The Impact of Model Misspecification on Parameter Estimation and Item-Fit Assessment in Log-Linear Diagnostic Classification Models

Journal of Educational Measurement

Volumn 49, Issue 1, 2012, Pages 59-81

  Kunina Habenicht, Olga ^a   Rupp, André A ^b   Wilhelm, Oliver ^c  

^a GOETHE UNIVERSITY   (Germany)

^b UNIVERSITY OF MARYLAND   (United States)

^c UNIVERSITY OF ULM   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84859119870     PISSN: 00220655     EISSN: 17453984     Source Type: Journal    
DOI: 10.1111/j.1745-3984.2011.00160.x     Document Type: Article

References (27)

1. Akaike, H. (1974). A new look at the statistical identification model. IEEE Transactions on Automated Control, 19, 716-723. doi:10.1109/TAC.1974.1100705
2. Choi, H.-J., Templin, J. L., Cohen, A. S., & Atwood, C. H. (April 2010). The impact of model misspecification on estimation accuracy in diagnostic classification models. Paper presented at the meeting of the National Council on Measurement in Education (NCME), Denver, CO.
3. de la Torre, J. (2008). An empirically-based method of Q-matrix validation for the DINA model: Development and applications. Journal of Educational Measurement, 45, 343-362. doi:10.1111/j.1745-3984.2008.00069.x
4. de la Torre, J. (2011). The generalized DINA model framework. Psychometrika, 76(2), 179-199. doi:10.1007/s11336-011-9207-7
5. 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, The Netherlands : Elsevier.
6. Hartz, S. M. (2002). A Bayesian framework for the unified model for assessing cognitive abilities: Blending theory with practicality. Unpublished doctoral dissertation, University of Illinois at Urbana-Champaign, Urbana-Champaign, IL.
7. Henson, R. A., & Templin, J. L. (2009). Implications of Q-matrix misspecification in cognitive diagnosis. Unpublished paper.
8. Henson, R. A., Templin, J., & Willse, J. (2009). Defining a family of cognitive diagnosis models using log-linear models with latent variables. Psychometrika, 74(2), 191-210. doi:10.1007/s11336-008-9089-5
9. Henson, R. A., Roussos, L., Douglas, J., & He, X. (2008). Cognitive diagnostic attribute level discrimination indices. Applied Psychological Measurement, 32, 275-288. doi:10.1177/0146621607302478
10. Kunina-Habenicht, O., Rupp, A. A., & Wilhelm, O. (2009). A practical illustration of multidimensional diagnostic skills profiling: Comparing results from confirmatory factor analysis and diagnostic classification models. Studies in Educational Evaluation, 35(2), 64-70. doi:10.1016/j.stueduc.2009.10.003
11. Muthén, L. K., & Muthén, B. O. (19982008). Mplus (Version 5.21) [Computer software]. Los Angeles, CA: Muthén & Muthén.
12. Nugent, R., Dean, N., Ayers, B., & Junker, B. (July 2009). Clustering in cognitive diagnosis: Some recent work and open questions. Paper presented at the Cognitive Diagnosis Models Working Group at the SAMSI Summer Program in Psychometrics, Durham, NC.
13. Rupp, A. A., diCerbo, K., Levy, R., Benson, M., Sweet, S., Crawford Behrens, J. (In press). The interplay of theory and data: Evidence identification and aggregation for product and process data within a digital learning environment for network engineering proficiency. Journal of Educational Data Mining.
14. Rupp, A. A., & Templin, J. (2008a). The effects of Q-Matrix misspecification on parameter estimates and classification accuracy in the DINA model. Educational and Psychological Measurement, 68, 78-96. doi:10.1177/0013164407301545
15. Rupp, A. A., & Templin, J. (2008b). Unique characteristics of diagnostic classification models: A comprehensive review of the current state-of-the-art. Measurement: Interdisciplinary Research and Perspectives, 6, 219-262. doi:10.1080/15366360802490866
16. Rupp, A. A., Templin, J., & Henson, R. (2010). Diagnostic measurement: Theory, methods, and applications. New York, NY : Guilford Press.
17. Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461-464. doi:10.1214/aos/1176344136
18. Sinharay, S., Puhan, G., & Haberman, S. J. (2011). An NCME instructional module on subscores. Educational Measurement: Issues and Practice, 30(3), 29-40. doi:10.1111/j.1745-3992.2011.00208.x
19. Tatsuoka, K. K. (1990). Toward an integration of item-response theory and cognitive error diagnosis. In N. Frederiksen, R. Glaser, A. Lesgold, & M. Safto (Eds.), Monitoring skills and knowledge acquisition (pp. 453-488). Hillsdale, NJ : Erlbaum.
20. Tatsuoka, K. K. (2009). Cognitive assessment: An introduction to the rule-space method. Florence, KY : Routledge.
21. Templin, J. L., & Henson, R. (2006). Measurement of psychological disorders using cognitive diagnosis models. Psychological Methods, 11, 287-305. doi: 10.1037/1082-989X.11.3.287
22. Templin, J., Henson, R., Rupp, A., Jang, E., & Ahmed, M. (2009). Diagnostic models for nominal response data. Unpublished paper.
23. Templin, J., & Rupp, A. A. (In press). On the origins of a statistical species: The evolution of diagnostic classification models within the psychometric taxonomy. In G. Macready & G. Hancock (Eds.), A Festschrift in Honor of C. Mitchell Dayton. Charlotte, NC : Information Age.
24. von Davier, M. (2006). Multidimensional latent trait modelling (MDLTM) [Software program]. Princeton, NJ: Educational Testing Service.
25. von Davier, M. (2009). Some notes on the reinvention of latent structure models as diagnostic classification models. Measurement: Interdisciplinary Research & Perspective, 7(1), 67-74. doi: 10.1080/15366360902799851
26. von Davier, M. (2010). Hierarchical mixtures of diagnostic models. Psychological Test and Assessment Modeling, 52(1), 8-28.
27. Xu, X., & von Davier, M. (2006). Cognitive diagnosis of NAEP proficiency data. Research Report RR-06-08. Princeton, NJ: Educational Testing Service.