The accuracy and consistency of mastery for each content domain using the Rasch and deterministic inputs, noisy “and” gate diagnostic classification models: a simulation study and a real-world analysis using data from the Korean Medical Licensing Examination

ABSTRACT:

Purpose

Diagnostic classification models (DCMs) were developed to identify the mastery or non-mastery of the attributes required for solving test items, but their application has been limited to very low-level attributes, and the accuracy and consistency of high-level attributes using DCMs have rarely been reported compared with classical test theory (CTT) and item response theory models. This paper compared the accuracy of high-level attribute mastery between deterministic inputs, noisy “and” gate (DINA) and Rasch models, along with sub-scores based on CTT.

Methods

First, a simulation study explored the effects of attribute length (number of items per attribute) and the correlations among attributes with respect to the accuracy of mastery. Second, a real-data study examined

SUBMITTER: Seo DG 

PROVIDER: S-EPMC8381025 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature