Using machine-learning strategies to solve psychometric problems.

ABSTRACT: Validating scales for clinical use is a common procedure in medicine and psychology. Through the application of computational methods, we present a new strategy for estimating construct validity and criterion validity. 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. 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. In particular, this study examined the extent to which measured items are inferable by theoretically rel

SUBMITTER: Trognon A 

PROVIDER: S-EPMC9640572 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature