Robustness of Adaptive Measurement of Change to Item Parameter Estimation Error.

ABSTRACT: Adaptive measurement of change (AMC) is a psychometric method for measuring intra-individual change on one or more latent traits across testing occasions. Three hypothesis tests-a Z test, likelihood ratio test, and score ratio index-have demonstrated desirable statistical properties in this context, including low false positive rates and high true positive rates. However, the extant AMC research has assumed that the item parameter values in the simulated item banks were devoid of estimation error. This assumption is unrealistic for applied testing settings, where item parameters are estimated from a calibration sample before test administration. Using Monte Carlo simulation, this study evaluated the robustness of the common AMC hypothesis tests to the presence of item parameter esti

SUBMITTER: Cooperman AW 

PROVIDER: S-EPMC9228691 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature