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Validity – Types, Examples and Guide

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Definition:

Validity refers to the extent to which a concept, measure, or study accurately represents the intended meaning or reality it is intended to capture. It is a fundamental concept in research and assessment that assesses the soundness and appropriateness of the conclusions, inferences, or interpretations made based on the data or evidence collected.

Research Validity

Research validity refers to the degree to which a study accurately measures or reflects what it claims to measure. In other words, research validity concerns whether the conclusions drawn from a study are based on accurate, reliable and relevant data.

Validity is a concept used in logic and research methodology to assess the strength of an argument or the quality of a research study. It refers to the extent to which a conclusion or result is supported by evidence and reasoning.

How to Ensure Validity in Research

Ensuring validity in research involves several steps and considerations throughout the research process. Here are some key strategies to help maintain research validity:

Clearly Define Research Objectives and Questions

Start by clearly defining your research objectives and formulating specific research questions. This helps focus your study and ensures that you are addressing relevant and meaningful research topics.

Use appropriate research design

Select a research design that aligns with your research objectives and questions. Different types of studies, such as experimental, observational, qualitative, or quantitative, have specific strengths and limitations. Choose the design that best suits your research goals.

Use reliable and valid measurement instruments

If you are measuring variables or constructs, ensure that the measurement instruments you use are reliable and valid. This involves using established and well-tested tools or developing your own instruments through rigorous validation processes.

Ensure a representative sample

When selecting participants or subjects for your study, aim for a sample that is representative of the population you want to generalize to. Consider factors such as age, gender, socioeconomic status, and other relevant demographics to ensure your findings can be generalized appropriately.

Address potential confounding factors

Identify potential confounding variables or biases that could impact your results. Implement strategies such as randomization, matching, or statistical control to minimize the influence of confounding factors and increase internal validity.

Minimize measurement and response biases

Be aware of measurement biases and response biases that can occur during data collection. Use standardized protocols, clear instructions, and trained data collectors to minimize these biases. Employ techniques like blinding or double-blinding in experimental studies to reduce bias.

Conduct appropriate statistical analyses

Ensure that the statistical analyses you employ are appropriate for your research design and data type. Select statistical tests that are relevant to your research questions and use robust analytical techniques to draw accurate conclusions from your data.

Consider external validity

While it may not always be possible to achieve high external validity, be mindful of the generalizability of your findings. Clearly describe your sample and study context to help readers understand the scope and limitations of your research.

Peer review and replication

Submit your research for peer review by experts in your field. Peer review helps identify potential flaws, biases, or methodological issues that can impact validity. Additionally, encourage replication studies by other researchers to validate your findings and enhance the overall reliability of the research.

Transparent reporting

Clearly and transparently report your research methods, procedures, data collection, and analysis techniques. Provide sufficient details for others to evaluate the validity of your study and replicate your work if needed.

Types of Validity

There are several types of validity that researchers consider when designing and evaluating studies. Here are some common types of validity:

Internal Validity

Internal validity relates to the degree to which a study accurately identifies causal relationships between variables. It addresses whether the observed effects can be attributed to the manipulated independent variable rather than confounding factors. Threats to internal validity include selection bias, history effects, maturation of participants, and instrumentation issues.

External Validity

External validity concerns the generalizability of research findings to the broader population or real-world settings. It assesses the extent to which the results can be applied to other individuals, contexts, or timeframes. Factors that can limit external validity include sample characteristics, research settings, and the specific conditions under which the study was conducted.

Construct Validity

Construct validity examines whether a study adequately measures the intended theoretical constructs or concepts. It focuses on the alignment between the operational definitions used in the study and the underlying theoretical constructs. Construct validity can be threatened by issues such as poor measurement tools, inadequate operational definitions, or a lack of clarity in the conceptual framework.

Content Validity

Content validity refers to the degree to which a measurement instrument or test adequately covers the entire range of the construct being measured. It assesses whether the items or questions included in the measurement tool represent the full scope of the construct. Content validity is often evaluated through expert judgment, reviewing the relevance and representativeness of the items.

Criterion Validity

Criterion validity determines the extent to which a measure or test is related to an external criterion or standard. It assesses whether the results obtained from a measurement instrument align with other established measures or outcomes. Criterion validity can be divided into two subtypes: concurrent validity, which examines the relationship between the measure and the criterion at the same time, and predictive validity, which investigates the measure’s ability to predict future outcomes.

Face Validity

Face validity refers to the degree to which a measurement or test appears, on the surface, to measure what it intends to measure. It is a subjective assessment based on whether the items seem relevant and appropriate to the construct being measured. Face validity is often used as an initial evaluation before conducting more rigorous validity assessments.

Importance of Validity

Validity is crucial in research for several reasons:

• Accurate Measurement: Validity ensures that the measurements or observations in a study accurately represent the intended constructs or variables. Without validity, researchers cannot be confident that their results truly reflect the phenomena they are studying. Validity allows researchers to draw accurate conclusions and make meaningful inferences based on their findings.
• Credibility and Trustworthiness: Validity enhances the credibility and trustworthiness of research. When a study demonstrates high validity, it indicates that the researchers have taken appropriate measures to ensure the accuracy and integrity of their work. This strengthens the confidence of other researchers, peers, and the wider scientific community in the study’s results and conclusions.
• Generalizability: Validity helps determine the extent to which research findings can be generalized beyond the specific sample and context of the study. By addressing external validity, researchers can assess whether their results can be applied to other populations, settings, or situations. This information is valuable for making informed decisions, implementing interventions, or developing policies based on research findings.
• Sound Decision-Making: Validity supports informed decision-making in various fields, such as medicine, psychology, education, and social sciences. When validity is established, policymakers, practitioners, and professionals can rely on research findings to guide their actions and interventions. Validity ensures that decisions are based on accurate and trustworthy information, which can lead to better outcomes and more effective practices.
• Avoiding Errors and Bias: Validity helps researchers identify and mitigate potential errors and biases in their studies. By addressing internal validity, researchers can minimize confounding factors and alternative explanations, ensuring that the observed effects are genuinely attributable to the manipulated variables. Validity assessments also highlight measurement errors or shortcomings, enabling researchers to improve their measurement tools and procedures.
• Progress of Scientific Knowledge: Validity is essential for the advancement of scientific knowledge. Valid research contributes to the accumulation of reliable and valid evidence, which forms the foundation for building theories, developing models, and refining existing knowledge. Validity allows researchers to build upon previous findings, replicate studies, and establish a cumulative body of knowledge in various disciplines. Without validity, the scientific community would struggle to make meaningful progress and establish a solid understanding of the phenomena under investigation.
• Ethical Considerations: Validity is closely linked to ethical considerations in research. Conducting valid research ensures that participants’ time, effort, and data are not wasted on flawed or invalid studies. It upholds the principle of respect for participants’ autonomy and promotes responsible research practices. Validity is also important when making claims or drawing conclusions that may have real-world implications, as misleading or invalid findings can have adverse effects on individuals, organizations, or society as a whole.

Examples of Validity

Here are some examples of validity in different contexts:

• Example 1: All men are mortal. John is a man. Therefore, John is mortal. This argument is logically valid because the conclusion follows logically from the premises.
• Example 2: If it is raining, then the ground is wet. The ground is wet. Therefore, it is raining. This argument is not logically valid because there could be other reasons for the ground being wet, such as watering the plants.
• Example 1: In a study examining the relationship between caffeine consumption and alertness, the researchers use established measures of both variables, ensuring that they are accurately capturing the concepts they intend to measure. This demonstrates construct validity.
• Example 2: A researcher develops a new questionnaire to measure anxiety levels. They administer the questionnaire to a group of participants and find that it correlates highly with other established anxiety measures. This indicates good construct validity for the new questionnaire.
• Example 1: A study on the effects of a particular teaching method is conducted in a controlled laboratory setting. The findings of the study may lack external validity because the conditions in the lab may not accurately reflect real-world classroom settings.
• Example 2: A research study on the effects of a new medication includes participants from diverse backgrounds and age groups, increasing the external validity of the findings to a broader population.
• Example 1: In an experiment, a researcher manipulates the independent variable (e.g., a new drug) and controls for other variables to ensure that any observed effects on the dependent variable (e.g., symptom reduction) are indeed due to the manipulation. This establishes internal validity.
• Example 2: A researcher conducts a study examining the relationship between exercise and mood by administering questionnaires to participants. However, the study lacks internal validity because it does not control for other potential factors that could influence mood, such as diet or stress levels.
• Example 1: A teacher develops a new test to assess students’ knowledge of a particular subject. The items on the test appear to be relevant to the topic at hand and align with what one would expect to find on such a test. This suggests face validity, as the test appears to measure what it intends to measure.
• Example 2: A company develops a new customer satisfaction survey. The questions included in the survey seem to address key aspects of the customer experience and capture the relevant information. This indicates face validity, as the survey seems appropriate for assessing customer satisfaction.
• Example 1: A team of experts reviews a comprehensive curriculum for a high school biology course. They evaluate the curriculum to ensure that it covers all the essential topics and concepts necessary for students to gain a thorough understanding of biology. This demonstrates content validity, as the curriculum is representative of the domain it intends to cover.
• Example 2: A researcher develops a questionnaire to assess career satisfaction. The questions in the questionnaire encompass various dimensions of job satisfaction, such as salary, work-life balance, and career growth. This indicates content validity, as the questionnaire adequately represents the different aspects of career satisfaction.
• Example 1: A company wants to evaluate the effectiveness of a new employee selection test. They administer the test to a group of job applicants and later assess the job performance of those who were hired. If there is a strong correlation between the test scores and subsequent job performance, it suggests criterion validity, indicating that the test is predictive of job success.
• Example 2: A researcher wants to determine if a new medical diagnostic tool accurately identifies a specific disease. They compare the results of the diagnostic tool with the gold standard diagnostic method and find a high level of agreement. This demonstrates criterion validity, indicating that the new tool is valid in accurately diagnosing the disease.

Where to Write About Validity in A Thesis

In a thesis, discussions related to validity are typically included in the methodology and results sections. Here are some specific places where you can address validity within your thesis:

Research Design and Methodology

In the methodology section, provide a clear and detailed description of the measures, instruments, or data collection methods used in your study. Discuss the steps taken to establish or assess the validity of these measures. Explain the rationale behind the selection of specific validity types relevant to your study, such as content validity, criterion validity, or construct validity. Discuss any modifications or adaptations made to existing measures and their potential impact on validity.

Measurement Procedures

In the methodology section, elaborate on the procedures implemented to ensure the validity of measurements. Describe how potential biases or confounding factors were addressed, controlled, or accounted for to enhance internal validity. Provide details on how you ensured that the measurement process accurately captures the intended constructs or variables of interest.

Data Collection

In the methodology section, discuss the steps taken to collect data and ensure data validity. Explain any measures implemented to minimize errors or biases during data collection, such as training of data collectors, standardized protocols, or quality control procedures. Address any potential limitations or threats to validity related to the data collection process.

Data Analysis and Results

In the results section, present the analysis and findings related to validity. Report any statistical tests, correlations, or other measures used to assess validity. Provide interpretations and explanations of the results obtained. Discuss the implications of the validity findings for the overall reliability and credibility of your study.

Limitations and Future Directions

In the discussion or conclusion section, reflect on the limitations of your study, including limitations related to validity. Acknowledge any potential threats or weaknesses to validity that you encountered during your research. Discuss how these limitations may have influenced the interpretation of your findings and suggest avenues for future research that could address these validity concerns.

Applications of Validity

Validity is applicable in various areas and contexts where research and measurement play a role. Here are some common applications of validity:

Psychological and Behavioral Research

Validity is crucial in psychology and behavioral research to ensure that measurement instruments accurately capture constructs such as personality traits, intelligence, attitudes, emotions, or psychological disorders. Validity assessments help researchers determine if their measures are truly measuring the intended psychological constructs and if the results can be generalized to broader populations or real-world settings.

Educational Assessment

Validity is essential in educational assessment to determine if tests, exams, or assessments accurately measure students’ knowledge, skills, or abilities. It ensures that the assessment aligns with the educational objectives and provides reliable information about student performance. Validity assessments help identify if the assessment is valid for all students, regardless of their demographic characteristics, language proficiency, or cultural background.

Program Evaluation

Validity plays a crucial role in program evaluation, where researchers assess the effectiveness and impact of interventions, policies, or programs. By establishing validity, evaluators can determine if the observed outcomes are genuinely attributable to the program being evaluated rather than extraneous factors. Validity assessments also help ensure that the evaluation findings are applicable to different populations, contexts, or timeframes.

Medical and Health Research

Validity is essential in medical and health research to ensure the accuracy and reliability of diagnostic tools, measurement instruments, and clinical assessments. Validity assessments help determine if a measurement accurately identifies the presence or absence of a medical condition, measures the effectiveness of a treatment, or predicts patient outcomes. Validity is crucial for establishing evidence-based medicine and informing medical decision-making.

Social Science Research

Validity is relevant in various social science disciplines, including sociology, anthropology, economics, and political science. Researchers use validity to ensure that their measures and methods accurately capture social phenomena, such as social attitudes, behaviors, social structures, or economic indicators. Validity assessments support the reliability and credibility of social science research findings.

Market Research and Surveys

Validity is important in market research and survey studies to ensure that the survey questions effectively measure consumer preferences, buying behaviors, or attitudes towards products or services. Validity assessments help researchers determine if the survey instrument is accurately capturing the desired information and if the results can be generalized to the target population.

Limitations of Validity

Here are some limitations of validity:

• Construct Validity: Limitations of construct validity include the potential for measurement error, inadequate operational definitions of constructs, or the failure to capture all aspects of a complex construct.
• Internal Validity: Limitations of internal validity may arise from confounding variables, selection bias, or the presence of extraneous factors that could influence the study outcomes, making it difficult to attribute causality accurately.
• External Validity: Limitations of external validity can occur when the study sample does not represent the broader population, when the research setting differs significantly from real-world conditions, or when the study lacks ecological validity, i.e., the findings do not reflect real-world complexities.
• Measurement Validity: Limitations of measurement validity can arise from measurement error, inadequately designed or flawed measurement scales, or limitations inherent in self-report measures, such as social desirability bias or recall bias.
• Statistical Conclusion Validity: Limitations in statistical conclusion validity can occur due to sampling errors, inadequate sample sizes, or improper statistical analysis techniques, leading to incorrect conclusions or generalizations.
• Temporal Validity: Limitations of temporal validity arise when the study results become outdated due to changes in the studied phenomena, interventions, or contextual factors.
• Researcher Bias: Researcher bias can affect the validity of a study. Biases can emerge through the researcher’s subjective interpretation, influence of personal beliefs, or preconceived notions, leading to unintentional distortion of findings or failure to consider alternative explanations.
• Ethical Validity: Limitations can arise if the study design or methods involve ethical concerns, such as the use of deceptive practices, inadequate informed consent, or potential harm to participants.

Also see  Reliability Vs Validity

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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1 Validity and Validation in Research and Assessment

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This chapter first sets out the book's purpose, namely to further define validity and to explore the factors that should be considered when evaluating claims from research and assessment. It then discusses validity theory and its philosophical foundations, with connections between the philosophical foundations and specific ways validation is considered in research and measurement. An overview of the subsequent chapters is also presented.

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Qualitative Research and Content Validity

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• First Online: 01 January 2024
• pp 5658–5666
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• Meryl Brod 2 ,
• Betsy Pohlman 2 &
• Laura Tesler Waldman 3  

Index of sustainable economic well-being

Qualitative research investigates events that are difficult to quantify mathematically, such as beliefs.

Content validity , derived during concept elicitation, is the measurement property that assesses whether items are comprehensive and adequately reflect the patient perspective for the population of interest.

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Establishing content validity for both new and existing patient-reported outcome (PRO) measures is central to a scientifically sound instrument development process (Brod et al. 2009 ). Content validity, derived during concept elicitation, is the measurement property that assesses whether items are comprehensive and adequately reflect the patient perspective for the population of interest. Content validation provides evidence that the conceptual framework, content of items, and overall measurement approach are consistent with the perspective, experience, and words of the patient group and is necessary to meet...

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Beatty, P., & Willis, G. (2007). Research synthesis: The practice of cognitive interviewing. Public Opinion Quarterly, 71 (2), 287–311.

Article   Google Scholar  

Brod, M., Tesler, L., & Christensen, T. (2009). Qualitative research and content validity: Developing best practices based on science and experience. Quality of Life Research, 18 (9), 1263–1278.

Charmaz, K. (2003). Qualitative interviewing and grounded theory analysis. In J. Holstein & J. Gubrium (Eds.), Inside interviewing: New lenses, new concerns (pp. 311–330). Thousand Oaks: SAGE.

Google Scholar  

Corbin, J., & Strauss, A. (1990). Grounded theory research: Procedures, canons and evaluative criteria. Qualitative Sociology, 13 (1), 3–21.

Corbin, J., & Strauss, A. (2008). Basics of qualitative research (3rd ed.). Newbury Park: SAGE.

Cutcliffe, J. (2000). Methodological issues in grounded theory. Journal of Advanced Nursing, 31 (6), 1476–1484.

FDA. (2009). Patient-reported outcome measures: Use in medical product development to support labeling claims . Rockville: Food and Drug Administration. http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM193282.pdf .

Frost, M., Reeve, B., Liepa, A., Stauffer, J., & Hays, R. (2007). What is sufficient evidence for the reliability and validity of patient-reported outcome measures? Value in Health, 10 (2), S94–S105.

Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough?: An experiment with data saturation and variability. Field Methods, 18 (1), 59–82.

Lasch, K., Marquis, P., Vigneux, M., Abetz, L., Arnould, B., Bayliss, M., et al. (2010). PRO development: Rigorous qualitative research as the crucial foundation. Quality of Life Research, 19 (8), 1087–1096.

Leidy, N., & Vernon, M. (2008). Perspectives on patient-reported outcomes: Content validity and qualitative research in a changing clinical trial environment. PharmacoEconomics, 26 (5), 363–370.

Magasi, S., Ryan, G., Revicki, D., Lenderking, W., Hays, R., Brod, M., et al. (2012). Content validity of patient-reported outcome measures: Perspectives from a PROMIS meeting. Quality of Life Research, 21 (5), 739–746.

Morgan, D. (1996). Focus groups. Annual Review of Sociology, 22 , 129–152.

Patrick, D., Burke, L., Powers, J., Scott, J., Rock, E., Dawisha, S., O’Neill, R., et al. (2007). Patient-reported outcomes to support medical product labeling claims: FDA perspective. Value in Health, 10 (2), S125–S137.

Patrick, D., Burke, L., Gwaltney, C., Leidy, N., Martin, M., Molsen, E., et al. (2011a). Content validity – Establishing and reporting the evidence in newly developed patient-reported outcomes (PRO) instruments for medical product evaluation: ISPOR PRO good research practices task force report: Part 1 – Eliciting concepts for a new PRO instrument. Value in Health, 14 (8), 967–977.

Patrick, D., Burke, L., Gwaltney, C., Leidy, N., Martin, M., Molsen, E., et al. (2011b). Content validity – Establishing and reporting the evidence in newly developed patient-reported outcomes (PRO) instruments for medical product evaluation: ISPOR PRO good research practices task force report: Part 2 – Assessing respondent understanding. Value in Health, 14 (8), 978–988.

Patton, M. (2002). Qualitative research and evaluation methods (3rd ed.). Thousand Oaks: SAGE.

Poland, B. (2003). Transcription quality. In J. Holstein & J. Gubrium (Eds.), Inside interviewing: New lenses, new concerns . Thousand Oaks: SAGE.

Ritchie, J., Spencer, L., & O’Connor, W. (2003). Carrying out qualitative analysis. In J. Ritchie & J. Lewis (Eds.), Qualitative research practice: A guide for social science students and researchers (pp. 219–262). London: SAGE.

Rothman, M., Beltran, P., Cappelleri, J., Lipscomb, J., & Teschendorf, B. (2007). Patient-reported outcomes: Conceptual issues. Value in Health, 10 (2), S66–S75.

Stewart, D., Shamdasani, P., & Rook, D. (2007). Focus groups (2nd ed.). Thousand Oaks: SAGE.

Book   Google Scholar  

Turner, R., Quittner, A., Parasuraman, B., Kallich, J., & Cleeland, C. (2007). Patient-reported outcomes: Instrument development and selection issues. Value in Health, 10 (2), S86–S93.

Willis, G. (1999). Cognitive interviewing: A “how to” guide . Research Triangle Park: Research Triangle Institute. http://appliedresearch.cancer.gov/areas/cognitive/interview.pdf .

Willis, G. (2004). Cognitive interviewing revisited: A useful technique, in theory? In S. Presser, J. Rothgeb, M. Couper, J. Lessler, E. Martin, & E. Singer (Eds.), Methods for testing and evaluating survey questionnaires (pp. 23–44). Hoboken: Wiley.

Chapter   Google Scholar  

Willis, G. (2005). Cognitive interviewing: A tool for improving questionnaire design . Thousand Oaks: SAGE.

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Brod, M., Pohlman, B., Waldman, L.T. (2023). Qualitative Research and Content Validity. In: Maggino, F. (eds) Encyclopedia of Quality of Life and Well-Being Research. Springer, Cham. https://doi.org/10.1007/978-3-031-17299-1_3848

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5.13: The Reliability and Validity of Research

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Learning Objectives

• Define reliability and validity

Interpreting Experimental Findings

Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this experiment 100 times, we would expect to find the same results at least 95 times out of 100.

The greatest strength of experiments is the ability to assert that any significant differences in the findings are caused by the independent variable. This occurs because random selection, random assignment, and a design that limits the effects of both experimenter bias and participant expectancy should create groups that are similar in composition and treatment. Therefore, any difference between the groups is attributable to the independent variable, and now we can finally make a causal statement. If we find that watching a violent television program results in more violent behavior than watching a nonviolent program, we can safely say that watching violent television programs causes an increase in the display of violent behavior.

Reporting Research

When psychologists complete a research project, they generally want to share their findings with other scientists. The American Psychological Association (APA) publishes a manual detailing how to write a paper for submission to scientific journals. Unlike an article that might be published in a magazine like Psychology Today, which targets a general audience with an interest in psychology, scientific journals generally publish peer-reviewed journal articles aimed at an audience of professionals and scholars who are actively involved in research themselves.

Link to Learning

The Online Writing Lab (OWL) at Purdue University can walk you through the APA writing guidelines.

A peer-reviewed journal article is read by several other scientists (generally anonymously) with expertise in the subject matter. These peer reviewers provide feedback—to both the author and the journal editor—regarding the quality of the draft. Peer reviewers look for a strong rationale for the research being described, a clear description of how the research was conducted, and evidence that the research was conducted in an ethical manner. They also look for flaws in the study’s design, methods, and statistical analyses. They check that the conclusions drawn by the authors seem reasonable given the observations made during the research. Peer reviewers also comment on how valuable the research is in advancing the discipline’s knowledge. This helps prevent unnecessary duplication of research findings in the scientific literature and, to some extent, ensures that each research article provides new information. Ultimately, the journal editor will compile all of the peer reviewer feedback and determine whether the article will be published in its current state (a rare occurrence), published with revisions, or not accepted for publication.

Peer review provides some degree of quality control for psychological research. Poorly conceived or executed studies can be weeded out, and even well-designed research can be improved by the revisions suggested. Peer review also ensures that the research is described clearly enough to allow other scientists to replicate it, meaning they can repeat the experiment using different samples to determine reliability. Sometimes replications involve additional measures that expand on the original finding. In any case, each replication serves to provide more evidence to support the original research findings. Successful replications of published research make scientists more apt to adopt those findings, while repeated failures tend to cast doubt on the legitimacy of the original article and lead scientists to look elsewhere. For example, it would be a major advancement in the medical field if a published study indicated that taking a new drug helped individuals achieve a healthy weight without changing their diet. But if other scientists could not replicate the results, the original study’s claims would be questioned.

Dig Deeper: The Vaccine-Autism Myth and the Retraction of Published Studies

Some scientists have claimed that routine childhood vaccines cause some children to develop autism, and, in fact, several peer-reviewed publications published research making these claims. Since the initial reports, large-scale epidemiological research has suggested that vaccinations are not responsible for causing autism and that it is much safer to have your child vaccinated than not. Furthermore, several of the original studies making this claim have since been retracted.

A published piece of work can be rescinded when data is called into question because of falsification, fabrication, or serious research design problems. Once rescinded, the scientific community is informed that there are serious problems with the original publication. Retractions can be initiated by the researcher who led the study, by research collaborators, by the institution that employed the researcher, or by the editorial board of the journal in which the article was originally published. In the vaccine-autism case, the retraction was made because of a significant conflict of interest in which the leading researcher had a financial interest in establishing a link between childhood vaccines and autism (Offit, 2008). Unfortunately, the initial studies received so much media attention that many parents around the world became hesitant to have their children vaccinated (Figure 1). For more information about how the vaccine/autism story unfolded, as well as the repercussions of this story, take a look at Paul Offit’s book, Autism’s False Prophets: Bad Science, Risky Medicine, and the Search for a Cure.

Reliability and Validity

Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways. Unfortunately, being consistent in measurement does not necessarily mean that you have measured something correctly. This is where validity comes into play. Validity refers to the extent to which a given instrument or tool accurately measures what it’s supposed to measure. While any valid measure is by necessity reliable, the reverse is not necessarily true. Researchers strive to use instruments that are both highly reliable and valid.

Query \(\PageIndex{1}\)

Everyday Connection: How Valid Is the SAT?

Standardized tests like the SAT are supposed to measure an individual’s aptitude for a college education, but how reliable and valid are such tests? Research conducted by the College Board suggests that scores on the SAT have high predictive validity for first-year college students’ GPA (Kobrin, Patterson, Shaw, Mattern, & Barbuti, 2008). In this context, predictive validity refers to the test’s ability to effectively predict the GPA of college freshmen. Given that many institutions of higher education require the SAT for admission, this high degree of predictive validity might be comforting.

However, the emphasis placed on SAT scores in college admissions has generated some controversy on a number of fronts. For one, some researchers assert that the SAT is a biased test that places minority students at a disadvantage and unfairly reduces the likelihood of being admitted into a college (Santelices & Wilson, 2010). Additionally, some research has suggested that the predictive validity of the SAT is grossly exaggerated in how well it is able to predict the GPA of first-year college students. In fact, it has been suggested that the SAT’s predictive validity may be overestimated by as much as 150% (Rothstein, 2004). Many institutions of higher education are beginning to consider de-emphasizing the significance of SAT scores in making admission decisions (Rimer, 2008).

Recent examples of high profile cheating scandals both domestically and abroad have only increased the scrutiny being placed on these types of tests, and as of March 2019, more than 1000 institutions of higher education have either relaxed or eliminated the requirements for SAT or ACT testing for admissions (Strauss, 2019, March 19).

Query \(\PageIndex{2}\)

Query \(\PageIndex{3}\)

reliability:  consistency and reproducibility of a given result

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• Analyzing Findings. Authored by : OpenStax College. Located at : http://cnx.org/contents/[email protected]:mfArybye@7/Analyzing-Findings . License : CC BY: Attribution . License Terms : Download for free at http://cnx.org/contents/[email protected]

• How it works

Reliability and Validity – Definitions, Types & Examples

Published by Alvin Nicolas at August 16th, 2021 , Revised On October 26, 2023

A researcher must test the collected data before making any conclusion. Every  research design  needs to be concerned with reliability and validity to measure the quality of the research.

What is Reliability?

Reliability refers to the consistency of the measurement. Reliability shows how trustworthy is the score of the test. If the collected data shows the same results after being tested using various methods and sample groups, the information is reliable. If your method has reliability, the results will be valid.

Example: If you weigh yourself on a weighing scale throughout the day, you’ll get the same results. These are considered reliable results obtained through repeated measures.

Example: If a teacher conducts the same math test of students and repeats it next week with the same questions. If she gets the same score, then the reliability of the test is high.

What is the Validity?

Validity refers to the accuracy of the measurement. Validity shows how a specific test is suitable for a particular situation. If the results are accurate according to the researcher’s situation, explanation, and prediction, then the research is valid. 

If the method of measuring is accurate, then it’ll produce accurate results. If a method is reliable, then it’s valid. In contrast, if a method is not reliable, it’s not valid. 

Example:  Your weighing scale shows different results each time you weigh yourself within a day even after handling it carefully, and weighing before and after meals. Your weighing machine might be malfunctioning. It means your method had low reliability. Hence you are getting inaccurate or inconsistent results that are not valid.

Example:  Suppose a questionnaire is distributed among a group of people to check the quality of a skincare product and repeated the same questionnaire with many groups. If you get the same response from various participants, it means the validity of the questionnaire and product is high as it has high reliability.

Most of the time, validity is difficult to measure even though the process of measurement is reliable. It isn’t easy to interpret the real situation.

Example:  If the weighing scale shows the same result, let’s say 70 kg each time, even if your actual weight is 55 kg, then it means the weighing scale is malfunctioning. However, it was showing consistent results, but it cannot be considered as reliable. It means the method has low reliability.

Internal Vs. External Validity

One of the key features of randomised designs is that they have significantly high internal and external validity.

Internal validity  is the ability to draw a causal link between your treatment and the dependent variable of interest. It means the observed changes should be due to the experiment conducted, and any external factor should not influence the  variables .

Example: age, level, height, and grade.

External validity  is the ability to identify and generalise your study outcomes to the population at large. The relationship between the study’s situation and the situations outside the study is considered external validity.

Also, read about Inductive vs Deductive reasoning in this article.

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Threats to Interval Validity

Threats of external validity, how to assess reliability and validity.

Reliability can be measured by comparing the consistency of the procedure and its results. There are various methods to measure validity and reliability. Reliability can be measured through  various statistical methods  depending on the types of validity, as explained below:

Types of Reliability

Types of validity.

As we discussed above, the reliability of the measurement alone cannot determine its validity. Validity is difficult to be measured even if the method is reliable. The following type of tests is conducted for measuring validity. 

Does your Research Methodology Have the Following?

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If not, we can help. Our panel of experts makes sure to keep the 3 pillars of Research Methodology strong.

How to Increase Reliability?

• Use an appropriate questionnaire to measure the competency level.
• Ensure a consistent environment for participants
• Make the participants familiar with the criteria of assessment.
• Train the participants appropriately.
• Analyse the research items regularly to avoid poor performance.

How to Increase Validity?

Ensuring Validity is also not an easy job. A proper functioning method to ensure validity is given below:

• The reactivity should be minimised at the first concern.
• The Hawthorne effect should be reduced.
• The respondents should be motivated.
• The intervals between the pre-test and post-test should not be lengthy.
• Dropout rates should be avoided.
• The inter-rater reliability should be ensured.
• Control and experimental groups should be matched with each other.

How to Implement Reliability and Validity in your Thesis?

According to the experts, it is helpful if to implement the concept of reliability and Validity. Especially, in the thesis and the dissertation, these concepts are adopted much. The method for implementation given below:

Frequently Asked Questions

What is reliability and validity in research.

Reliability in research refers to the consistency and stability of measurements or findings. Validity relates to the accuracy and truthfulness of results, measuring what the study intends to. Both are crucial for trustworthy and credible research outcomes.

What is validity?

Validity in research refers to the extent to which a study accurately measures what it intends to measure. It ensures that the results are truly representative of the phenomena under investigation. Without validity, research findings may be irrelevant, misleading, or incorrect, limiting their applicability and credibility.

What is reliability?

Reliability in research refers to the consistency and stability of measurements over time. If a study is reliable, repeating the experiment or test under the same conditions should produce similar results. Without reliability, findings become unpredictable and lack dependability, potentially undermining the study’s credibility and generalisability.

What is reliability in psychology?

In psychology, reliability refers to the consistency of a measurement tool or test. A reliable psychological assessment produces stable and consistent results across different times, situations, or raters. It ensures that an instrument’s scores are not due to random error, making the findings dependable and reproducible in similar conditions.

What is test retest reliability?

Test-retest reliability assesses the consistency of measurements taken by a test over time. It involves administering the same test to the same participants at two different points in time and comparing the results. A high correlation between the scores indicates that the test produces stable and consistent results over time.

How to improve reliability of an experiment?

• Standardise procedures and instructions.
• Use consistent and precise measurement tools.
• Train observers or raters to reduce subjective judgments.
• Increase sample size to reduce random errors.
• Conduct pilot studies to refine methods.
• Repeat measurements or use multiple methods.
• Address potential sources of variability.

What is the difference between reliability and validity?

Reliability refers to the consistency and repeatability of measurements, ensuring results are stable over time. Validity indicates how well an instrument measures what it’s intended to measure, ensuring accuracy and relevance. While a test can be reliable without being valid, a valid test must inherently be reliable. Both are essential for credible research.

Are interviews reliable and valid?

Interviews can be both reliable and valid, but they are susceptible to biases. The reliability and validity depend on the design, structure, and execution of the interview. Structured interviews with standardised questions improve reliability. Validity is enhanced when questions accurately capture the intended construct and when interviewer biases are minimised.

Are IQ tests valid and reliable?

IQ tests are generally considered reliable, producing consistent scores over time. Their validity, however, is a subject of debate. While they effectively measure certain cognitive skills, whether they capture the entirety of “intelligence” or predict success in all life areas is contested. Cultural bias and over-reliance on tests are also concerns.

Are questionnaires reliable and valid?

Questionnaires can be both reliable and valid if well-designed. Reliability is achieved when they produce consistent results over time or across similar populations. Validity is ensured when questions accurately measure the intended construct. However, factors like poorly phrased questions, respondent bias, and lack of standardisation can compromise their reliability and validity.

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• What Is Content Validity? | Definition & Examples

What Is Content Validity? | Definition & Examples

Published on 2 September 2022 by Kassiani Nikolopoulou . Revised on 10 October 2022.

Content validity evaluates how well an instrument (like a test) covers all relevant parts of the construct it aims to measure. Here, a construct is a theoretical concept, theme, or idea – in particular, one that cannot usually be measured directly.

Content validity is one of the four types of measurement validity . The other three are:

• Face validity : Does the content of the test appear to be suitable for its aims?
• Criterion validity : Do the results accurately measure the concrete outcome they are designed to measure?
• Construct validity : Does the test measure the concept that it’s intended to measure?

Table of contents

Content validity examples, step-by-step guide: how to measure content validity, frequently asked questions about content validity.

Some constructs are directly observable or tangible, and thus easier to measure. For example, height is measured in inches. Other constructs are more difficult to measure. Depression, for instance, consists of several dimensions and cannot be measured directly.

Additionally, in order to achieve content validity, there has to be a degree of general agreement, for example among experts, about what a particular construct represents.

Research has shown that there are at least three different components that make up intelligence: short-term memory, reasoning, and a verbal component.

Construct vs. content validity example

It can be easy to confuse construct validity and content validity, but they are fundamentally different concepts.

Construct validity evaluates how well a test measures what it is intended to measure. If any parts of the construct are missing, or irrelevant parts are included, construct validity will be compromised. Remember that in order to establish construct validity, you must demonstrate both convergent and divergent (or discriminant) validity .

• Convergent validity shows whether a test that is designed to measure a particular construct correlates with other tests that assess the same construct.
• Divergent (or discriminant) validity shows you whether two tests that should not be highly related to each other are indeed unrelated. There should be little to no relationship between the scores of two tests measuring two different constructs.

On the other hand, content validity applies to any context where you create a test or questionnaire for a particular construct and want to ensure that the questions actually measure what you intend them to.

• High content validity. If your survey questions cover all dimensions of health needs –   physical, mental, social, and environmental – your questionnaire will have high content validity.
• Low content validity. If most of your survey questions relate to the attitude of the study population towards the health services provided to them instead of to health needs , the results are no longer a valid measure of community health needs.
• Low construct validity. If some dimensions of health needs are left out, then the results may not give an accurate indication of the health needs of the community due to poor operationalisation of the concept.

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Measuring content validity correctly is important – a high content validity score shows that the construct was measured accurately. You can measure content validity following the step-by-step guide below:

Step 1: Collect data from experts

Step 2: calculate the content validity ratio, step 3: calculate the content validity index.

Measuring content validity requires input from a judging panel of subject matter experts (SMEs). Here, SMEs are people who are in the best position to evaluate the content of a test.

For example, the expert panel for a school math test would consist of qualified math teachers who teach that subject.

For each individual question, the panel must assess whether the component measured by the question is ‘essential’, ‘useful’, but ‘not essential’, or ‘not necessary’ for measuring the construct.

The higher the agreement among panelists that a particular item is essential, the higher that item’s level of content validity is.

Next, you can use the following formula to calculate the content validity ratio (CVR) for each question:

Content Validity Ratio = (ne − N/2) / (N/2) where:

• ne = number of SME panelists indicating ‘essential’
• N = total number of SME panelists

The content validity ratio for the first question would be calculated as:

Using the same formula, you calculate the CVR for each question.

Note that this formula yields values which range from +1 to −1. Values above 0 indicate that at least half the SMEs agree that the question is essential. The closer to +1, the higher the content validity.

However, agreement could be due to coincidence. In order to rule that out, you can use the critical values table below. Depending on the number of experts in the panel, the content validity ratio (CVR) for a given question should not fall below a minimum value, also called the critical value.

To measure the content validity of the entire test, you need to calculate the content validity index (CVI) . The CVI is the average CVR score of all questions in the test. Remember that values closer to 1 denote higher content validity.

To calculate the content validity index (CVI) of the entire test, you take the average of all the CVR scores of the seven questions.

Here, that would be:

Comparing the CVI with the critical value for a panel of 5 experts (0.99), you notice that the CVI is too low. This means that the test does not accurately measure what you intended it to. You decide to improve the questions with a low CVR, in order to get a higher CVI.

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

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

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

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

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

A glossary is a collection of words pertaining to a specific topic. In your thesis or dissertation, it’s a list of all terms you used that may not immediately be obvious to your reader. In contrast, an index is a list of the contents of your work organised by page number.

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

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

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

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

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Content Validity in Research: Definition & Examples

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• Content validity is a type of criterion validity that demonstrates how well a measure covers the construct it is meant to represent.
• It is important for researchers to establish content validity in order to ensure that their study is measuring what it intends to measure.
• There are several ways to establish content validity, including expert opinion, focus groups , and surveys.

What Is Content Validity?

Content Validity is the degree to which elements of an assessment instrument are relevant to a representative of the targeted construct for a particular assessment purpose.

This encompasses aspects such as the appropriateness of the items, tasks, or questions to the specific domain being measured and whether the assessment instrument covers a broad enough range of content to enable conclusions to be drawn about the targeted construct (Rossiter, 2008).

