Key Takeaways
- Construct validity is a type of validity that assesses how well a particular measurement reflects the theoretical construct it is intended to measure.
- This form of validity involves making sure that the items included in a research tool are actually measuring what they are supposed to measure.
- Construct validity can be established through a variety of statistical measures but is more difficult to establish than other types of psychological validity because constructs tend to be abstract and difficult to objectify.
What is Construct Validity?
Construct validity refers to the degree to which a psychological test or assessment measures the abstract concept or psychological construct that it purports to measure. In other words, it examines whether the test is actually measuring what it claims to measure.
It involves both the theoretical relationship between a test and the construct it claims to measure, as well as the empirical evidence that the test measures that construct.
Different types of evidence contribute to construct validity, including content validity, criterion validity, convergent and discriminant validity, etc.
Construct validation is an ongoing process of learning more about what a test measures, the meaning of scores derived from the test, and the appropriateness of specific interpretations and uses of scores.
For instance, if a researcher develops a new questionnaire to evaluate aggression, the instrument’s construct validity would be the extent to which it actually assesses aggression as opposed to assertiveness, social dominance, and so on (American Psychological Association).
As another, more clinical example, a medication might only be effective due to the placebo effect rather than the strength of its active ingredients being absorbed into the bloodstream. In this case, the construct validity of the medication would be low.
Construct validity is important because it determines how useful an instrument or test is for measuring a specific concept (Smith, 2005).
How to assess construct validity
Psychological constructs cannot, in most cases, be observed directly. People cannot directly observe neuroticism, extraversion, dependency, or any other inferred trait in the way that a physicist can measure the length of an object relative to a bar corresponding to the true length of a meter.
Thus, psychologists must comprehensively and accurately measure correlations between thoughts and behaviors and their theories of mind. The process of doing this is the process of establishing construct validity (Smith, 2005).
High construct validity means there is substantial theoretical and empirical evidence that a test measures the intended construct. Low construct validity implies the test may be measuring something else unintended or that score interpretations are questionable.
Evaluating construct validity quantitatively often involves sophisticated statistical analyses like factor analysis, structural equation modeling, and correlations with other validated measures. Qualitative judgment is also important.
There are several ways to establish construct validity, including content, convergent, discriminant, and nomological. Content validity refers to the extent to which an instrument covers all aspects of a construct.
There are at least two processes through which a researcher can establish construct validity (Fink, 2010).
Firstly, a researcher might hypothesize that the new measure correlates with one or more measures of a similar characteristic and does not correlate with measures of dissimilar characteristics. This means that the measure would have both convergent and discriminant validity.
For example, a survey researcher who is validating a survey on levels of self-reported happiness in different countries might posit that it is highly correlated with another quality-of-life instrument, a measure of functioning, and a measure of health status.
At the same time, the survey researcher would hypothesize that the new measure does not correlate with selected measures of cultural social desirability — the tendency of people in some cultures to identify more positively than others — and of hostility (Fink, 2010).
In the second case, a researcher could hypothesize that a measure can distinguish one group from another based on some important variable. For instance, a researcher might develop a measure of anxiety and hypothesize that people with anxiety disorders will score higher on the measure than people without anxiety disorders.
This requires translating theories around anxiety into measurable criteria and proving that the measure consistently and correctly distinguishes between people with and without anxiety disorders.
Examples of construct validity
Questionnaires
One method of measuring construct validity frequently used for questionnaires is Confirmatory Factor Analysis.
In Confirmatory Factor Analysis, researchers state how they believe the questionnaire items are correlated by creating or specifying a theoretical model.
This theoretical model may be based on an earlier exploratory factor analysis, on previous research, or on an individual’s own theory.
The researcher can then calculate the statistical likelihood that the data from the questionnaire items fit with this model, thus confirming their theory (Phye et al., 2001).
The factor analysis model illustrates the process of confirmatory factor analysis. It shows how questionnaire items are correlated because they each relate to an underlying latent construct or factor.
These correlations are known as factor loadings and are represented by arrows between the latent factor and the questionnaire items on the model. The questions that participants answer on questionnaires can often be latent measures of the construct.
For instance, individuals taking a test of introversion and extroversion may answer questions such as “I enjoy being in social situations” and “I feel comfortable talking to new people.”
If the model shows that the data fit with the theory, then this provides evidence for construct validity. If the model does not fit with the data, then this is evidence against construct validity.
There are many different ways that researchers can assess how well a model fits with data, but one common method is through a goodness-of-fit test (Phye et al., 2001).
Psychological tests (anxiety, intelligence)
Cronbach and Meehl (1955) established one of the first measures of construct validity for psychological tests.
They emphasized principles for making inferences about the meaning of test scores or experimental outcomes over just doing a series of procedural “validity analyses.”
In the later researchers Lawshe’s (1985) view, the validation process should be understood as a system involving sound research design, appropriate data analysis, and suitable inferences from one’s findings.
For example, consider an intelligence test. The first step in establishing construct validity is to show that the answers to test items are correlated with a theory of intelligence.
People can do this in a number of ways, including showing that the test is correlated with other measures of intelligence or that it predicts success on some outcome that is known to be related to intelligence.
The next step is to show that the test is valid for the specific population that it will be used with. This might involve demonstrating that the test works equally well with different groups of people or that it produces similar results regardless of when it is administered.
Education
Construct validity is important for education because providing accurate information to students and educators about student ability is essential for leading students on suitable learning paths and, ultimately, toward a secure career trajectory.
Construct validity is also important for creating equitable education systems. If instructors and educational researchers can show that a test has good construct validity, then they can be more confident that it is not biased against any particular group of students (Fink, 2010).
FAQs
Is construct validity internal or external?
Construct validity, like external and internal validity, is related to generalizing.
However, while external validity is concerned with generalizing from one experiment to another, construct validity is focused on generalizing within a single experiment. It answers questions about the measurement of a construct.
Thus, construct validity is more aligned with internal validity, which is a measure of how well a study is conducted and how accurately its results reflect the group being studied (Smith, 2005).
What is the difference between construct and content validity?
Content validity focuses on the items within the test, while construct validity focuses on the underlying latent construct or factor.
Content validity examines whether a test contains an appropriate sample of items to measure a specific concept, whereas construct validity looks at the relationships between test scores and external variables, such as other measures of the same concept.
Additionally, content validity is concerned with how accurately a test measures what it is supposed to measure, while construct validity is more focused on how well different variables fit together in a theoretical model.
How can we improve construct validity?
There are a few ways to improve the construct validity in an experiment.
Firstly, it is important to ensure that the researcher has chosen an appropriate measurement and clear definition of the concepts being studied. An explicit list of hypotheses should be formulated prior to data collection, which should be based on reliable theoretical assumptions and objective criteria for evaluating the results.
Secondly, different methods, such as interviews and surveys, should be used to assess a wide range of items related to the construct in question. This will help reduce errors associated with a single method or tool and provide more comprehensive coverage of the construct’s facets.
Finally, multiple indicators can be used for each concept being examined in order to enhance reliability and accuracy in measurement. Additionally, researchers should consider collecting data from multiple sources if possible, using statistical techniques like factor analysis or structural equation modeling (Lievens, 1998).
Why is construct validity the most difficult to measure?
Construct validity is often difficult to measure because most concepts in psychology, such as intelligence or attitude, are not easily measured with objective metrics. Many factors can influence the results of different measurement methods.
As a result, it is often more challenging for researchers to assess and evaluate the validity of their data than other types of validity, such as internal or external validity.
Finally, due to its abstract nature, it may be hard for individuals outside of research circles to comprehend the importance and implications of construct validity.
Is internal consistency the same as construct validity?
Internal consistency and construct validity are two similar but distinct concepts.
Internal consistency is a measure of how closely related the items within a scale are to one another, whereas construct validity is concerned with how well an instrument measures what it intends to measure.
Internal consistency is an important component of assessing construct validity because it can help reveal if the data collected from a given instrument is reliable and valid. However, construct validity on a broader level is a foundation that gives measures of internal consistency meaning.
When construct validity is low, the internal consistency of research instruments does not matter since they do not measure what the researcher intends to measure in the first place (Smith, 2005).
References
American Psychological Association. (n.D.) Construct Validity. American Psychological Association Dictionary
Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological bulletin, 52(4), 281.
Fink, A. (2010). Survey Research Methods. In Peterson, P. L., Baker, E., & McGaw, B. (2010). International encyclopedia of education. Elsevier Ltd.
Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563–575. https://doi.org/10.1111/j.1744-6570. 1975.tb01393.x
Lawshe, C. H. (1985). Inferences from personnel tests and their validity. Journal of Applied Psychology, 70(1), 237.
Lievens, F. (1998). Factors which improve the construct validity of assessment centers: A review. International Journal of Selection and Assessment, 6(3), 141-152.
Phye, G. D., Saklofske, D. H., Andrews, J. J., & Janzen, H. L. (2001). Handbook of Psychoeducational Assessment: A Practical Handbook A Volume in the EDUCATIONAL PSYCHOLOGY Series. Elsevier.
Smith, G. T. (2005). On construct validity: issues of method and measurement. Psychological assessment, 17(4), 396.
