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Reliability vs. Validity in Research | Difference, Types and Examples
Published on July 3, 2019 by Fiona Middleton . Revised on June 22, 2023.
Reliability and validity are concepts used to evaluate the quality of research. They indicate how well a method , technique. or test measures something. Reliability is about the consistency of a measure, and validity is about the accuracy of a measure.opt
It’s important to consider reliability and validity when you are creating your research design , planning your methods, and writing up your results, especially in quantitative research . Failing to do so can lead to several types of research bias and seriously affect your work.
Table of contents
Understanding reliability vs validity, how are reliability and validity assessed, how to ensure validity and reliability in your research, where to write about reliability and validity in a thesis, other interesting articles.
Reliability and validity are closely related, but they mean different things. A measurement can be reliable without being valid. However, if a measurement is valid, it is usually also reliable.
What is reliability?
Reliability refers to how consistently a method measures something. If the same result can be consistently achieved by using the same methods under the same circumstances, the measurement is considered reliable.
What is validity?
Validity refers to how accurately a method measures what it is intended to measure. If research has high validity, that means it produces results that correspond to real properties, characteristics, and variations in the physical or social world.
High reliability is one indicator that a measurement is valid. If a method is not reliable, it probably isn’t valid.
If the thermometer shows different temperatures each time, even though you have carefully controlled conditions to ensure the sample’s temperature stays the same, the thermometer is probably malfunctioning, and therefore its measurements are not valid.
However, reliability on its own is not enough to ensure validity. Even if a test is reliable, it may not accurately reflect the real situation.
Validity is harder to assess than reliability, but it is even more important. To obtain useful results, the methods you use to collect data must be valid: the research must be measuring what it claims to measure. This ensures that your discussion of the data and the conclusions you draw are also valid.
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Reliability can be estimated by comparing different versions of the same measurement. Validity is harder to assess, but it can be estimated by comparing the results to other relevant data or theory. Methods of estimating reliability and validity are usually split up into different types.
Types of reliability
Different types of reliability can be estimated through various statistical methods.
Types of validity
The validity of a measurement can be estimated based on three main types of evidence. Each type can be evaluated through expert judgement or statistical methods.
To assess the validity of a cause-and-effect relationship, you also need to consider internal validity (the design of the experiment ) and external validity (the generalizability of the results).
The reliability and validity of your results depends on creating a strong research design , choosing appropriate methods and samples, and conducting the research carefully and consistently.
If you use scores or ratings to measure variations in something (such as psychological traits, levels of ability or physical properties), it’s important that your results reflect the real variations as accurately as possible. Validity should be considered in the very earliest stages of your research, when you decide how you will collect your data.
- Choose appropriate methods of measurement
Ensure that your method and measurement technique are high quality and targeted to measure exactly what you want to know. They should be thoroughly researched and based on existing knowledge.
For example, to collect data on a personality trait, you could use a standardized questionnaire that is considered reliable and valid. If you develop your own questionnaire, it should be based on established theory or findings of previous studies, and the questions should be carefully and precisely worded.
- Use appropriate sampling methods to select your subjects
To produce valid and generalizable results, clearly define the population you are researching (e.g., people from a specific age range, geographical location, or profession). Ensure that you have enough participants and that they are representative of the population. Failing to do so can lead to sampling bias and selection bias .
Reliability should be considered throughout the data collection process. When you use a tool or technique to collect data, it’s important that the results are precise, stable, and reproducible .
- Apply your methods consistently
Plan your method carefully to make sure you carry out the same steps in the same way for each measurement. This is especially important if multiple researchers are involved.
For example, if you are conducting interviews or observations , clearly define how specific behaviors or responses will be counted, and make sure questions are phrased the same way each time. Failing to do so can lead to errors such as omitted variable bias or information bias .
- Standardize the conditions of your research
When you collect your data, keep the circumstances as consistent as possible to reduce the influence of external factors that might create variation in the results.
For example, in an experimental setup, make sure all participants are given the same information and tested under the same conditions, preferably in a properly randomized setting. Failing to do so can lead to a placebo effect , Hawthorne effect , or other demand characteristics . If participants can guess the aims or objectives of a study, they may attempt to act in more socially desirable ways.
It’s appropriate to discuss reliability and validity in various sections of your thesis or dissertation or research paper . Showing that you have taken them into account in planning your research and interpreting the results makes your work more credible and trustworthy.
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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.
- Normal distribution
- Degrees of freedom
- Null hypothesis
- Discourse analysis
- Control groups
- Mixed methods research
- Non-probability sampling
- Quantitative research
- Ecological validity
- Rosenthal effect
- Implicit bias
- Cognitive bias
- Selection bias
- Negativity bias
- Status quo bias
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Home » Validity – Types, Examples and Guide
Validity – Types, Examples and Guide
Table of Contents
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 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.
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 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 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 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 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 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 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.
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.
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.
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.
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
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Validity In Psychology Research: Types & Examples
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Editor-in-Chief for Simply Psychology
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Saul Mcleod, Ph.D., is a qualified psychology teacher with over 18 years experience of working in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.
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In psychology research, validity refers to the extent to which a test or measurement tool accurately measures what it’s intended to measure. It ensures that the research findings are genuine and not due to extraneous factors.
Validity can be categorized into different types based on internal and external validity .
The concept of validity was formulated by Kelly (1927, p. 14), who stated that a test is valid if it measures what it claims to measure. For example, a test of intelligence should measure intelligence and not something else (such as memory).
Internal and External Validity In Research
Internal validity refers to whether the effects observed in a study are due to the manipulation of the independent variable and not some other confounding factor.
In other words, there is a causal relationship between the independent and dependent variables .
Internal validity can be improved by controlling extraneous variables, using standardized instructions, counterbalancing, and eliminating demand characteristics and investigator effects.
External validity refers to the extent to which the results of a study can be generalized to other settings (ecological validity), other people (population validity), and over time (historical validity).
External validity can be improved by setting experiments more naturally and using random sampling to select participants.
Types of Validity In Psychology
Two main categories of validity are used to assess the validity of the test (i.e., questionnaire, interview, IQ test, etc.): Content and criterion.
- Content validity refers to the extent to which a test or measurement represents all aspects of the intended content domain. It assesses whether the test items adequately cover the topic or concept.
- Criterion validity assesses the performance of a test based on its correlation with a known external criterion or outcome. It can be further divided into concurrent (measured at the same time) and predictive (measuring future performance) validity.
Face validity is simply whether the test appears (at face value) to measure what it claims to. This is the least sophisticated measure of content-related validity, and is a superficial and subjective assessment based on appearance.
Tests wherein the purpose is clear, even to naïve respondents, are said to have high face validity. Accordingly, tests wherein the purpose is unclear have low face validity (Nevo, 1985).
A direct measurement of face validity is obtained by asking people to rate the validity of a test as it appears to them. This rater could use a Likert scale to assess face validity.
- The test is extremely suitable for a given purpose
- The test is very suitable for that purpose;
- The test is adequate
- The test is inadequate
- The test is irrelevant and, therefore, unsuitable
It is important to select suitable people to rate a test (e.g., questionnaire, interview, IQ test, etc.). For example, individuals who actually take the test would be well placed to judge its face validity.
Also, people who work with the test could offer their opinion (e.g., employers, university administrators, employers). Finally, the researcher could use members of the general public with an interest in the test (e.g., parents of testees, politicians, teachers, etc.).
The face validity of a test can be considered a robust construct only if a reasonable level of agreement exists among raters.
It should be noted that the term face validity should be avoided when the rating is done by an “expert,” as content validity is more appropriate.
Having face validity does not mean that a test really measures what the researcher intends to measure, but only in the judgment of raters that it appears to do so. Consequently, it is a crude and basic measure of validity.
A test item such as “ I have recently thought of killing myself ” has obvious face validity as an item measuring suicidal cognitions and may be useful when measuring symptoms of depression.
However, the implication of items on tests with clear face validity is that they are more vulnerable to social desirability bias. Individuals may manipulate their responses to deny or hide problems or exaggerate behaviors to present a positive image of themselves.
It is possible for a test item to lack face validity but still have general validity and measure what it claims to measure. This is good because it reduces demand characteristics and makes it harder for respondents to manipulate their answers.
For example, the test item “ I believe in the second coming of Christ ” would lack face validity as a measure of depression (as the purpose of the item is unclear).
This item appeared on the first version of The Minnesota Multiphasic Personality Inventory (MMPI) and loaded on the depression scale.
Because most of the original normative sample of the MMPI were good Christians, only a depressed Christian would think Christ is not coming back. Thus, for this particular religious sample, the item does have general validity but not face validity.
Construct validity assesses how well a test or measure represents and captures an abstract theoretical concept, known as a construct. It indicates the degree to which the test accurately reflects the construct it intends to measure, often evaluated through relationships with other variables and measures theoretically connected to the construct.
Construct validity was invented by Cronbach and Meehl (1955). This type of content-related validity refers to the extent to which a test captures a specific theoretical construct or trait, and it overlaps with some of the other aspects of validity
Construct validity does not concern the simple, factual question of whether a test measures an attribute.
Instead, it is about the complex question of whether test score interpretations are consistent with a nomological network involving theoretical and observational terms (Cronbach & Meehl, 1955).
To test for construct validity, it must be demonstrated that the phenomenon being measured actually exists. So, the construct validity of a test for intelligence, for example, depends on a model or theory of intelligence .
Construct validity entails demonstrating the power of such a construct to explain a network of research findings and to predict further relationships.
The more evidence a researcher can demonstrate for a test’s construct validity, the better. However, there is no single method of determining the construct validity of a test.
Instead, different methods and approaches are combined to present the overall construct validity of a test. For example, factor analysis and correlational methods can be used.
Convergent validity is a subtype of construct validity. It assesses the degree to which two measures that theoretically should be related are related.
It demonstrates that measures of similar constructs are highly correlated. It helps confirm that a test accurately measures the intended construct by showing its alignment with other tests designed to measure the same or similar constructs.
For example, suppose there are two different scales used to measure self-esteem:
Scale A and Scale B. If both scales effectively measure self-esteem, then individuals who score high on Scale A should also score high on Scale B, and those who score low on Scale A should score similarly low on Scale B.
If the scores from these two scales show a strong positive correlation, then this provides evidence for convergent validity because it indicates that both scales seem to measure the same underlying construct of self-esteem.
Concurrent Validity (i.e., occurring at the same time)
Concurrent validity evaluates how well a test’s results correlate with the results of a previously established and accepted measure, when both are administered at the same time.
It helps in determining whether a new measure is a good reflection of an established one without waiting to observe outcomes in the future.
If the new test is validated by comparison with a currently existing criterion, we have concurrent validity.
Very often, a new IQ or personality test might be compared with an older but similar test known to have good validity already.
Predictive validity assesses how well a test predicts a criterion that will occur in the future. It measures the test’s ability to foresee the performance of an individual on a related criterion measured at a later point in time. It gauges the test’s effectiveness in predicting subsequent real-world outcomes or results.
For example, a prediction may be made on the basis of a new intelligence test that high scorers at age 12 will be more likely to obtain university degrees several years later. If the prediction is born out, then the test has predictive validity.
Cronbach, L. J., and Meehl, P. E. (1955) Construct validity in psychological tests. Psychological Bulletin , 52, 281-302.
Hathaway, S. R., & McKinley, J. C. (1943). Manual for the Minnesota Multiphasic Personality Inventory . New York: Psychological Corporation.
Kelley, T. L. (1927). Interpretation of educational measurements. New York : Macmillan.
Nevo, B. (1985). Face validity revisited . Journal of Educational Measurement , 22(4), 287-293.
The Validity and Reliability of Qualitative Research - PowerPoint PPT Presentation
The Validity and Reliability of Qualitative Research
An important design element, for increasing interpretive validity, therefore, is ... codebooks (specifies definitions and relationships of concepts and terms) ... – powerpoint ppt presentation.
- Section 1 Philosophical Orientation Getting in the Right Mindset
- Section 2 Enhancing the Validity of Qualitative Research
- Section 3 Enhancing the Reliability of Qualitative Research
- Section 4 Applications of Qualitative Research Design
- Section 5 Group Work to Apply Learning
- Philosophical Orientation
- The philosophical underpinnings of qualitative research (as contrasted against quantitative research)
- Implications for sampling
- Implications for addressing issues of validity and reliability
- Quantitative Research is
- Fundamentally an inferential enterprise that seeks to uncover universal principles
- Philosophically and methodologically built or designed around the ability to infer from a sample to a larger population
- Qualitative Research is
- Fundamentally an interpretive enterprise that is context-dependent
- Philosophically and methodologically built or designed around the ability to interpret (comprehend/understand) a phenomenon from an emic (insider), as well as an etic (outsider) perspective
- This inter-subjective (i.e., shared) understanding serves as a proxy for objectivity
- A client wants to know about how well a program is working for youth
- Quantitative Research Questions (descriptive, explanatory -gtinferential)
- Qualitative Research Questions (descriptive, explanatory -gtinterpretive)
- Sampling Strategies used in Quantitative Research
- Obtaining a random or representative sample (based on probabilities)
- Permits the researcher to infer from a segment of the population (from which it is more feasible to collect data) to a larger population
- Sampling Strategies used in Qualitative Research
- Purposive sampling (to ensure that the researcher has adequately understood the variation in the phenomena of interest)
- Theoretical sampling (to test developing ideas about a setting by selecting phenomena that are crucial to the validity of those ideas)
- Example Case study selection in ACY (see, also, handout on sampling techniques)
- In quantitative research, threats to validity are addressed by prior design features (such as randomization and controls)
- In qualitative research, such prior elimination of threats to validity is less possible because
- qualitative research is more inductive, and
- it focuses primarily on understanding particulars rather than generalizing to universals.
- Qualitative researchers view threats as an opportunity for learning
- - e.g. researcher effects and bias are part of the story that is told they are not controlled for
- Enhancing the Validity
- of Qualitative Research
- Defining validity within the qualitative paradigm
- Major types of validity within the qualitative paradigm
- Design considerations
- Validity is not a commodity that can be purchased with techniques Rather, validity is, like integrity, character and quality, to be assessed relative to purposes and circumstances.
- In general, validity concerns the degree to which an account is accurate or truthful
- In qualitative research, validity concerns the degree to which a finding is judged to have been interpreted in a correct way
- Can another research read your field (and other types of) notes (i.e., the explication of your logic) and come to the same understandings of a given phenomenon?
- Concern about validity (as well as reliability) is the primary reason thick description is an essential component of the qualitative research enterprise
- Handout Different Types of Notes
- Example ACY Site Visit Toolkit
- Descriptive Validity
- Interpretive Validity
- Theoretical Validity
- External Validity (i.e., generalizability)
- Concerned with the factual accuracy of an account (that is, making sure one is not making up or distorting the things one hears and sees)
- All subsequent types of validity are dependent on the existence of this fundamental aspect of validity
- Behavior must be attended to, and with some exactness, because it is through the flow of behavior or, more precisely, social action that cultural forms find articulation.
- Interpretive accounts are grounded in the language of the people studied and rely, as much as possible, on their own words and concepts
- At issue, then, is the accuracy of the concepts as applied to the perspective of the individuals included in the account
- While the relevant consensus about the terms used in description rests in the research community, the relevant consensus for the terms used in interpretation rests, to a substantial extent, in the community studied
- An important design element, for increasing interpretive validity, therefore, is to employee, at some level/to some degree, a participatory research approach (e.g., through member checks, peer to peer research model, etc.)
- Theoretical understanding goes beyond concrete description and interpretation its value is derived based on its ability to explain succinctly the most amount of data
- A theory articulates/formulates a model of relationships as they are postulated to exist between salient variables or concepts
- Theoretical validity is thus concerned, not only with the validity of the concepts, but also their postulated relationships to one another, and thus its goodness of fit as an explanation
- Type I error believing a principle to be true when it is not (i.e., mistakenly rejecting the null hypothesis)
- Type II error rejecting a principle when, in fact, it is true
- Type III error asking the wrong question
- Case example Parable of the blind men and the elephant
- The most fertile search for validity comes from a combined series of different measures, each with its own idiosyncratic weaknesses, each pointed to a single hypothesis. When a hypothesis can survive the confrontation of a series of complementary methods of testing, it contains a degree of validity unattainable by one tested within the more constricted framework of a single method.
- There is broad agreement that generalizability (in the sense of producing laws that apply universally) is not a useful standard or goal for qualitative research
- This is not to say, however, that studies conducted to examine a particular phenomenon in a unique setting cannot contribute to the development of a body of knowledge accumulating about that particular phenomenon of interest
- Consensus appears to be emerging that for qualitative researchers generalizability is best thought of as a matter of the fit between the situation studied and others to which one might be interested in applying the concepts and conclusions of that study.
- Thick descriptions are crucial.
- Such descriptions of both the site in which the studies are conducted and of the site to which one wishes to generalize (or apply ones findings) are critical in allowing one to search for the similarities and differences between the situations.
- Analysis of these similarities and differences makes it possible to make a reasoned judgment about the extent to which we can use the findings from one study as a working hypothesis about what might occur in another situation.
- A finding emerging repeatedly in the study of numerous sites would appear to be more likely to be a good working hypothesis about some as yet unstudied site than a finding emerging from just one or two sites.
- A finding emerging from the study of several very heterogeneous sites would be more robust and, thus, more likely to be useful in understanding various other sites than one emerging from the study of several very similar sites.
- Heterogeneity may be obtained by creating a sampling frame that maximizes the variation inherent in the sample, specifically in terms of potentially theoretically important dimensions
- Enhancing the Reliability
- Defining reliability
- Key strategies for enhancing the reliability of qualitative research
- Reliability concerns the ability of different researchers to make the same observations of a given phenomenon if and when the observation is conducted using the same method(s) and procedure(s)
- Researchers can enhance the reliability of their qualitative research by
- Standardizing data collection techniques and protocols
- Again, documenting, documenting, documenting (e.g., time day and place observations made)
- Inter-rater reliability (a consideration during the analysis phase of the research process)
- Applications of
- Qualitative Research Design
- Core Qualitative Methods
- Guiding Principles
- Qualitative Research Techniques
- Semi- or Un-structured, Open-Ended
- In-depth Interviews (in the field, face-to-face)
- Participant Observation (field/site visits)
- Archival Research (document review and analysis)
- Qualitative research designs consider ways to foster
- Reflexivity (an ongoing process of reflecting on the researchers subjective experience, ways to broaden and enhance this source of knowing, examining how it informs research)
- Iteration (a spiraling process sequential and repetitive steps in examining preliminary findings for the purposes of guiding additional data collection and analysis)
- Intersubjectivity (a process of reaching a shared/ objective agreement about how to assign meaning to a social experience - with insiders and outsiders)
- Key Informants (question development and piloting of instrument)
- Unstructured to Semi-structured
- Data Processing and Analytic Tools
- Single v. Multiple Cases (not an individual)
- Expert and Key Informants (identification and recruitment of sample)
- Roles of the Researcher (identification and recruitment of sample)
- Data Collection
- Participants as Data Collectors
- Field Notes (personal reflections, observations, emerging concepts/theories)
- Debriefing (a participant, a participating researcher, a non-participating researcher)
- Key Informant Feedback
- Codebooks (specifies definitions and relationships of concepts and terms)
- Memos (emerging patterns, concepts documentation of analytic pathways)
- Case Analysis Meeting (a meeting of a research team for the purposes of reflecting on analytic process, tools, and findings)
- Matrices or Diagrams (to identify and examine time sequencing, the structure of relationships, conditions of cross case events)
- Group Work to Apply Learning
- What is a given program achieving with homeless and runaway youth?
- Key Methodological Issues (instrumentation, sampling, data collection, analysis)
- What More Do You Need to Know?
- Initial Methodological Approach and Justification
- Handouts, OMNI Reports and Proposals
- Qualitative Research Design, An Interactive Approach (Maxwell, 1996) Sage, Applied Social Research Methods Series
- Qualitative Data Analysis (Miles Huberman, 1994)
- The Quality of Qualitative Research (Seale, 1999)
- Focus Groups, Theory and Practice (Stewart Shamdasani, 1990) Sage, Applied Social Research Methods Series
- The Mismeasure of Man (Gould)
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9 Types of Validity in Research
Validity refers to whether or not a test or an experiment is actually doing what it is intended to do.
Validity sits upon a spectrum. For example:
- Low Validity: Most people now know that the standard IQ test does not actually measure intelligence or predict success in life.
- High Validity: By contrast, a standard pregnancy test is about 99% accurate , meaning it has very high validity and is therefore a very reliable test.
There are many ways to determine validity. Most of them are defined below.
Types of Validity
1. face validity.
Face validity refers to whether a scale “appears” to measure what it is supposed to measure. That is, do the questions seem to be logically related to the construct under study.
For example, a personality scale that measures emotional intelligence should have questions about self-awareness and empathy. It should not have questions about math or chemistry.
One common way to assess face validity is to ask a panel of experts to examine the scale and rate it’s appropriateness as a tool for measuring the construct. If the experts agree that the scale measures what it has been designed to measure, then the scale is said to have face validity.
If a scale, or a test, doesn’t have face validity, then people taking it won’t be serious.
Conbach explains it in the following way:
“When a patient loses faith in the medicine his doctor prescribes, it loses much of its power to improve his health. He may skip doses, and in the end may decide doctors cannot help him and let treatment lapse all together. For similar reasons, when selecting a test one must consider how worthwhile it will appear to the participant who takes it and other laymen who will see the results” (Cronbach, 1970, p. 182).
2. Content Validity
Content validity refers to whether a test or scale is measuring all of the components of a given construct. For example, if there are five dimensions of emotional intelligence (EQ), then a scale that measures EQ should contain questions regarding each dimension.
Similar to face validity, content validity can be assessed by asking subject matter experts (SMEs) to examine the test. If experts agree that the test includes items that assess every domain of the construct, then the test has content validity.
For example, the math portion of the SAT contains questions that require skills in many types of math: arithmetic, algebra, geometry, calculus, and many others. Since there are questions that assess each type of math, then the test has content validity.
The developer of the test could ask SMEs to rate the test’s construct validity. If the SMEs all give the test high ratings, then it has construct validity.
3. Construct Validity
Construct validity is the extent to which a measurement tool is truly assessing what it has been designed to assess.
There are two main methods of assessing construct validity: convergent and discriminant validity.
Convergent validity involves taking two tests that are supposed to measure the same construct and administering them to a sample of participants. The higher the correlation between the two tests, the stronger the construct validity.
With divergent validity, two tests that measure completely different constructs are administered to the same sample of participants. Since the tests are measuring different constructs, there should be a very low correlation between the two.
4. Internal Validity
Internal validity refers to whether or not the results of an experiment are due to the manipulation of the independent, or treatment, variables. For example, a researcher wants to examine how temperature affects willingness to help, so they have research participants wait in a room.
There are different rooms, one has the temperature set at normal, one at moderately warm, and the other at very warm.
During the next phase of the study, participants are asked to donate to a local charity before taking part in the rest of the study. The results showed that as the temperature of the room increased, donations decreased.
On the surface, it seems as though the study has internal validity: room temperature affected donations. However, even though the experiment involved three different rooms set at different temperatures, each room was a different size. The smallest room was the warmest and the normal temperature room was the largest.
Now, we don’t know if the donations were affected by room temperature or room size. So, the study has questionable internal validity.
Another way internal validity is assessed is through inter-rater reliability measures, which helps bolster both the validity and reliability of the study.
5. External Validity
External validity refers to whether the results of a study generalize to the real world or other situations. A lot of psychological studies take place in a university lab. Therefore, the setting is not very realistic.
This creates a big problem regarding external validity. Can we say that what happens in a lab would be the same thing that would happen in the real world?
For example, a study on mindfulness involves the researcher randomly assigning different research participants to use one of three mindfulness apps on their phones at home every night for 3 weeks. At the end of three weeks, their level of stress is measured with some high-tech EEG equipment.
This study has external validity because the participants used real apps and they were at home when using those apps. The apps and the home setting are realistic, so the study has external validity.
See More: Examples of External Validity
6. Concurrent Validity
Concurrent validity is a method of assessing validity that involves comparing a new test with an already existing test, or an already established criterion.
For example, a newly developed math test for the SAT will need to be validated before giving it to thousands of students. So, the new version of the test is administered to a sample of college math majors along with the old version of the test.
Scores on the two tests are compared by calculating a correlation between the two. The higher the correlation, the stronger the concurrent validity of the new test.
7. Predictive Validity
Predictive validity refers to whether scores on one test are associated with performance on a given criterion. That is, can a person’s score on the test predict their performance on the criterion?
For example, an IT company needs to hire dozens of programmers for an upcoming project. But conducting interviews with hundreds of applicants is time-consuming and not very accurate at identifying skilled coders.
So, the company develops a test that contains programming problems similar to the demands of the new project. The company assesses predictive validity of the test by having their current programmers take the test and then compare their scores with their yearly performance evaluations.
The results indicate that programmers with high marks in their evaluations also did very well on the test. Therefore, the test has predictive validity.
Now, when new applicants’ take the test, the company can predict how well they will do at the job in the future. People that do well on the predictor variable test will most likely do well at the job.
8. Statistical Conclusion Validity
Statistical conclusion validity refers to whether the conclusions drawn by the authors of a study are supported by the statistical procedures.
For example, did the study apply the correct statistical analyses, were adequate sampling procedures implemented, did the study use measurement tools that are valid and reliable?
If the answers to those questions are all “yes,” then the study has statistical conclusion validity. However, if the some or all of the answers are “no,” then the conclusions of the study are called into question.
Using the wrong statistical analyses or basing the conclusions on very small sample sizes, make the results questionable. If the results are based on faulty procedures, then the conclusions cannot be accepted as valid.
9. Criterion Validity
Criterion validity is sometimes called predictive validity. It refers to how well scores on one measurement device are associated with scores on a given performance domain (the criterion).
For example, how well do SAT scores predict college GPA? Or, to what extent are measures of consumer confidence related to the economy?
An example of low criterion validity is how poorly athletic performance at the NFL’s combine actually predicts performance on the field on gameday. There are dozens of tests that the athletes go through, but about 99% of them have no association with how well they do in games.
However, nutrition and exercise are highly related to longevity (the criterion). Those constructs have criterion validity because hundreds of studies have identified that nutrition and exercise are directly linked to living a longer and healthier life.
There are so many types of validity because the measurement precision of abstract concepts is hard to discern. There can also be confusion and disagreement among experts on the definition of constructs and how they should be measured.
For these reasons, social scientists have spent considerable time developing a variety of methods to assess the validity of their measurement tools. Sometimes this reveals ways to improve techniques, and sometimes it reveals the fallacy of trying to predict the future based on faulty assessment procedures.
Cook, T.D. and Campbell, D.T. (1979) Quasi-Experimentation: Design and Analysis Issues for Field Settings. Houghton Mifflin, Boston.
Cohen, R. J., & Swerdlik, M. E. (2005). Psychological testing and assessment: An introduction to tests and measurement (6th ed.). New York: McGraw-Hill.
Cronbach, L. J. (1970). Essentials of Psychological Testing . New York: Harper & Row.
Cronbach, L. J., and Meehl, P. E. (1955) Construct validity in psychological tests. Psychological Bulletin , 52 , 281-302.
Simms, L. (2007). Classical and Modern Methods of Psychological Scale Construction. Social and Personality Psychology Compass, 2 (1), 414 – 433. https://doi.org/10.1111/j.1751-9004.2007.00044.x
Dave Cornell (PhD)
Dr. Cornell has worked in education for more than 20 years. His work has involved designing teacher certification for Trinity College in London and in-service training for state governments in the United States. He has trained kindergarten teachers in 8 countries and helped businessmen and women open baby centers and kindergartens in 3 countries.
- Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 25 Positive Punishment Examples
- Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 25 Dissociation Examples (Psychology)
- Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 15 Zone of Proximal Development Examples
- Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ Perception Checking: 15 Examples and Definition
Chris Drew (PhD)
This article was peer-reviewed and edited by Chris Drew (PhD). The review process on Helpful Professor involves having a PhD level expert fact check, edit, and contribute to articles. Reviewers ensure all content reflects expert academic consensus and is backed up with reference to academic studies. Dr. Drew has published over 20 academic articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education and holds a PhD in Education from ACU.
- Chris Drew (PhD) #molongui-disabled-link 25 Positive Punishment Examples
- Chris Drew (PhD) #molongui-disabled-link 25 Dissociation Examples (Psychology)
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- Chris Drew (PhD) #molongui-disabled-link Perception Checking: 15 Examples and Definition
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Types of Reliability and Validity
Jul 30, 2014
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Types of Reliability and Validity. Research Methods (cont’d). Types of Reliability. There are four types of reliability measures. For a test/experiment to be deemed ‘reliable,’ it must yield consistent results no matter the circumstances . Four types: Inter-rater Test-retest
- social science looks
- same knowledge
- specific group
- conclusion validity
Types of Reliability and Validity Research Methods (cont’d)
Types of Reliability • There are four types of reliability measures. For a test/experiment to be deemed ‘reliable,’ it must yield consistent results no matter the circumstances. • Four types: • Inter-rater • Test-retest • Parallel-forms • Internal consistency
Types of Reliability (cont’d) • Inter-rater reliability= the test/experiment yields similar results no matter who is giving the test. • This eliminates observer bias. • Test-retest reliability= the test/experiment yields similar results across time. • Factors that can influence test variation: mood, disruptions, time of day. • An unreliable test will vary greatly in results depending on these factors.
Types of Reliability (cont’d) • Internal consistency reliability= the test/experiment will yield similar results, even though different questions are asked. • Questions are different, but material is similar in construct. • Example: The following two questions test the same knowledge, although phrased differently. • Which social science looks at past cultures and societies? • Past cultures and societies are the predominant focus of which social science?
Types of Reliability (cont’d) • Parallel forms reliability= used to determine the best versionof a test/experiment. • Different people take different versions of a test at the same time. • These two versions are done in parallel, to determine which is best. • Example: two field tests are given to students at two different schools on the same day. Whichever test yields more consistent results becomes the nationwide test.
Types of Validity • A test that has validity accurately measures what it is supposed to. • There are 4 main types of validity that are important to sociological research: • Construct validity • Content validity • Conclusion validity • Face validity
Types of Validity • Construct validity= determines the quality of an instrument, test or experiment. • Does it measure what it is designed to measure? • Content validity= measures what it is supposed to measure AND includes an adequate sample. • Does the measurement include different groups or does it focus on the knowledge/characteristics of one specific group?
Types of Validity (cont’d) • Conclusion validity= a relationship between two variables can be determined, either positive or negative. • Face validity= appears to be valid on the surface. This should not be the sole factor in determining validity!!! • It can, however, be a good springboard for experimental design– does your experiment seem like it will work?
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Types of Validity in Research | Examples | PPT
In this article you will learn different types of validity in research in detail.
In social science research, validity is the degree to which a study measures what it intends to measure. Different types of validity exist and when designing a study, researchers need to take into account which types of validity are most important for their particular goals.
When conducting research, it is important to consider the different types of validity in order to ensure that the results are accurate. Each type of validity has different implications for the research being conducted. It is important to consider all types of validity when designing and conducting research.
- Table of Contents
In terms of measuring methods, validity refers to an instrument’s capacity to measure what it is intended to measure. Validity is defined as the extent to which the researcher measured what he intended to measure.
There are four types of validity in research
Criterion-related validity, construct validity, face validity.
The content validity of the measure ensures that it has an adequate and representative selection of items that tap the concept. Content validity is the extent to which a measure accurately captures the construct it is meant to measure. The stronger the content validity, the more the scale items represent the domain or universe of the concept being measured.
A panel of experts can testify to the instrument’s content validity.
✔ According to Kidder and Judd (1986), a test meant to quantify degrees of speech impairment can be regarded valid if it is so appraised by a group of experts (i.e., professional speech therapists).
It is important for content validity to be high in order for a measure to be useful.
There are several ways to assess content validity, including
Expert judgment involves having experts in the field review the items on a measure and make judgments about whether or not they are appropriate for assessing the construct of interest.
Content analysis involves analyzing the items on a measure to see if they cover all aspects of the construct of interest.
Both expert judgment and content analysis are important methods for assessing content validity.
However, content validity is ultimately determined by how well a measure predicts outcomes of interest. If a measure has good predictive validity, then it can be said to have good content validity.
Content validity is important because it is one way to determine whether a test is measuring what it is supposed to be measuring. If a test has good content validity, then we can have more confidence that the results of the test are accurate and reliable.
A math instructor creates an end-of-semester calculus test for her students. The exam should cover every type of calculus presented in class. If specific types of algebra are omitted, the results may not accurately reflect students’ understanding of the topic. Similarly, if she includes non-calculus questions, the findings are no longer a valid test of calculus knowledge.
Face validity suggests that the items intended to test a concept appear to measure the idea on the surface. It refers to the degree to which a test appears to measure what it is supposed to measure.
Face validity is not an accurate predictor of a test’s actual psychometric properties, but it is important for determining whether a test will be accepted by those who will be taking it. If a test has poor face validity, test-takers may resist taking the test or may not take it seriously, which can lead to invalid results.
There are several ways to assess face validity.
✔ ask experts in the field whether they believe the test measures what it is supposed to measure.
✔ask potential test-takers whether they believe the test is a good measure of the desired construct.
You design a questionnaire to evaluate the consistency of people’s food habits. You go over the items in the questionnaire, which include questions regarding every meal of the day as well as snacks taken in between for every day of the week. On the surface, the survey appears to be a solid depiction of what you want to test, so you give it a high face validity validity.
Another example of face validity would be a personality test that included items that assessed whether the respondent was outgoing, shy, etc.
When a measure differentiates individuals on a criterion that it is anticipated to predict, criterion-related validity is established. Criterion-related validity is a type of validity that demonstrates how well a measure correlates with an established criterion. It is concerned with the relationship between a measure and some external criterion, such as a performance on another test or a real-world behavior.
This type of validity is important when choosing a measure to use for decision making, because it can help to ensure that the results of the measure are accurate.
If you want to predict how well someone will do on a test, you would want to use a measure with good criterion-related validity.
If you want to know whether or not a new math test can predict how well students will do on the state math test, you would look at the criterion-related validity of that new math test.
There are two types of criterion-related validity,
- Predictive validity
- Concurrent validity
Predictive validity is concerned with the ability of a measure to predict future performance on some criterion. It assesses the ability of a measure to predict future results. It is a type of validity that is used to assess whether or not a predictor variable is able to accurately predict an outcome variable.
Predictive validity refers to a measuring instrument’s ability to distinguish between persons in relation to a future criterion. It is usually expressed as a correlation coefficient.
✔ A high predictive validity means that the measure can accurately predict future performance
✔ A low predictive validity means that the measure is not a good predictor of future performance.
Predictive validity is important for choosing measures that will be useful for predicting future performance.
If you are interested in predicting how well students will do on a test, you would want to choose a measure with high predictive validity for that test. However, if you are interested in predicting how well students will do in school overall, you would want to choose a measure with high predictive validity for school overall.
If you were interested in predicting whether or not someone would get a job offer based on their interview performance, you would want to have strong predictive validity. In order for predictive validity to be strong, the relationship between the predictor and outcome variables must be linear and free from error. Additionally, the predictor variable must be able to explain a significant portion of the variance in the outcome variable.
Concurrent validity is concerned with the relationship between a measure and some other measure that is being used as a criterion at the same time. This validity assesses the ability of a measure to predict results that have already been established,
Concurrent validity is established when the scale discriminates across persons who are known to be different, implying that they should score differently on the instrument. It is the extent to which a measure predicts the same construct at the same time. Concurrent validity can be established through correlations between measures, or by using known groups comparisons.
Concurrent validity is an important tool for researchers to understand how well a new measure correlates with an existing measure.
A new intelligence test might be given to a group of students who have already taken an established intelligence test. The results of the new test can then be compared to the results of the established test to see how well the two measures correlate. This type of validity can be used to judge the quality of a new measure.
Construct validity measures how well the findings of using the measure fit the theories around which it is constructed. This term used in the psychological literature to refer to the extent to which a measure accurately reflects the construct it is purporting to measure.
In order for a measure to be said to have construct validity,
✔ It must first be shown to be reliable – that is, it must produce consistent results across repeated measurements.
Once reliability has been established, researchers can then begin to look at whether or not the measure is actually tapping into the construct of interest.
