Exploring the Impact of Q-Matrix Specifications Through a DINA Model in a Large-Scale Mathematics Assessment

Abstract

The demand for diagnostic feedback has triggered extensive research on cognitive diagnostic models (CDMs), such as the deterministic input, noisy output and gate (DINA) model. This study explored two Q-matrix specifications with the DINA model in a statewide large-scale mathematics assessment. The first Q-matrix was developed based on five predefined content reporting categories, and the second was based on the post hoc coding of 15 attributes by test-development experts. Total raw scores correlated strongly with the number of skills mastered, using both Q-matrices. Correlations between the DINA-model item statistics and those from the item response theory analyses were moderate to strong, but were always lower for the 15-skill model. Results highlighted the trade-off between finer-grained modeling and less precise model estimation.