Construct validity

Construct validity concerns how well a set of indicators represents or reflects a concept that is not directly measurable. Construct validation is the accumulation of evidence to support the interpretation of what a measure reflects. Modern validity theory defines construct validity as the overarching concern of validity research, subsuming all other types of validity evidence such as content validity and criterion validity. Construct validity is the appropriateness of inferences made based on observations or measurements (often test scores), specifically whether a test can reasonably be considered to reflect the intended construct. Constructs are abstractions that are deliberately created by researchers to conceptualize the latent variable, which is correlated with scores on a given measure (although it is not directly observable). Construct validity examines the question: Does the measure behave like the theory says a measure of that construct should behave? Construct validity is essential to the perceived overall validity of the test. Construct validity is particularly important in the social sciences, psychology, psychometrics and language studies.…

Construct validity concerns how well a set of indicators represents or reflects a concept that is not directly measurable. Construct validation is the accumulation of evidence to support the interpretation of what a measure reflects. Modern validity theory defines construct validity as the overarching concern of validity research, subsuming all other types of validity evidence such as content validity and criterion validity. Construct validity is the appropriateness of inferences made based on observations or measurements (often test scores), specifically whether a test can reasonably be considered to reflect the intended construct. Constructs are abstractions that are deliberately created by researchers to conceptualize the latent variable, which is correlated with scores on a given measure (although it is not directly observable). Construct validity examines the question: Does the measure behave like the theory says a measure of that construct should behave? Construct validity is essential to the perceived overall validity of the test. Construct validity is particularly important in the social sciences, psychology, psychometrics and language studies. Psychologists such as Samuel Messick (1998) have pushed for a unified view of construct validity "...as an integrated evaluative judgment of the degree to which empirical evidence and theoretical rationales support the adequacy and appropriateness of inferences and actions based on test scores..." While Messick's views are popularized in educational measurement and originated in a career around explaining validity in the context of the testing industry, a definition more in line with foundational psychological research, supported by data-driven empirical studies that emphasize statistical and causal reasoning was given by (Borsboom et al., 2004). Key to construct validity are the theoretical ideas behind the trait under consideration, i.e., the concepts that organize how aspects of personality, intelligence, etc. are viewed. Paul Meehl states that, "The best construct is the one around which we can build the greatest number of inferences, most directly." Scale purification, i.e., "the process of eliminating items from multi-item scales" (Wieland et al., 2017), can influence construct validity. A framework presented by Wieland et al. (2017) highlights that both statistical and judgmental criteria need to be taken into consideration when making scale purification decisions. ===Convergent and discriminant validity=== Convergent and discriminant validity are the two subtypes of validity that make up construct validity. Convergent validity refers to the degree to which two measures of constructs that theoretically should be related, are in fact related. In contrast, discriminant validity tests whether concepts or measurements that are supposed to be unrelated are, in fact, unrelated. Take, for example, a construct of general happiness. If a measure of general happiness had convergent validity, then constructs similar to happiness (satisfaction, contentment, cheerfulness, etc.) should relate positively to the measure of general happiness. If this measure has discriminant validity, then constructs that are not supposed to be related positively to general happiness (sadness, depression, despair, etc.) should not relate to the measure of general happiness. Measures can have one of the subtypes of construct validity and not the other. Using the example of general happiness, a researcher could create an inventory where there is a very high positive correlation between general happiness and contentment, but if there is also a significant positive correlation between happiness and depression, then the measure's construct validity is called into question. The test has convergent validity but not discriminant validity. ==Threats to construct validity== Apparent construct validity can be misleading due to a range of problems in hypothesis formulation and experimental design. Hypothesis guessing: If the participant knows, or guesses, the desired end-result, the participant's actions may change. An example is the Hawthorne effect: in a 1925 industrial ergonomics study conducted at the Hawthorne Works factory outside Chicago, experimenters observed that both lowering and brightening the ambient light levels improved worker productivity. They eventually determined the basis for this paradoxical result: workers who were aware of being observed worked harder no matter what the change in the environment. Bias in experimental design (intentional or unintentional). An example of this is provided in Stephen Jay Gould's 1981 book "The Mismeasure of Man". Among the questions used around the time of World War I in the battery used to measure intelligence was "In which city do the Dodgers play?" (they were then based in Brooklyn). Recent immigrants to the US from Eastern Europe unfamiliar with the sport of baseball got the answer wrong, and this was used to infer that Eastern Europeans had lower intelligence. The question did not measure intelligence: it only measured how long one had lived in the US and become accultured to a popular pastime. Researcher expectations may be communicated unintentionally to the participants non-verbally, eliciting the desired effect. To control for this possibility, double-blind experimental designs should be used where possible. That is, the evaluator of a particular participant should be unaware of what intervention has been performed on that particular participant or should be independent of the experimenter. Defining predicted outcome too narrowly. For instance, using only job satisfaction to measure happiness will exclude relevant information from outside the workplace. Confounding variables (covariates): The root cause for the observed effects may be due to variables that have not been considered or measured. An in-depth exploration of the threats to construct validity is presented in Trochim.

Text derived from https://en.wikipedia.org/w/index.php?oldid=1362891131 under CC BY-SA 4.0; modifications have been made.