If you were interested in measuring anxiety, you would want to show that your measure is correlated with other measures of anxiety and not with measures of unrelated constructs such as depression.
In order to establish construct validity, researchers need to provide evidence that the constructs they are interested in actually exist. One way to do this is to show that the items on a measure are tapping into a single construct and that this construct is related to other constructs in the way that theory predicts.
Construct Validity is assessed through
When the scores obtained with two separate instruments assessing the same concept are highly associated, convergent validity is established. Convergent validity is the degree to which different measures of the same construct produce similar results. It is important to establish convergent validity when using multiple measures of a construct, such as self-report and performance-based measures, in order to ensure that the results are consistent across measures.
There are a few ways to establish convergent validity, including
- Factor Analysis.
Correlation is a statistical measure that can be used to assess the strength of the relationship between two variables.
Factor analysis is a statistical technique that can be used to identify underlying factors or dimensions in a set of data.
Establishing convergent validity is important for ensuring the reliability and validity of research findings. When measures of a construct are shown to be convergent, it provides confidence that the findings are accurate and trustworthy.
There are several ways to establish convergent validity. In addition to statistical methods, researchers often use theoretical arguments and empirical evidence from previous studies. Establishing convergent validity is important for ensuring that your research measures are valid and reliable.
Let’s say you are interested in studying the relationship between hours of sleep and academic performance. To measure these constructs, you could use self-report surveys. In order to establish convergent validity, you would want to see a strong correlation between the two measures.
Discriminant validity is established when two variables are projected to be uncorrelated based on theory, and the scores obtained by measuring them are empirically confirmed to be so.
It is a statistical concept that is used to determine whether or not two constructs are measuring different things. Discriminant validity is the ability of a test to accurately measure what it is supposed to measure. In order for a test to be considered valid, it must have discriminant validity.
✔ In order for discriminant validity to be present, the two constructs must be correlated with each other, but they must also be significantly different from each other.
✔ If discriminant validity is not present, then it is possible that the two constructs are actually measuring the same thing.
Discriminant validity is important because it allows researchers to be confident that the results of their study are accurate and not due to chance.
A good example of discriminant validity would be if a study was able to show that there are differences between males and females on a measure of aggression. This would demonstrate that the measure used in the study is able to discriminate between the two groups.
Another example of discriminant validity would be if a study was able to show that there are differences between people of different ages on a measure of intelligence. This would demonstrate that the measure used in the study is able to discriminate between different age groups.
Types of Validity Used in Methodologies
Please read through some of our other articles with examples and explanations if you’d like to learn more about research methodology.
- APA Reference Page
- MLA Citations
- Chicago Style Format
- “et al.” in APA, MLA, and Chicago Style
- Footnote Citation
- Do All References in a Reference List Need to Be Cited in Text?
- PLS-SEM model
- Principal Components Analysis
- Multivariate Analysis
- Friedman Test
- Chi-Square Test (Χ²)
- Effect Size
- Critical Values in Statistics
- Statistical Analysis
- Calculate the Sample Size for Randomized Controlled Trials
- Covariate in Statistics
- Avoid Common Mistakes in Statistics
- Standard Deviation
- Derivatives & Formulas
- Build a PLS-SEM model using AMOS
- Principal Components Analysis using SPSS
- Statistical Tools
- Type I vs Type II error
- Descriptive and Inferential Statistics
- Microsoft Excel and SPSS
- One-tailed and Two-tailed Test
- Parametric and Non-Parametric Test
- Independent vs. Dependent Variable
- Research Article and Research Paper
- Proposition and Hypothesis
- Principal Component Analysis and Partial Least Squares
- Academic Research vs Industry Research
- Clinical Research vs Lab Research
- Research Lab and Hospital Lab
- Thesis Statement and Research Question
- Quantitative Researchers vs. Quantitative Traders
- Premise, Hypothesis and Supposition
- Survey Vs Experiment
- Hypothesis and Theory
- APA vs. MLA
- Ghost Authorship vs. Gift Authorship
- Basic and Applied Research
- Cross-Sectional vs Longitudinal Studies
- Survey vs Questionnaire
- Open Ended vs Closed Ended Questions
- Experimental and Non-Experimental Research
- Inductive vs Deductive Approach
- Null and Alternative Hypothesis
- Reliability vs Validity
- Population vs Sample
- Conceptual Framework and Theoretical Framework
- Bibliography and Reference
- Stratified vs Cluster Sampling
- Sampling Error vs Sampling Bias
- Internal Validity vs External Validity
- Full-Scale, Laboratory-Scale and Pilot-Scale Studies
- Plagiarism and Paraphrasing
- Research Methodology Vs. Research Method
- Mediator and Moderator
- Dissertation Topic
- Thesis Statement
- Research Proposal
- Research Questions
- Research Problem
- Research Gap
- Types of Research Gaps
- Operationalization of Variables
- Literature Review
- Research Hypothesis
- Measurement of Scale
- Sampling Techniques
- Data Coding
- Research Methods
- Quantitative Research
- Qualitative Research
- Case Study Research
- Survey Research
- Conclusive Research
- Descriptive Research
- Cross-Sectional Research
- Theoretical Framework
- Conceptual Framework
- Grounded Theory
- Quasi-Experimental Design
- Mixed Method
- Correlational Research
- Randomized Controlled Trial
- Stratified Sampling
- Ghost Authorship
- Secondary Data Collection
- Primary Data Collection
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