The Impact of Q-matrix Misspecification and Model Misuse on Classification Accuracy in the Generalized DINA Model
Abstract
This simulation study explored the impact of Q-matrix misspecification and model misuse on examinees’ classification accuracy within the generalized deterministic input, noisy “and” gate (G-DINA) model framework under the different conditions. The data was generated by saturated G-DINA model. Along with the generating model, two reduced models were used to fit the data: the additive CDM (A-CDM) and DINA model. The manipulated conditions included number of respondents, attribute correlations and test length. Two types of classification accuracy were examined: the overall classification accuracy and the class-specific classification accuracy. Results showed that the Q-matrix misspecification influenced classification accuracy more ominously than model misuse. The proportion of examinees classified correctly for each latent class was related to the types of Q-matrix misspecification. More test items had greater positive impact on classification accuracy than more respondents taking the test.
Keywords
References
- Henson, R., Roussos, L., Douglas, J., & He, X. (2008). Cognitive diagnostic attribute-level discrimination indices. Applied Psychological Measurement, 32(4), 275-288.
Details
Primary Language
English
Subjects
-
Journal Section
Research Article
Authors
Miao Gao
Nanjing Normal University
0000-0001-6842-5468
China
M. David Mıller
This is me
University of Florida
0000-0002-3506-1167
United States
Ren Lıu
This is me
University of Florida
0000-0002-6708-4996
United States
Publication Date
December 28, 2017
Submission Date
August 4, 2017
Acceptance Date
October 16, 2017
Published in Issue
Year 2017 Volume: 8 Number: 4
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