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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.