Please enter verification code
Model Data Fit Comparison between DINA and G-DINA in Cognitive Diagnostic Models
Education Journal
Volume 2, Issue 6, November 2013, Pages: 256-262
Received: Dec. 3, 2013; Published: Dec. 20, 2013
Views 3466      Downloads 245
T. Oguz Basokcu, Department of Assessment and Evaluation in Education, Ege University, İzmir, Turkey
Tuncay Ogretmen, Department of Assessment and Evaluation in Education, Ege University, İzmir, Turkey
Hulya Kelecioglu, Department of Assessment and Evaluation in Education, Hacettepe University, Ankara, Turkey
Article Tools
Follow on us
In this study, item and model data fit indices, calculated by DINA and G-DINA Models using the same sample and Q matrix, are analyzed. Fit indices for these two models from Cognitive Diagnostic Models are analyzed using 2LL, AIC and BIC statistics. Item fit indices are analyzed using residual correlations and probabilities. Analysis results showed G-DINA model had better fit results than DINA model. DINA model could give rather better results to estimate student profile in tests where higher level and progressive behaviors are used together. On the other hand, G-DINA model weights required attributes for an item when estimating student profile. Therefore in items requiring more than one attributes, contributions of attributes to probability that a student answers the item correctly are not equal. This provides an important advantage to testers to evaluate multiple choice items in assessing complex and prerequisite forming patterns.
Cognitive Diagnostic Models, DINA Model, G-DINA Model, Model Fit Indices
To cite this article
T. Oguz Basokcu, Tuncay Ogretmen, Hulya Kelecioglu, Model Data Fit Comparison between DINA and G-DINA in Cognitive Diagnostic Models, Education Journal. Vol. 2, No. 6, 2013, pp. 256-262. doi: 10.11648/
Akıncı, D. E. (2007). Information Criterion of Structural Equation Models, MimarSinanGüzelSanatlarUniversity, Institute of Science, unpublished Phdtheses’.
Bandalos, D.L. (1993). Factors influencing cross-validation of confirmatory factor analysis models, Multivariate Behavioral Research, 28(3), 351-374
Başokçu T. O. (2012). The Analyzing Item Discrimination Index Estimated by Using DINA Model Parameters. Education and Science, 37, 310-321
Brown, K.W. (1999). Research Methods in Human Development: Mayfield Pub.
Cavanaugh, J.E. (2009). The Bayesian Information Criterion, Model Selection lecture notes, The University of Iowa, Department of Statistics and Actuarial Science
Cheng, Y. (2010). Improving Cognitive Diagnostic Computerized Adaptive Testing by Balancing Attribute Coverage: The Modified Maximum Global Discrimination Index Method. Educational and Psychological Measurement, 70(6), 902,-913
de la Torre, J. & Douglas, J. (2008). Model Evaluation and Multiple Strategies in Cognitive Diagnosis: An Analysis of Fraction Subtraction Data. Psychometrika, 73(3), 595-624.
de la Torre, J. (2008a). The generalized DINA model framework. Unpublished manuscript. State University of New Jersey.
de la Torre, J. (2008b). An empirically-based method of Q-matrix validation for the DINA model: Development and applications. Journal of Educational Measurement, 45, 343–362.
de la Torre, J. (2009a). A cognitive diagnosis model for cognitively-based multiple-choice options. Applied Psychological Measurement, 33, 163–183
de la Torre, J. (2009b). DINA Model and Parameter Estimation: A Didactic. Journal of Educational and Behavioral Statistics March, 34(1), 115,-130
de la Torre, J. .& Lee, Y.S. (2010). A note on Invariance of the DINA Model Parameters,Journal of Educational Measurement, 47(1), 115-127
de la Torre, J. , Hong, Y. & Deng, W. (2010). Factors Affecting the Item Parameter Estimation and Classification Accuracy of the DINA Model, Journal of Educational Measurement, 47(2), 227-249
DeCarlo, L. T. (2011). On the analysis of fraction subtraction data: The DINA model, classification, latent class sizes, and the Q-matrix. Applied Psychological Measurement, 35, 8-26
DeCarlo, L. T. (2012). Recognizing uncertainty in the Q-matrix via a Bayesian extension of the DINA model.Applied Psychological Measurement, 36, 447-468.
diBello, L., Roussos, L. A., & Stout, W. (2007). Review of cognitively diagnostic assessment and a summary of psychometric models. In C. V. Rao& S. Sinharay (Eds.), Handbook of Statistics (Vol. 26, Psychometrics) (pp. 979-1027). Amsterdam, Netherlands: Elsevier.
Embretson, S. (1984). A general latent trait model for response processes. Psychometrika, 49, 175–186.
Embretson, S. E. (1998). A cognitive design system approach to generating valid tests: Application to abstract reasoning. Psychological Methods, 3, 380-396
Haertel, E.H. (1989). Using restricted latent class models to map the skill structure of achievement items. Journal of Educational Measurement, 26, 333-352.
Henson, R.A., Roussos, L., Templin, J.L. (2004) Cognitive diagnostic "fit" indices. Unpublished ETS Project Report, Princeton, NJ.
Leighton, J. P.&Gierl M. J. (2007). Why Cognitive Diagnostic Assessment? Leighton, J. P. Gierl M. J. (Eds). Cognitive Diagnostic Assessment for Education. New York: Cambridge University Press
Tatsuoka, K. (1985). A probabilistic model for diagnosing misconceptions in the pattern classification approach. Journal of Educational Statistics, 12, 55–73.
Tatsuoka, K. (1995). Architecture of knowledge structures and cognitive diagnosis: A statistical pattern recognition and classification approach. In P.D. Nichols, S. F.
Wenmin, Z. (2006). Detecting Differential Item Functioning Using the DINA Model. The University of North Carolina at Greensboro. Unpublished Phd Thesis. Greensboro
Whitley, B. E., Kite, M. E., & Adams, H. L. (2012). Principles of Research in Behavioral Science: Routledge.
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
Tel: (001)347-983-5186