A Comparison of Differential Item Functioning Detection Methods in Cognitive Diagnostic Models

Frontiers in Psychology

article

Yanlou Liu¹  Hao Yin²  Tao Xin³  Laicheng Shao⁴  Lu Yuan³ 

[1] Qufu Normal University;Department of Psychology, School of Education, Qufu Normal University;Collaborative Innovation Center of Assessment toward Basic Education Quality, Beijing Normal University;School of Economics and Management, Taishan University

关键词: cognitive diagnostic model;    Wald statistics;    differential item functioning;    information matrix;    logistic regression method;   

DOI  :  10.3389/fpsyg.2019.01137

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

As a class of discrete latent variable models, cognitive diagnostic models have been widely researched in education, psychology, and many other disciplines. Detecting and eliminating differential item functioning (DIF) items from cognitive diagnostic tests is of great importance for test fairness and validity. A Monte Carlo study with varying manipulated factors was carried out to investigate the performance of the Mantel-Haenszel (MH), logistic regression (LR), and Wald tests based on item-wise information, cross-product information, observed information, and sandwich-type covariance matrices (denoted by W d , W XPD , W Obs , and W Sw , respectively) for DIF detection. The results showed that (1) the W XPD and LR methods had the best performance in controlling Type I error rates among the six methods investigated in this study and (2) under the uniform DIF condition, when the item quality was high or medium, the power of W XPD , W Obs , and W Sw was comparable with or superior to that of MH and LR, but when the item quality was low, W XPD , W Obs , and W Sw were less powerful than MH and LR. Under the non-uniform DIF condition, the power of W XPD , W Obs , and W Sw was comparable with or higher than that of LR.

【 授权许可】

CC BY