Inferential Item-Fit Evaluation in Cognitive Diagnosis Modeling.

ABSTRACT: Research related to the fit evaluation at the item level involving cognitive diagnosis models (CDMs) has been scarce. According to the parsimony principle, balancing goodness of fit against model complexity is necessary. General CDMs require a larger sample size to be estimated reliably, and can lead to worse attribute classification accuracy than the appropriate reduced models when the sample size is small and the item quality is poor, which is typically the case in many empirical applications. The main purpose of this study was to systematically examine the statistical properties of four inferential item-fit statistics: S-X2 , the likelihood ratio (LR) test, the Wald (W) test, and the Lagrange multiplier (LM) test. To evaluate the performance of the statistics, a comprehensive set of fac

SUBMITTER: Sorrel MA 

PROVIDER: S-EPMC5978477 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature