A Polytomous Model of Cognitive Diagnostic Assessment for Graded Data. Issue 3 (3rd July 2018) - Tu, Dongbo; Zheng, Chanjin; Cai, Yan; Gao, Xuliang; Wang, Daxun
Abstract : Pursuing the line of the difference models in IRT (Thissen & Steinberg, 1986 ), this article proposed a new cognitive diagnostic model for graded/polytomous data based on the deterministic input, noisy, and gate (Haertel, 1989 ; Junker & Sijtsma, 2001 ), which is named the DINA model for graded data (DINA-GD). We investigated the performance of a full Bayesian estimation of the proposed model. In the simulation, the classification accuracy and item recovery for the DINA-GD model were investigated. The results indicated that the proposed model had acceptable examinees' correct attribute classification rate and item parameter recovery. In addition, a real-data example was used to illustrate the application of this new model with the graded data or polytomously scored items.