What is Construct Validity?
Construct validity relates to the soundness of inferences that you draw from test scores and other measurements. Specifically, it addresses whether a test measures the intended construct. For example, does a test that evaluates self-esteem truly measure that construct or something else?
A construct is a complex idea that is formed by combining simpler ideas. Researchers create them to understand a latent variable that is not directly observable. Anxiety, self-esteem, and persistence are examples of psychological constructs. Constructs are a single concept, but they’re complex and manifest themselves in different ways. Researchers can’t measure any of them directly but instead infer them from multiple items on a test.
Construct validity is particularly important in psychology, language studies, and social sciences because these fields work with intangible concepts, such as personality traits, emotional states, intelligence levels, skills, abilities, etc. These ideas are not directly observable because they exist only in the human brain. Frequently, they don’t even have concrete measurement units.
Measurement instruments and tests ask questions that collectively evaluate these constructs. Researchers use these instruments to make inferences and answer their research questions. However, if the test doesn’t measure the concept it claims to measure, the researchers’ conclusions are invalid. For instance, if a self-esteem test actually measures happiness, all findings that researchers make using that test are now suspect. That’s why evaluating construct validity is crucial!
Validity theory currently places construct validity as the primary focus of validity research. Let’s learn more about construct validity and how to evaluate it.
Assessing Construct Validity
In broad terms, there are two phases to evaluating construct validity. The first phase relates to assessing the definition of your construct and the measurement instrument. The second phase involves judging the measures from the instrument to see how they correlate with other characteristics.
Many websites fail to discuss the early portion of construct validity. But the care and dedication you put into the earlier work of defining the construct and creating the test are crucial for successfully validating it later.
Each concept will have its unique circumstances and challenges. Expertise and research are crucial for devising profound definitions and insightful measurement instruments. Here are some general tips for defining it and evaluating how well an instrument operationalizes its measurements.
Construct Definition and Operationalization
The early stages of construct validity involve assessing the definition of your construct. Your description should indicate what it represents and how it is similar and different from other ideas. It should be relevant to your field of study and ongoing research. You should explain the rationale for your definition, provide evidence backing it, and show why it’s important to measure. Is there a theoretical foundation that supports it?
After assessing the validity of your construct’s definition, you can evaluate the nature of the questions in your measurement instrument. Have you operationalized the definition in a meaningful way? In other words, does your test ask the right questions and genuinely relate to it? This process involves research, reviews by experts, and pilot studies. In short, the items on your measurement instrument must support your construct’s definition. The two go hand in hand. If they don’t, your test will be invalid.
Evaluating Construct Validity with Correlations
After defining your construct, creating the test, and administering it, you can evaluate the test scores. This process involves determining whether your measures correlate with other concepts in a manner consistent with theory.
Constructs are abstractions of unobserved, latent variables. That description makes it sound hard to evaluate construct validity. However, theory and prior research indicate how your characteristic should relate to attributes. There’s a web of correlations that exists between them. When assessing one concept, you can see whether it fits in correctly with other ideas. Consequently, evaluating construct validity for one assessment requires other measures for comparison.
Construct validity links the theoretical relationships between characteristics with observed associations to see how closely they agree. It evaluates the full range of attributes for the concept you’re measuring and determines if they correlate appropriately with other attributes, behaviors, and events.
Researchers typically assess construct validity by correlating different types of data. You expect your measurements to have particular relationships with other variables. For measures to have high validity, they need to satisfy at a minimum the following two subcategories of construct validity—convergent and discriminant validity. These two concepts work together to correctly evaluate whether your measure fits the big picture with other attributes. Researchers also use factor analysis to assess construct validity. Learn more about Factor Analysis.
Related post: Interpreting Correlation
Convergent validity relates to relationships between the measure you’re assessing and other characteristics. If your data are valid, you’d expect to see a specific correlation pattern between your measurements and other constructs.
For example, anxiety measures should correlate positively with other negative thoughts. You might expect to see positive correlations between anxiety scores, eating disorders, and depression. Seeing this pattern of relationships supports convergent validity. Our measure for anxiety correlates with other characteristics as theory expects and exhibits construct validity.
It is called convergent validity because scores for different measures converge or correspond as theory suggests. You should observe high correlations (either positive or negative). It is also known as criterion validity.
Discriminant validity is the opposite of convergent validity. If you have valid data, you expect particular pairs of attributes to correlate positively or negatively. However, for other pairs of variables, you anticipate no relationship.
For example, if the locus of control and self-esteem are not related in reality, measures of these characteristics should not correlate. You should observe a low correlation between them.
It is also known as divergent validity because it relates to the differentiation between attributes. Low correlations (close to zero) suggest that measures of one concept do not relate to the values of another. In other words, the instrument successfully distinguishes between unrelated concepts.
In short, the measures from the instrument you’re evaluating must correlate positively and negatively with the theoretically appropriate characteristics and not correlate with unrelated attributes.
Cronbach, L. J.; Meehl, P.E. (1955). Construct Validity in Psychological Tests. Psychological Bulletin. 52 (4): 281–302.