One example of an assessment with high content validity is the Iowa Test of Basic Skills (ITBS). The ITBS is a standardized test that has been used since 1935 to assess the academic achievement of students in grades 3-8.

The test covers a wide range of academic skills, including reading, math, language arts, and social studies. The items on the test are carefully developed and reviewed by a panel of experts to ensure that they are fair and representative of the skills being tested.

As a result, the ITBS has high content validity and is widely used by schools and districts to measure student achievement.

Meanwhile, most driving tests have low content validity.  The questions on the test are often not representative of the skills needed to drive safely. For example, many driving permit tests do not include questions about how to parallel park or how to change lanes.

Meanwhile, driving license tests often do not test drivers in non-ideal conditions, such as rain or snow. As a result, these tests do not provide an accurate measure of a person’s ability to drive safely.

The higher the content validity of an assessment, the more accurately it can measure what it is intended to measure — the target construct (Rossiter, 2008).

Why is content validity important in research?

Content validity is important in research as it provides confidence that an instrument is measuring what it is supposed to be measuring.

This is particularly relevant when developing new measures or adapting existing ones for use with different populations.

It also has implications for the interpretation of results, as findings can only be accurately applied to groups for which the content validity of the measure has been established.

Step-by-step guide: How to measure content validity?

Haynes et al. (1995) emphasized the importance of content validity and gave an overview of ways to assess it.

One of the first ways of measuring content validity was the Delphi method, which was invented by NASA in 1940 as a way of systematically creating technical predictions. 

The method involves a group of experts who make predictions about the future and then reach a consensus about those predictions. Today, the Delphi method is most commonly used in medicine.

In a content validity study using the Delphi method, a panel of experts is asked to rate the items on an assessment instrument on a scale. The expert panel also has the opportunity to add comments about the items.

After all ratings have been collected, the average item rating is calculated. In the second round, the experts receive summarized results of the first round and are able to make further comments and revise their first-round answers.

This back-and-forth continues until some homogeneity criterion — similarity between the results of researchers — is achieved (Koller et al., 2017).

Lawshie (1975) and Lynn (1986) created numerical methods to assess content validity. Both of these methods require the development of a content validity index (CVI). A content validity index is a statistical measure of the degree to which an assessment instrument covers the content domain of interest.

There are two steps in calculating a content validity index:

• Determining the number of items that should be included in the assessment instrument;
• Determining the percentage of items that actually are included in the assessment instrument.

The first step, determining the number of items that should be included in an assessment instrument, can be done using one of two approaches: item sampling or expert consensus.

Item sampling involves selecting a sample of items from a larger set of items that cover the content domain. The number of items in the sample is then used to estimate the total number of items needed to cover the content domain.

This approach has the advantage of being quick and easy, but it can be biased if the sample of items is not representative of the larger set (Koller et al., 2017).

The second approach, expert consensus, involves asking a group of experts how many items should be included in an assessment instrument to adequately cover the content domain. This approach has the advantage of being more objective, but it can be time-consuming and expensive.

Experts are able to assign these items to dimensions of the construct that they intend to measure and assign relevance values to decide whether an item is a strong measure of the construct.

Although various attempts to numerize the process of measuring content validity exist, there is no systematic procedure that could be used as a general guideline for the evaluation of content validity (Newman et al., 2013).

When is content validity used?

Education assessment.

In the context of educational assessment, validity is the extent to which an assessment instrument accurately measures what it is intended to measure. Validity concerns anyone who is making inferences and decisions about a learner based on data.

This can have deep implications for students’ education and future. For instance, a test that poorly measures students’ abilities can lead to placement in a future course that is unsuitable for the student and, ultimately, to the student’s failure (Obilor, 2022).

There are a number of factors that specifically affect the validity of assessments given to students, such as (Obilor, 2018):

• Unclear Direction: If directions do not clearly indicate to the respondent how to respond to the tool’s items, the validity of the tool is reduced.
• Vocabulary: If the vocabulary of the respondent is poor, and he does not understand the items, the validity of the instrument is affected.
• Poorly Constructed Test Items: If items are constructed in such a way that they have different meanings for different respondents, validity is affected.
• Difficulty Level of Items: In an achievement test, too easy or too difficult test items would not discriminate among students, thereby lowering the validity of the test.
• Influence of Extraneous Factors: Extraneous factors like the style of expression, legibility, mechanics of grammar (spelling, punctuation), handwriting, and length of the tool, amongst others, influence the validity of a tool.
• Inappropriate Time Limit: In a speed test, if enough time limit is given, the result will be invalidated as a measure of speed. In a power test, an inappropriate time limit will lower the validity of the test.

There are a few reasons why interviews may lack content validity . First, interviewers may ask different questions or place different emphases on certain topics across different candidates. This can make it difficult to compare candidates on a level playing field.

Second, interviewers may have their own personal biases that come into play when making judgments about candidates.

Finally, the interview format itself may be flawed. For example, many companies ask potential programmers to complete brain teasers — such as calculating the number of plumbers in Chicago or coding tasks that rely heavily on theoretical knowledge of data structures — even if this knowledge would be used rarely or never on the job.

Questionnaires

Questionnaires rely on the respondents’ ability to accurately recall information and report it honestly. Additionally, the way in which questions are worded can influence responses.

To increase content validity when designing a questionnaire, careful consideration must be given to the types of questions that will be asked.

Open-ended questions are typically less biased than closed-ended questions, but they can be more difficult to analyze.

It is also important to avoid leading or loaded questions that might influence respondents’ answers in a particular direction. The wording of questions should be clear and concise to avoid confusion (Koller et al., 2017).

Is content validity internal or external?

Most experts agree that content validity is primarily an internal issue. This means that the concepts and items included in a test should be based on a thorough analysis of the specific content area being measured.

The items should also be representative of the range of difficulty levels within that content area. External factors, such as the opinions of experts or the general public, can influence content validity, but they are not necessarily the primary determinant.

In some cases, such as when developing a test for licensure or certification, external stakeholders may have a strong say in what is included in the test (Koller et al., 2017).

How can content validity be improved?

There are a few ways to increase content validity. One is to create items that are more representative of the targeted construct. Another is to increase the number of items on the assessment so that it covers a greater range of content.

Finally, experts can review the items on the assessment to ensure that they are fair and representative of the skills being tested (Koller et al., 2017).

How do you test the content validity of a questionnaire?

There are a few ways to test the content validity of a questionnaire. One way is to ask experts in the field to review the questions and provide feedback on whether or not they believe the questions are relevant and cover all important topics.

Another way is to administer the questionnaire to a small group of people and then analyze the results to see if there are any patterns or themes emerging from the responses.

Finally, it is also possible to use statistical methods to test for content validity, although this approach is more complex and usually requires access to specialized software (Koller et al., 2017).

How can you tell if an instrument is content-valid?

There are a few ways to tell if an instrument is content-valid. The first of these involves looking at two subsets of content validity: face and construct validity.

Face validity is a measure of whether or not the items on the test appear to measure what they claim to measure. This is highly subjective but convenient to assess.

Another way is to look at the construct validity, which is whether or not the items on the test measure what they are supposed to measure. Finally, you can also look at the criterion-related validity, which is whether or not the items on the test predict future performance.

What is the difference between content and criterion validity?

Content validity is a measure of how well a test covers the content it is supposed to cover.

Criterion validity, meanwhile, is an index of how well a test correlates with an established standard of comparison or a criterion.

For example, if a measure of criminal behavior is criterion valid, then it should be possible to use it to predict whether an individual will be arrested in the future for a criminal violation, is currently breaking the law, and has a previous criminal record (American Psychological Association).

Are content validity and construct validity the same?

Content validity is not the same as construct validity.

Content validity is a method of assessing the degree to which a measure covers the range of content that it purports to measure.

In contrast, construct validity is a method of assessing the degree to which a measure reflects the underlying construct that it purports to measure.

It is important to note that content validity and construct validity are not mutually exclusive; a measure can be both valid and invalid with respect to content and construct.

However, content validity is a necessary but not sufficient condition for construct validity. That is, a measure cannot be construct valid if it does not first have content validity (Koller et al., 2017).

For example, an academic achievement test in math may have content validity if it contains questions from all areas of math a student is expected to have learned before the test, but it may not have construct validity if it does not somehow relate to tests of similar and different constructs.

How many experts are needed for content validity?

There is no definitive answer to this question as it depends on a number of factors, including the nature of the instrument being validated and the purpose of the validation exercise.

However, in general, a minimum of three experts should be used in order to ensure that the content validity of an instrument is adequately established (Koller et al., 2017).

American Psychological Association. (n.D.). Content Validity. American Psychological Association Dictionary.

Haynes, S. N., Richard, D., & Kubany, E. S. (1995). Content validity in psychological assessment: A functional approach to concepts and methods. Psychological assessment , 7 (3), 238.

Koller, I., Levenson, M. R., & Glück, J. (2017). What do you think you are measuring? A mixed-methods procedure for assessing the content validity of test items and theory-based scaling. Frontiers in psychology , 8 , 126.

Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel psychology , 28 (4), 563-575.

Lynn, M. R. (1986). Determination and quantification of content validity. Nursing research .

Obilor, E. I. (2018). Fundamentals of research methods and Statistics in Education and Social Sciences. Port Harcourt: SABCOS Printers & Publishers.

OBILOR, E. I. P., & MIWARI, G. U. P. (2022). Content Validity in Educational Assessment.

Newman, Isadore, Janine Lim, and Fernanda Pineda. “Content validity using a mixed methods approach: Its application and development through the use of a table of specifications methodology.” Journal of Mixed Methods Research 7.3 (2013): 243-260.

Rossiter, J. R. (2008). Content validity of measures of abstract constructs in management and organizational research. British Journal of Management , 19 (4), 380-388.

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Chapter 5: Psychological Measurement

Reliability and Validity of Measurement

Learning Objectives

• Define reliability, including the different types and how they are assessed.
• Define validity, including the different types and how they are assessed.
• Describe the kinds of evidence that would be relevant to assessing the reliability and validity of a particular measure.

Again, measurement involves assigning scores to individuals so that they represent some characteristic of the individuals. But how do researchers know that the scores actually represent the characteristic, especially when it is a construct like intelligence, self-esteem, depression, or working memory capacity? The answer is that they conduct research using the measure to confirm that the scores make sense based on their understanding of the construct being measured. This is an extremely important point. Psychologists do not simply  assume  that their measures work. Instead, they collect data to demonstrate  that they work. If their research does not demonstrate that a measure works, they stop using it.

As an informal example, imagine that you have been dieting for a month. Your clothes seem to be fitting more loosely, and several friends have asked if you have lost weight. If at this point your bathroom scale indicated that you had lost 10 pounds, this would make sense and you would continue to use the scale. But if it indicated that you had gained 10 pounds, you would rightly conclude that it was broken and either fix it or get rid of it. In evaluating a measurement method, psychologists consider two general dimensions: reliability and validity.

Reliability

Reliability  refers to the consistency of a measure. Psychologists consider three types of consistency: over time (test-retest reliability), across items (internal consistency), and across different researchers (inter-rater reliability).

Test-Retest Reliability

When researchers measure a construct that they assume to be consistent across time, then the scores they obtain should also be consistent across time.  Test-retest reliability  is the extent to which this is actually the case. For example, intelligence is generally thought to be consistent across time. A person who is highly intelligent today will be highly intelligent next week. This means that any good measure of intelligence should produce roughly the same scores for this individual next week as it does today. Clearly, a measure that produces highly inconsistent scores over time cannot be a very good measure of a construct that is supposed to be consistent.

Assessing test-retest reliability requires using the measure on a group of people at one time, using it again on the  same  group of people at a later time, and then looking at  test-retest correlation  between the two sets of scores. This is typically done by graphing the data in a scatterplot and computing Pearson’s  r . Figure 5.2 shows the correlation between two sets of scores of several university students on the Rosenberg Self-Esteem Scale, administered two times, a week apart. Pearson’s r for these data is +.95. In general, a test-retest correlation of +.80 or greater is considered to indicate good reliability.

Again, high test-retest correlations make sense when the construct being measured is assumed to be consistent over time, which is the case for intelligence, self-esteem, and the Big Five personality dimensions. But other constructs are not assumed to be stable over time. The very nature of mood, for example, is that it changes. So a measure of mood that produced a low test-retest correlation over a period of a month would not be a cause for concern.

Internal Consistency

A second kind of reliability is  internal consistency , which is the consistency of people’s responses across the items on a multiple-item measure. In general, all the items on such measures are supposed to reflect the same underlying construct, so people’s scores on those items should be correlated with each other. On the Rosenberg Self-Esteem Scale, people who agree that they are a person of worth should tend to agree that that they have a number of good qualities. If people’s responses to the different items are not correlated with each other, then it would no longer make sense to claim that they are all measuring the same underlying construct. This is as true for behavioural and physiological measures as for self-report measures. For example, people might make a series of bets in a simulated game of roulette as a measure of their level of risk seeking. This measure would be internally consistent to the extent that individual participants’ bets were consistently high or low across trials.

Like test-retest reliability, internal consistency can only be assessed by collecting and analyzing data. One approach is to look at a  split-half correlation . This involves splitting the items into two sets, such as the first and second halves of the items or the even- and odd-numbered items. Then a score is computed for each set of items, and the relationship between the two sets of scores is examined. For example, Figure 5.3 shows the split-half correlation between several university students’ scores on the even-numbered items and their scores on the odd-numbered items of the Rosenberg Self-Esteem Scale. Pearson’s  r  for these data is +.88. A split-half correlation of +.80 or greater is generally considered good internal consistency.

Perhaps the most common measure of internal consistency used by researchers in psychology is a statistic called  Cronbach’s α  (the Greek letter alpha). Conceptually, α is the mean of all possible split-half correlations for a set of items. For example, there are 252 ways to split a set of 10 items into two sets of five. Cronbach’s α would be the mean of the 252 split-half correlations. Note that this is not how α is actually computed, but it is a correct way of interpreting the meaning of this statistic. Again, a value of +.80 or greater is generally taken to indicate good internal consistency.

Interrater Reliability

Many behavioural measures involve significant judgment on the part of an observer or a rater.  Inter-rater reliability  is the extent to which different observers are consistent in their judgments. For example, if you were interested in measuring university students’ social skills, you could make video recordings of them as they interacted with another student whom they are meeting for the first time. Then you could have two or more observers watch the videos and rate each student’s level of social skills. To the extent that each participant does in fact have some level of social skills that can be detected by an attentive observer, different observers’ ratings should be highly correlated with each other. Inter-rater reliability would also have been measured in Bandura’s Bobo doll study. In this case, the observers’ ratings of how many acts of aggression a particular child committed while playing with the Bobo doll should have been highly positively correlated. Interrater reliability is often assessed using Cronbach’s α when the judgments are quantitative or an analogous statistic called Cohen’s κ (the Greek letter kappa) when they are categorical.

Validity  is the extent to which the scores from a measure represent the variable they are intended to. But how do researchers make this judgment? We have already considered one factor that they take into account—reliability. When a measure has good test-retest reliability and internal consistency, researchers should be more confident that the scores represent what they are supposed to. There has to be more to it, however, because a measure can be extremely reliable but have no validity whatsoever. As an absurd example, imagine someone who believes that people’s index finger length reflects their self-esteem and therefore tries to measure self-esteem by holding a ruler up to people’s index fingers. Although this measure would have extremely good test-retest reliability, it would have absolutely no validity. The fact that one person’s index finger is a centimetre longer than another’s would indicate nothing about which one had higher self-esteem.

Discussions of validity usually divide it into several distinct “types.” But a good way to interpret these types is that they are other kinds of evidence—in addition to reliability—that should be taken into account when judging the validity of a measure. Here we consider three basic kinds: face validity, content validity, and criterion validity.

Face Validity

Face validity  is the extent to which a measurement method appears “on its face” to measure the construct of interest. Most people would expect a self-esteem questionnaire to include items about whether they see themselves as a person of worth and whether they think they have good qualities. So a questionnaire that included these kinds of items would have good face validity. The finger-length method of measuring self-esteem, on the other hand, seems to have nothing to do with self-esteem and therefore has poor face validity. Although face validity can be assessed quantitatively—for example, by having a large sample of people rate a measure in terms of whether it appears to measure what it is intended to—it is usually assessed informally.

Face validity is at best a very weak kind of evidence that a measurement method is measuring what it is supposed to. One reason is that it is based on people’s intuitions about human behaviour, which are frequently wrong. It is also the case that many established measures in psychology work quite well despite lacking face validity. The Minnesota Multiphasic Personality Inventory-2 (MMPI-2) measures many personality characteristics and disorders by having people decide whether each of over 567 different statements applies to them—where many of the statements do not have any obvious relationship to the construct that they measure. For example, the items “I enjoy detective or mystery stories” and “The sight of blood doesn’t frighten me or make me sick” both measure the suppression of aggression. In this case, it is not the participants’ literal answers to these questions that are of interest, but rather whether the pattern of the participants’ responses to a series of questions matches those of individuals who tend to suppress their aggression.

Content Validity

Content validity  is the extent to which a measure “covers” the construct of interest. For example, if a researcher conceptually defines test anxiety as involving both sympathetic nervous system activation (leading to nervous feelings) and negative thoughts, then his measure of test anxiety should include items about both nervous feelings and negative thoughts. Or consider that attitudes are usually defined as involving thoughts, feelings, and actions toward something. By this conceptual definition, a person has a positive attitude toward exercise to the extent that he or she thinks positive thoughts about exercising, feels good about exercising, and actually exercises. So to have good content validity, a measure of people’s attitudes toward exercise would have to reflect all three of these aspects. Like face validity, content validity is not usually assessed quantitatively. Instead, it is assessed by carefully checking the measurement method against the conceptual definition of the construct.

Criterion Validity

Criterion validity  is the extent to which people’s scores on a measure are correlated with other variables (known as  criteria ) that one would expect them to be correlated with. For example, people’s scores on a new measure of test anxiety should be negatively correlated with their performance on an important school exam. If it were found that people’s scores were in fact negatively correlated with their exam performance, then this would be a piece of evidence that these scores really represent people’s test anxiety. But if it were found that people scored equally well on the exam regardless of their test anxiety scores, then this would cast doubt on the validity of the measure.

A criterion can be any variable that one has reason to think should be correlated with the construct being measured, and there will usually be many of them. For example, one would expect test anxiety scores to be negatively correlated with exam performance and course grades and positively correlated with general anxiety and with blood pressure during an exam. Or imagine that a researcher develops a new measure of physical risk taking. People’s scores on this measure should be correlated with their participation in “extreme” activities such as snowboarding and rock climbing, the number of speeding tickets they have received, and even the number of broken bones they have had over the years. When the criterion is measured at the same time as the construct, criterion validity is referred to as concurrent validity ; however, when the criterion is measured at some point in the future (after the construct has been measured), it is referred to as predictive validity (because scores on the measure have “predicted” a future outcome).

Criteria can also include other measures of the same construct. For example, one would expect new measures of test anxiety or physical risk taking to be positively correlated with existing measures of the same constructs. This is known as convergent validity .

Assessing convergent validity requires collecting data using the measure. Researchers John Cacioppo and Richard Petty did this when they created their self-report Need for Cognition Scale to measure how much people value and engage in thinking (Cacioppo & Petty, 1982) [1] . In a series of studies, they showed that people’s scores were positively correlated with their scores on a standardized academic achievement test, and that their scores were negatively correlated with their scores on a measure of dogmatism (which represents a tendency toward obedience). In the years since it was created, the Need for Cognition Scale has been used in literally hundreds of studies and has been shown to be correlated with a wide variety of other variables, including the effectiveness of an advertisement, interest in politics, and juror decisions (Petty, Briñol, Loersch, & McCaslin, 2009) [2] .

Discriminant Validity

Discriminant validity , on the other hand, is the extent to which scores on a measure are not correlated with measures of variables that are conceptually distinct. For example, self-esteem is a general attitude toward the self that is fairly stable over time. It is not the same as mood, which is how good or bad one happens to be feeling right now. So people’s scores on a new measure of self-esteem should not be very highly correlated with their moods. If the new measure of self-esteem were highly correlated with a measure of mood, it could be argued that the new measure is not really measuring self-esteem; it is measuring mood instead.

When they created the Need for Cognition Scale, Cacioppo and Petty also provided evidence of discriminant validity by showing that people’s scores were not correlated with certain other variables. For example, they found only a weak correlation between people’s need for cognition and a measure of their cognitive style—the extent to which they tend to think analytically by breaking ideas into smaller parts or holistically in terms of “the big picture.” They also found no correlation between people’s need for cognition and measures of their test anxiety and their tendency to respond in socially desirable ways. All these low correlations provide evidence that the measure is reflecting a conceptually distinct construct.

Key Takeaways

• Psychological researchers do not simply assume that their measures work. Instead, they conduct research to show that they work. If they cannot show that they work, they stop using them.
• There are two distinct criteria by which researchers evaluate their measures: reliability and validity. Reliability is consistency across time (test-retest reliability), across items (internal consistency), and across researchers (interrater reliability). Validity is the extent to which the scores actually represent the variable they are intended to.
• Validity is a judgment based on various types of evidence. The relevant evidence includes the measure’s reliability, whether it covers the construct of interest, and whether the scores it produces are correlated with other variables they are expected to be correlated with and not correlated with variables that are conceptually distinct.
• The reliability and validity of a measure is not established by any single study but by the pattern of results across multiple studies. The assessment of reliability and validity is an ongoing process.
• Practice: Ask several friends to complete the Rosenberg Self-Esteem Scale. Then assess its internal consistency by making a scatterplot to show the split-half correlation (even- vs. odd-numbered items). Compute Pearson’s  r too if you know how.
• Discussion: Think back to the last college exam you took and think of the exam as a psychological measure. What construct do you think it was intended to measure? Comment on its face and content validity. What data could you collect to assess its reliability and criterion validity?
• Cacioppo, J. T., & Petty, R. E. (1982). The need for cognition. Journal of Personality and Social Psychology, 42 , 116–131. ↵
• Petty, R. E, Briñol, P., Loersch, C., & McCaslin, M. J. (2009). The need for cognition. In M. R. Leary & R. H. Hoyle (Eds.), Handbook of individual differences in social behaviour (pp. 318–329). New York, NY: Guilford Press. ↵

The consistency of a measure.

The consistency of a measure over time.

The consistency of a measure on the same group of people at different times.

Consistency of people’s responses across the items on a multiple-item measure.

Method of assessing internal consistency through splitting the items into two sets and examining the relationship between them.

A statistic in which α is the mean of all possible split-half correlations for a set of items.

The extent to which different observers are consistent in their judgments.

The extent to which the scores from a measure represent the variable they are intended to.

The extent to which a measurement method appears to measure the construct of interest.

The extent to which a measure “covers” the construct of interest.

The extent to which people’s scores on a measure are correlated with other variables that one would expect them to be correlated with.

In reference to criterion validity, variables that one would expect to be correlated with the measure.

When the criterion is measured at the same time as the construct.

when the criterion is measured at some point in the future (after the construct has been measured).

When new measures positively correlate with existing measures of the same constructs.

The extent to which scores on a measure are not correlated with measures of variables that are conceptually distinct.

Research Methods in Psychology - 2nd Canadian Edition Copyright © 2015 by Paul C. Price, Rajiv Jhangiani, & I-Chant A. Chiang is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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