An empirical Q‐matrix validation method for the sequential generalized DINA model

2019 ◽  
Vol 73 (1) ◽  
pp. 142-163 ◽  
Author(s):  
Wenchao Ma ◽  
Jimmy Torre
Keyword(s):  
Validation Method ◽  
Dina Model ◽  
Q Matrix

An Empirical Q-Matrix Validation Method for the Polytomous G-DINA Model

Psychometrika ◽  
2021 ◽  
Author(s):  
Jimmy de la Torre ◽  
Xue-Lan Qiu ◽  
Kevin Carl Santos
Keyword(s):  
Validation Method ◽  
Dina Model ◽  
Q Matrix

A New Method of Q-matrix Validation Based on DINA Model

2013 ◽  
Vol 44 (4) ◽  
pp. 558-568 ◽  
Author(s):  
Dong-Bo TU ◽  
Yan CAI ◽  
Hai-Qi DAI
Keyword(s):  
New Method ◽  
Dina Model ◽  
Q Matrix

scholarly journals Q-Matrix Refinement Based on Item Fit Statistic RMSEA

2018 ◽  
Vol 43 (7) ◽  
pp. 527-542 ◽  
Author(s):  
Chunhua Kang ◽  
Yakun Yang ◽  
Pingfei Zeng
Keyword(s):  
Search Algorithm ◽  
Delta Method ◽  
Mean Square ◽  
Simulation Studies ◽  
Response Data ◽  
Item Fit ◽  
Dina Model ◽  
Q Matrix ◽  
Diagnosis Model ◽  
Effectiveness And Efficiency

A Q-matrix, which reflects how attributes are measured for each item, is necessary when applying a cognitive diagnosis model to an assessment. In most cases, the Q-matrix is constructed by experts in the field and may be subjective and incorrect. One efficient method to refine the Q-matrix is to employ a suitable statistic that is calculated using response data. However, this approach is limited by its need to estimate all items in the Q-matrix even if only some are incorrect. To address this challenge, this study proposes an item fit statistic root mean square error approximation (RMSEA) for validating a Q-matrix with the deterministic inputs, noisy, “and” (DINA) model. Using a search algorithm, two simulation studies were performed to evaluate the effectiveness and efficiency of the proposed method at recovering Q-matrices. Results showed that using RMSEA can help define attributes in a Q-matrix. A comparison with the existing Delta method and residual sum of squares (RSS) method revealed that the proposed method had higher mean recovery rates and can be used to identify and correct Q-matrix misspecifications. When no error exists in the Q-matrix, the proposed method does not modify the correct Q-matrix.

A method of Q-matrix validation based symmetrised Kullback–Leibler divergence for the DINA model

2019 ◽  
Vol 12 (4) ◽  
pp. 537-547
Author(s):  
Jinxin Guo ◽  
Xin Xu ◽  
Jian Tao ◽  
Ningzhong Shi
Keyword(s):  
Dina Model ◽  
Leibler Divergence ◽  
Q Matrix
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