakkala S, et al. Arthroscopic ultrasound technique for simultaneous quantitative assessment of articular cartilage and subchondral bone: an in vitro and in vivo feasibility study. Ultrasound Med Biol . (2013) 39:1460–8. doi: 10.1016/j.ultrasmedbio.2013.03.026

24. Goldring SR. Alterations in periarticular bone and cross talk between subchondral bone and articular cartilage in osteoarthritis. Ther Adv Musculoskelet Dis . (2012) 4:249–58. doi: 10.1177/1759720X12437353

25. Pritzker KP, Gay S, Jimenez SA, Ostergaard K, Pelletier JP, Revell PA, et al. Osteoarthritis cartilage histopathology: grading and staging. Osteoarthritis Cartilage . (2006) 14:13–29. doi: 10.1016/j.joca.2005.07.014

26. Wang Y, Guo Y, Zhang L, Niu H, Xu M, Zhao B, et al. Ultrasound biomicroscopy for the detection of early osteoarthritis in an animal model. Acad Radiol . (2011) 18:167–73. doi: 10.1016/j.acra.2010.09.011

27. Gelse K, Olk A, Eichhorn S, Swoboda B, Schoene M, Raum K. Quantitative ultrasound biomicroscopy for the analysis of healthy and repair cartilage tissue. Eur Cell Mater . (2010) 19:58–71. doi: 10.22203/ecm.v019a07

28. Kiviranta P, Toyras J, Nieminen MT, Laasanen MS, Saarakkala S, Nieminen HJ, et al. Comparison of novel clinically applicable methodology for sensitive diagnostics of cartilage degeneration. Eur Cell Mater . (2007) 13:46–55. doi: 10.22203/ecm.v013a05

29. Lisee C, Harkey M, Walker Z, Pfeiffer K, Covassin T, Kovan J, et al. Longitudinal changes in ultrasound-assessed femoral cartilage thickness in individuals from 4 to 6 months following anterior cruciate ligament reconstruction. Cartilage . (2021) 13:738S–46S. doi: 10.1177/19476035211038749

30. Viren T, Saarakkala S, Jurvelin JS, Pulkkinen HJ, Tiitu V, Valonen P, et al. Quantitative evaluation of spontaneously and surgically repaired rabbit articular cartilage using intra-articular ultrasound method in situ. Ultrasound Med Biol . (2010) 36:833–9. doi: 10.1016/j.ultrasmedbio.2010.02.015

31. Viren T, Saarakkala S, Tiitu V, Puhakka J, Kiviranta I, Jurvelin J, et al. Ultrasound evaluation of mechanical injury of bovine knee articular cartilage under arthroscopic control. IEEE Trans Ultrason Ferroelectr Freq Control . (2011) 58:148–55. doi: 10.1109/TUFFC.2011.1781

32. Wang Q, Liu Z, Wang Y, Pan Q, Feng Q, Huang Q, et al. Quantitative ultrasound assessment of cartilage degeneration in ovariectomized rats with low estrogen levels. Ultrasound Med Biol . (2016) 42:290–8. doi: 10.1016/j.ultrasmedbio.2015.08.004

33. Huang YP, Zhong J, Chen J, Yan CH, Zheng YP, Wen CY. High-frequency ultrasound imaging of tidemark in vitro in advanced knee osteoarthritis. Ultrasound Med Biol . (2018) 44:94–101. doi: 10.1016/j.ultrasmedbio.2017.08.1884

34. Zhang J, Xiao L, Tong L, Wan C, Hao Z. Quantitative evaluation of enzyme-induced porcine articular cartilage degeneration based on observation of entire cartilage layer using ultrasound. Ultrasound Med Biol . (2018) 44:861–71. doi: 10.1016/j.ultrasmedbio.2017.11.016

35. Pastrama M, Spierings J, van Hugten P, Ito K, Lopata R, van Donkelaar CC. Ultrasound-based quantification of cartilage damage after in vivo articulation with metal implants. Cartilage . (2021) 13:1540S–50S. doi: 10.1177/19476035211063861

