In conjunction with appropriately designed test, cognitive diagnosis models (CDMs) can be used to identify students’ mastery or nonmastery of skills in a domain. The diagnostic and finer-grained information from CDMs can lead to tailored instruction and remediation. In this chapter, we introduce the generalized deterministic input, noisy “and” gate (G-DINA) model as a general CDM framework. In addition to various CDM formulations, the G-DINA model framework subsumes parameter
estimation, model fit evaluation, Q-matrix validation, differential item functioning analysis, and classification accuracy estimation. Using empirically based simulated data, we illustrate how CDM analysis can be performed using the GDINA R package.
The present study compared the consistency of the results of three nonparametric differential item functioning (DIF) techniques – the Cumulative Common Log-Odds Ratio (CCLOR), Standardized Mean Difference (SMD), and the Mantel Test (Mantel) in detecting gender DIF in the Emotional Quotient Scale – College Version. The sample comprised 1,229 college students (male = 657; women= 572) from a state university in the Philippines. The agreement of the DIF methods was determined using classification consistency and matching percentages. Results show that CCLOR and Mantel agreed perfectly in detecting gender DIF items. SMD, on the other hand, had a moderate to high agreement with the two other DIF techniques. The agreement among the DIF methods was lower when DIF effect size was considered.