{Reference Type}: Journal Article
{Title}: Diagnostic Classification Models for Testlets: Methods and Theory.
{Author}: Xu X;Fang G;Guo J;Ying Z;Zhang S;
{Journal}: Psychometrika
{Volume}: 0
{Issue}: 0
{Year}: 2024 Mar 26
{Factor}: 2.29
{DOI}: 10.1007/s11336-024-09962-9
{Abstract}: Diagnostic classification models (DCMs) have seen wide applications in educational and psychological measurement, especially in formative assessment. DCMs in the presence of testlets have been studied in recent literature. A key ingredient in the statistical modeling and analysis of testlet-based DCMs is the superposition of two latent structures, the attribute profile and the testlet effect. This paper extends the standard testlet DINA (T-DINA) model to accommodate the potential correlation between the two latent structures. Model identifiability is studied and a set of sufficient conditions are proposed. As a byproduct, the identifiability of the standard T-DINA is also established. The proposed model is applied to a dataset from the 2015 Programme for International Student Assessment. Comparisons are made with DINA and T-DINA, showing that there is substantial improvement in terms of the goodness of fit. Simulations are conducted to assess the performance of the new method under various settings.