36. Ozturk A, Grajo JR, Dhyani M, Anthony BW, Samir AE. Principles of ultrasound elastography. Abdom Radiol (NY) . (2018) 43:773–85. doi: 10.1007/s00261-018-1475-6

37. Gennisson JL, Deffieux T, Fink M, Tanter M. Ultrasound elastography: principles and techniques. Diagn Interv Imaging . (2013) 94:487–95. doi: 10.1016/j.diii.2013.01.022

38. Shiina T. Wfumb guidelines and recommendations for clinical use of ultrasound elastography: part 1: basic principles and terminology. Ultrasound Med Biol . (2017) 43:S191–S2. doi: 10.1016/j.ultrasmedbio.2017.08.1653

Crossref Full Text | Google Scholar

39. Cui XW, Li KN, Yi AJ, Wang B, Wei Q, Wu GG, et al. Ultrasound elastography. Endosc Ultrasound . (2022) 11:252–74. doi: 10.4103/EUS-D-21-00151

40. Li J, Chen M, Cao CL, Zhou LQ, Li SG, Ge ZK, et al. Diagnostic performance of acoustic radiation force impulse elastography for the differentiation of benign and Malignant superficial lymph nodes: A meta-analysis. J Ultrasound Med . (2020) 39:213–22. doi: 10.1002/jum.15096

41. Ignee A, Jenssen C, Arcidiacono PG, Hocke M, Moller K, Saftoiu A, et al. Endoscopic ultrasound elastography of small solid pancreatic lesions: A multicenter study. Endoscopy . (2018) 50:1071–9. doi: 10.1055/a-0588-4941

42. Dong Y, D'Onofrio M, Hocke M, Jenssen C, Potthoff A, Atkinson N, et al. Autoimmune pancreatitis: imaging features. Endosc Ultrasound . (2018) 7:196–203. doi: 10.4103/eus.eus_23_17

43. Aslan A, Barutca H, Ayaz E, Aslan M, Kocaaslan C, Inan I, et al. Is real-time elastography helpful to differentiate acute from subacute deep venous thrombosis? A preliminary study. J Clin Ultrasound . (2018) 46:116–21. doi: 10.1002/jcu.22522

44. Wang B, Guo Q, Wang JY, Yu Y, Yi AJ, Cui XW, et al. Ultrasound elastography for the evaluation of lymph nodes. Front Oncol . (2021) 11:714660. doi: 10.3389/fonc.2021.714660

45. Wadugodapitiya S, Sakamoto M, Tanaka M, Sakagami Y, Morise Y, Kobayashi K. Assessment of knee collateral ligament stiffness by strain ultrasound elastography. BioMed Mater Eng . (2022) 33:337–49. doi: 10.3233/BME-211282

46. Sahan MH, Bayar Muluk N, Inal M, Asal N, Simsek G, Arikan OK. Sonoelastographic evaluation of the lower lateral nasal cartilage lateral crus, auricular conchal cartilage, and costal cartilage. Facial Plast Surg . (2019) 35:678–86. doi: 10.1055/s-0039-3399577

47. Ferraioli G, Roccarina D. Update on the role of elastography in liver disease. Therap Adv Gastroenterol . (2022) 15:17562848221140657. doi: 10.1177/17562848221140657

48. Ruan SM, Huang H, Cheng MQ, Lin MX, Hu HT, Huang Y, et al. Shear-wave elastography combined with contrast-enhanced ultrasound algorithm for noninvasive characterization of focal liver lesions. Radiol Med . (2023) 128:6–15. doi: 10.1007/s11547-022-01575-5

49. Zhang Y, Lu F, Shi H, Guo LH, Wei Q, Xu HX, et al. Predicting Malignancy in thyroid nodules with benign cytology results: the role of conventional ultrasound, shear wave elastography and braf V600e. Clin Hemorheol Microcirc . (2022) 81:33–45. doi: 10.3233/CH-211337

50. Han R, Li F, Wang Y, Ying Z, Zhang Y. Virtual touch tissue quantification (Vtq) in the diagnosis of thyroid nodules with coexistent chronic autoimmune hashimoto's thyroiditis: A preliminary study. Eur J Radiol . (2015) 84:327–31. doi: 10.1016/j.ejrad.2014.11.005

51. Cong Y, Fan Z, Dai Y, Zhang Z, Yan K. Application value of shear wave elastography in the evaluation of tumor downstaging for locally advanced rectal cancer after neoadjuvant chemoradiotherapy. J Ultrasound Med . (2021) 40:81–9. doi: 10.1002/jum.15378

52. Lee S, Eun LY, Hwang JY, Eun Y. Ex vivo evaluation of mechanical anisotropic tissues with high-frequency ultrasound shear wave elastography. Sensors (Basel) . (2022) 22:978-93. doi: 10.3390/s22030978

53. Stiver ML, Mirjalili SA, Agur AMR. Measuring shear wave velocity in adult skeletal muscle with ultrasound 2-D shear wave elastography: A scoping review. Ultrasound Med Biol . (2023) 49:1353–62. doi: 10.1016/j.ultrasmedbio.2023.02.005

54. Cloutier G, Cardinal MR, Ju Y, Giroux MF, Lanthier S, Soulez G. Carotid plaque vulnerability assessment using ultrasound elastography and echogenicity analysis. AJR Am J Roentgenol . (2018) 211:847–55. doi: 10.2214/AJR.17.19211

55. Chandra L, Ortiz J, Weitzel W, Hamilton JD, Gao J. Ultrasound elastography detects age-related changes in adult false vocal folds. J Ultrasound Med . (2023) 42:575–83. doi: 10.1002/jum.16033

56. Yin L, Du L, Li Y, Xiao Y, Zhang S, Ma H, et al. Quantitative evaluation of gastrocnemius medialis stiffness during passive stretching using shear wave elastography in patients with parkinson's disease: A prospective preliminary study. Korean J Radiol . (2021) 22:1841–9. doi: 10.3348/kjr.2020.1338

57. Sernik RA, Pereira RFB, Cerri GG, Damasceno RS, Bastos BB, Leao RV. Shear wave elastography is a valuable tool for diagnosing and grading carpal tunnel syndrome. Skeletal Radiol . (2023) 52:67–72. doi: 10.1007/s00256-022-04143-0

58. Huang M, Miller T, Fu SN, Ying MTC, Pang MYC. Structural and passive mechanical properties of the medial gastrocnemius muscle in ambulatory individuals with chronic stroke. Clin Biomech (Bristol Avon) . (2022) 96:105672. doi: 10.1016/j.clinbiomech.2022.105672

59. Gurun E, Akdulum I, Akyuz M, Oktar SO. Shear wave elastography evaluation of brachial plexus in multiple sclerosis. Acta Radiol . (2022) 63:520–6. doi: 10.1177/02841851211002828

60. Kohli DR, Mettman D, Andraws N, Haer E, Porter J, Ulusurac O, et al. Comparative accuracy of endosonographic shear wave elastography and transcutaneous liver stiffness measurement: A pilot study. Gastrointest Endosc . (2023) 97:35–41.e1. doi: 10.1016/j.gie.2022.08.035

61. Wang T, Jirapinyo P, Shah R, Schuster K, Thompson C, Lautz D, et al. Endoscopic ultrasound shear wave elastography is superior to fibroscan and non-invasive scores for fibrosis staging in patients with obesity and non-alcoholic fatty liver disease: A true virtual biopsy. Gastrointestinal Endoscopy . (2023) 97:AB787–AB8. doi: 10.1016/j.gie.2023.04.1286

62. Ivanochko NK, Gatti AA, Stratford PW, Maly MR. Interactions of cumulative load with biomarkers of cartilage turnover predict knee cartilage change over 2 years: data from the osteoarthritis initiative. Clin Rheumatol . (2024) 43:2317–27. doi: 10.1007/s10067-024-07014-2

63. Dreiner M, Willwacher S, Kramer A, Kümmel J, Frett T, Zaucke F, et al. Short-term response of serum cartilage oligomeric matrix protein to different types of impact loading under normal and artificial gravity. Front Physiol . (2020) 11:1032. doi: 10.3389/fphys.2020.01032

64. Brenneman Wilson EC, Gatti AA, Keir PJ, Maly MR. Daily cumulative load and body mass index alter knee cartilage response to running in women. Gait Posture . (2021) 88:192–7. doi: 10.1016/j.gaitpost.2021.05.030

65. Hatta T, Giambini H, Sukegawa K, Yamanaka Y, Sperling JW, Steinmann SP, et al. Quantified mechanical properties of the deltoid muscle using the shear wave elastography: potential implications for reverse shoulder arthroplasty. PloS One . (2016) 11:e0155102. doi: 10.1371/journal.pone.0155102

66. Akkaya S, Akkaya N, Gungor HR, Agladioglu K, Ok N, Ozcakar L. Sonoelastographic evaluation of the distal femoral cartilage in patients with anterior cruciate ligament reconstruction. Eklem Hastalik Cerrahisi . (2016) 27:2–8. doi: 10.5606/ehc.2016.02

67. Yokus A, Toprak M, Arslan H, Toprak N, Akdeniz H, Gunduz AM. Evaluation of distal femoral cartilage by B-mode ultrasonography and shear wave elastography in patients with knee osteoarthritis: A preliminary study. Acta Radiol . (2021) 62:510–4. doi: 10.1177/0284185120930642

68. Zhang X, Lin D, Jiang J, Guo Z. Preliminary study on grading diagnosis of early knee osteoarthritis by shear wave elastography. Contrast Media Mol Imaging . (2022) 2022:4229181. doi: 10.1155/2022/4229181

69. Filippucci E, da Luz KR, Di Geso L, Salaffi F, Tardella M, Carotti M, et al. Interobserver reliability of ultrasonography in the assessment of cartilage damage in rheumatoid arthritis. Ann Rheum Dis . (2010) 69:1845–8. doi: 10.1136/ard.2009.125179

70. Pradsgaard DO, Fiirgaard B, Spannow AH, Heuck C, Herlin T. Cartilage thickness of the knee joint in juvenile idiopathic arthritis: comparative assessment by ultrasonography and magnetic resonance imaging. J Rheumatol . (2015) 42:534–40. doi: 10.3899/jrheum.140162

71. Jain S, Khanduri S, Khan M, Khan S, Yadav VK, Khan BR, et al. Mid-second trimester measurement of nasal bone length in North Indian population. J Clin Imaging Sci . (2019) 9:14. doi: 10.25259/JCIS-15-2019

72. Arjunan SP, Thomas MC. A review of ultrasound imaging techniques for the detection of down syndrome. Irbm . (2020) 41:115–23. doi: 10.1016/j.irbm.2019.10.004

73. Aisen AM, McCune WJ, MacGuire A, Carson PL, Silver TM, Jafri SZ, et al. Sonographic evaluation of the cartilage of the knee. Radiology . (1984) 153:781–4. doi: 10.1148/radiology.153.3.6387794

74. Disler DG, Raymond E, May DA, Wayne JS, McCauley TR. Articular cartilage defects: in vitro evaluation of accuracy and interobserver reliability for detection and grading with us. Radiology . (2000) 215:846–51. doi: 10.1148/radiology.215.3.r00jn20846

75. Devrimsel G, Beyazal MS, Turkyilmaz AK, Sahin SB. Ultrasonographic evaluation of the femoral cartilage thickness in patients with hypothyroidism. J Phys Ther Sci . (2016) 28:2249–52. doi: 10.1589/jpts.28.2249

76. Fiorentino MC, Cipolletta E, Filippucci E, Grassi W, Frontoni E, Moccia S. A deep-learning framework for metacarpal-head cartilage-thickness estimation in ultrasound rheumatological images. Comput Biol Med . (2022) 141:105117. doi: 10.1016/j.compbiomed.2021.105117

77. Faisal A, Ng SC, Goh SL, Lai KW. Knee cartilage segmentation and thickness computation from ultrasound images. Med Biol Eng Comput . (2018) 56:657–69. doi: 10.1007/s11517-017-1710-2

78. Zatloukalova J, Raum K. High frequency ultrasound assesses transient changes in cartilage under osmotic loading. Math Biosci Eng . (2020) 17:5190–211. doi: 10.3934/mbe.2020281

79. Pirri C, Stecco C, Güvener O, Mezian K, Ricci V, Jačisko J, et al. Euro-musculus/usprm dynamic ultrasound protocols for knee. Am J Phys Med Rehabil . (2023) 102:e67–72. doi: 10.1097/phm.0000000000002173

80. Mezian K, Ricci V, Güvener O, Jačisko J, Novotný T, Kara M, et al. Euro-musculus/usprm dynamic ultrasound protocols for (Adult) hip. Am J Phys Med Rehabil . (2022) 101:e162–e8. doi: 10.1097/phm.0000000000002061

81. Okada S, Taniguchi M, Yagi M, Motomura Y, Okada S, Fukumoto Y, et al. Ultrasonographic echo intensity in the medial femoral cartilage is enhanced prior to cartilage thinning in women with early mild knee osteoarthritis. Knee Surg Sports Traumatol Arthrosc . (2023) 31:3964–70. doi: 10.1007/s00167-023-07440-w

82. Okada S, Taniguchi M, Yagi M, Motomura Y, Okada S, Nakazato K, et al. Characteristics of acute cartilage response after mechanical loading in patients with early-mild knee osteoarthritis. Ann BioMed Eng . (2024) 52:1326–34. doi: 10.1007/s10439-024-03456-6

83. Printemps C, Cousin I, Le Lez Soquet S, Saliou P, Josse A, De Vries P, et al. Pulvinar and pubic cartilage measurements to refine universal ultrasound screening for developmental dysplasia of the hip: data from 1896 infant hips. Eur J Radiol . (2021) 139:109727. doi: 10.1016/j.ejrad.2021.109727

84. Harkey MS, Blackburn JT, Davis H, Sierra-Arevalo L, Nissman D, Pietrosimone B. Ultrasonographic assessment of medial femoral cartilage deformation acutely following walking and running. Osteoarthritis Cartilage . (2017) 25:907–13. doi: 10.1016/j.joca.2016.12.026

85. Yildirim A, Onder ME, Ozkan D. Ultrasonographic evaluation of distal femoral and talar cartilage thicknesses in patients with early rheumatoid arthritis and their relationship with disease activity. Clin Rheumatol . (2022) 41:2001–7. doi: 10.1007/s10067-022-06132-z

86. Moller B, Bonel H, Rotzetter M, Villiger PM, Ziswiler HR. Measuring finger joint cartilage by ultrasound as a promising alternative to conventional radiograph imaging. Arthritis Rheum . (2009) 61:435–41. doi: 10.1002/art.24424

87. Kaya A, Kara M, Tiftik T, Tezcan ME, Ozturk MA, Akinci A, et al. Ultrasonographic evaluation of the femoral cartilage thickness in patients with systemic lupus erythematosus. Rheumatol Int . (2013) 33:899–901. doi: 10.1007/s00296-012-2462-9

Keywords: cartilage, osteoarthritis, ultrasonography, knee joint, elasticity imaging techniques

Citation: Zhang H, Ning E, Lu L, Zhou J, Shao Z, Yang X and Hao Y (2024) Research progress of ultrasound in accurate evaluation of cartilage injury in osteoarthritis. Front. Endocrinol. 15:1420049. doi: 10.3389/fendo.2024.1420049

Received: 22 May 2024; Accepted: 25 July 2024; Published: 15 August 2024.

Reviewed by:

Copyright © 2024 Zhang, Ning, Lu, Zhou, Shao, Yang and Hao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Yuefeng Hao, [email protected] ; Xing Yang, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

This paper is in the following e-collection/theme issue:

Published on 14.8.2024 in Vol 26 (2024)

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Quantitative ultrasound parameters offer new tool for diagnosing lung disease

by Matt Shipman, North Carolina State University

Researchers have established a suite of parameters that can be determined using ultrasound to quantitatively measure different physical characteristics of the lung. The researchers also demonstrated that the parameters can be used to accurately diagnose and assess the severity of lung diseases in an animal model.

The paper, " Lung quantitative ultrasound to stage and monitor interstitial lung diseases ," is published open access in the journal Scientific Reports .

"Diseases can affect lungs in many different ways," says Marie Muller, co-senior author of a paper on the work and an associate professor of mechanical and aerospace engineering at North Carolina State University. "They can change the microstructure of the lung, the elasticity of the lung tissue, the type and amount of fluid in the lungs, and so on. Each of these changes can be measured using ultrasound. Our goal with this work was to establish clear parameters for these lung characteristics and determine which combination of parameters is associated with different lung diseases."

"To be clear, we're talking about numeric measurements for each parameter," says Muller, who is also on faculty in the Joint Biomedical Engineering Department at NC State and the University of North Carolina at Chapel Hill.

"So, if there are three parameters associated with a disease, we'd have three numbers—one for each parameter. We can then use a mathematical formula that combines those three numbers to create a biomarker score. That score not only tells us whether a specific health problem is present, but how severe the problem is."

The researchers began by generating parameters for measuring a wide variety of lung characteristics, such as the density of alveoli or the amount of fluid in the lungs. The researchers also adapted existing ultrasound parameters used in other organs for use on lung tissue. Altogether, this resulted in a total of 60 parameters.

The researchers then measured all 60 parameters in the lungs of rats that were healthy or had various stages of fibrosis or edema. Fibrosis is scarring of the lung tissue. Edema refers to fluid build-up in the lung.

"We then used statistical methods to identify which combinations of parameters were both associated with a given health condition and sensitive enough to measure the severity of a health problem," Muller says.

Through this process, the researchers found that only five of the parameters were necessary for assessing fibrosis and edema: three for fibrosis and two for edema.

"One of the challenges with many diagnostic tools is that there is often a trade-off between sensitivity and specificity," Muller says. "A highly sensitive test may virtually guarantee that you detect a problem, but it also usually means that there can be a lot of false positives. On the other hand, a highly specific test will almost never give you a false positive, but it may also miss quite a few health problems it is supposed to detect, or not be able to assess the severity of a specific disease."

"We're excited about this new diagnostic tool because it is both highly sensitive and highly specific," Muller says. "And we're able to have that combination of specificity and sensitivity because we are measuring multiple parameters."

One way they were able to assess the sensitivity of the new tool is by making use of fibrosis treatments. As rats who had fibrosis received treatment, the new diagnostic tool was able to measure improvements in the rats' lung tissue.

The researchers have developed data processing software that can be used in conjunction with existing ultrasound hardware to determine the numbers for each parameter measurement, as well as establishing the biomarker scores for edema and fibrosis.

"We've established that this works well in a rat model," Muller says. "Next steps involve computational simulations, in vitro testing, and animal model testing to establish that this technique can work in cases where the ultrasound has to penetrate a much thicker chest wall. If that goes well, we'll pursue clinical trials.

"Also, because we have established 60 parameters—which is a lot—we're optimistic that this technique can be used in the future to identify diagnostic biomarkers for a range of other lung conditions."

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