<unk>  is the accumulation of evidence to support the interpretation of what a measure reflects. Modern<unk>  theory defines<unk>  as the overarching concern of<unk>  research, subsuming all other types of<unk>  evidence such as content<unk>  and criterion<unk> .<unk>  is the appropriateness of inferences made on the basis of observations or measurements (often test scores), specifically whether a test can reasonably be considered to reflect the intended<unk> .<unk> s are abstractions that are deliberately created by researchers in order to conceptualize the latent variable, which is correlated with scores on a given measure (although it is not directly observable).<unk>  examines the question: Does the measure behave like the theory says a measure of that<unk>  should behave?<unk>  is essential to the perceived overall<unk>  of the test.<unk>  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<unk>  "...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<unk>  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<unk>  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<unk>  is the one around which we can build the greatest number of inferences, in the most direct fashion." Scale purification, i.e. "the process of eliminating items from multi-item scales" (Wieland et al., 2017) can influence<unk> . A framework presented by Wieland et al. (2017) highlights that both statistical and judgmental criteria need to be taken under consideration when making scale purification decisions.
Construct validity
Outcomes paradox
Inequality in disease
Appropriate use criteria
Relative biological effectiveness
Widowhood effect
Proportional reporting ratio
Automation bias