![]() ![]() The concept of validity generally crosses three major aspects: construct validity, content validity, and criterion validity. Thus, it is not the scale itself that is formally validated, but rather the interpretation of the scores it generates 1, 2, 3. The current validity framework postulates that validity corresponds to the level of evidence and theoretical justification that supports the interpretation and use of scores given by a scale. Validating scales to discriminate between clinical populations is a relatively common procedure in psychology and medicine. In particular, this study examined the extent to which measured items are inferable by theoretically related items, as well as the extent to which the information carried by a given construct can be translated into other theoretically compatible normative scales based on other constructs (thereby providing information about construct validity) as well as the replicability of clinical decision rules on several partitions (thereby providing information about criterion validity). According to these findings, these approaches are capable of achieving construct and criterion validity and therefore could provide an additional layer of evidence to traditional validation approaches. XGBoost, Random Forest and Support-Vector machine learning algorithms were employed in order to make predictions based on the pattern of participants’ responses by systematically controlling computational experiments with artificial experiments whose results are guaranteed. Through the application of computational methods, we present a new strategy for estimating construct validity and criterion validity. Validating scales for clinical use is a common procedure in medicine and psychology.
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