The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
The Generalized DINA Model Framework
Abstract
The G-DINA (generalized deterministic inputs, noisy “and” gate) model is a generalization of the DINA model with more relaxed assumptions. In its saturated form, the G-DINA model is equivalent to other general models for cognitive diagnosis based on alternative link functions. When appropriate constraints are applied, several commonly used cognitive diagnosis models (CDMs) can be shown to be special cases of the general models. In addition to model formulation, the G-DINA model as a general CDM framework includes a component for item-by-item model estimation based on design and weight matrices, and a component for item-by-item model comparison based on the Wald test. The paper illustrates the estimation and application of the G-DINA model as a framework using real and simulated data. It concludes by discussing several potential implications of and relevant issues concerning the proposed framework.
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Cites
Information Theory and an Extension of the Maximum Likelihood Principle
Akaike, H. (1998) · Springer Series in Statistics · doi:10.1007/978-1-4612-1694-0_15 · Cited by 17,781
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RULE SPACE: AN APPROACH FOR DEALING WITH MISCONCEPTIONS BASED ON ITEM RESPONSE THEORY
TATSUOKA, K. K. (1983) · Journal of Educational Measurement · doi:10.1111/j.1745-3984.1983.tb00212.x · Cited by 920
DINA Model and Parameter Estimation: A Didactic
de la Torre, J. (2009) · Journal of Educational and Behavioral Statistics · doi:10.3102/1076998607309474 · Cited by 671
Self-citationMeasurement of psychological disorders using cognitive diagnosis models.
Templin, J. L., & Henson, R. A. (2006) · Psychological Methods · doi:10.1037/1082-989x.11.3.287 · Cited by 651
The linear logistic test model as an instrument in educational research
Fischer, G. H. (1973) · Acta Psychologica · doi:10.1016/0001-6918(73)90003-6 · Cited by 645
Higher-Order Latent Trait Models for Cognitive Diagnosis
de la Torre, J., & Douglas, J. A. (2004) · Psychometrika · doi:10.1007/bf02295640 · Cited by 626
Self-citationDefining a Family of Cognitive Diagnosis Models Using Log-Linear Models with Latent Variables
Henson, R. A., Templin, J. L., & Willse, J. T. (2009) · Psychometrika · doi:10.1007/s11336-008-9089-5 · Cited by 559
Cited by
Item Response Theory: A Statistical Framework for Educational and Psychological Measurement
Chen, Y., Li, X., Liu, J., & Ying, Z. (2025) · Statistical Science · doi:10.1214/23-sts896 · Cited by 30
Survey of Computerized Adaptive Testing: A Machine Learning Perspective
Zhuang, Y., Liu, Q., Bi, H., Huang, Z., Huang, W., Li, J., Yu, J., Liu, Z., Hu, Z., Hong, Y., et al. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3672850 · Cited by 9
“In recent years, the rapid growth of deep learning techniques stimulates the development of deep learning-driven Measurement Models.”
Wang, Z., Zheng, X., & Zeng, C. (2025) · arXiv · doi:10.48550/arxiv.2510.22559 · Cited by 2
Metadata
- Authors
- de la Torre, J.
- Venue
- Psychometrika
- Publisher
- Cambridge University Press (CUP)
- Published
- 2011-04
- Volume
- 76
- Issue
- 2
- Pages
- 179-199
- ISSN
- 0033-3123 (ISSN-L)
- 1860-0980
- OpenAlex
- W1995884758
- Cited by
- 772
- References
- 28
- FWCI
- 20.38
- Type
- journal article
- Language
- en
- Open access
- closed
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