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The Generalized DINA Model Framework

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An Erratum to this article was published on 05 May 2011

Abstract

The G-DINA (generalized deterministic inputs, noisyandgate) 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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References

  • Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In Petrov, B.N., & Csaki, F. (Eds.) Proceedings of the second international symposium on information theory (pp. 267–281). Budapest: Akad. Kiado.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • de la Torre, J. (2009a). A cognitive diagnosis model for cognitively-based multiple-choice options. Applied Psychological Measurement, 33, 163–183.

    Article  Google Scholar 

  • de la Torre, J. (2009b). DINA model and parameter estimation: a didactic. Journal of Educational and Behavioral Statistics, 34, 115–130.

    Article  Google Scholar 

  • de la Torre, J., & Douglas, J. (2004). A higher-order latent trait model for cognitive diagnosis. Psychometrika, 69, 333–353.

    Article  Google Scholar 

  • de la Torre, J., & Douglas, J. (2008). Model evaluation and multiple strategies in cognitive diagnosis: an analysis of fraction subtraction data. Psychometrika, 73, 595–624.

    Article  Google Scholar 

  • Doornik, J.A. (2003). Object-oriented matrix programming using Ox (version 3.1) [Computer software]. London: Timberlake Consultants Press.

    Google Scholar 

  • Fischer, G.H. (1973). The linear logistic test model as an instrument in educational research. Acta Psychologica, 37, 359–374.

    Article  Google Scholar 

  • Fischer, G.H. (1997). Unidimensional linear logistic Rasch models. In van der Linden, W., & Hambleton, R.K. (Eds.), Handbook of modern item response theory (pp. 225–244). New York: Springer.

    Google Scholar 

  • Hagenaars, J.A. (1990). Categorical longitudinal data: loglinear panel, trend, and cohort analysis. Thousand Oaks: Sage.

    Google Scholar 

  • Hagenaars, J.A. (1993). Loglinear models with latent variables. Thousand Oaks: Sage.

    Google Scholar 

  • Hartz, S.M. (2002). A Bayesian framework for the Unified Model for assessing cognitive abilities: blending theory with practicality. Unpublished doctoral dissertation.

  • Henson, R., Templin, J., & Willse, J. (2009). Defining a family of cognitive diagnosis models using log-linear models with latent variables. Psychometrika, 74, 191–210.

    Article  Google Scholar 

  • Jaeger, J., Tatsuoka, C., & Berns, S. (2003). Innovative methods for extracting valid cognitive deficit profiles from NP test data in schizophrenia. Schizophrenia Research, 60, 140–140.

    Article  Google Scholar 

  • Junker, B.W., & Sijtsma, K. (2001). Cognitive assessment models with few assumptions, and connections with non-parametric item response theory. Applied Psychological Measurement, 25, 258–272.

    Article  Google Scholar 

  • Lehmann, E.L., & Casella, G. (1998). Theory of point estimation (2nd ed.). New York: Springer.

    Google Scholar 

  • Leighton, J.P., Gierl, M.J., & Hunka, S. (2004). The attribute hierarchy method for cognitive assessment: a variation on Tatsuoka’s rule-space approach. Journal of Educational Measurement, 41, 205–236.

    Article  Google Scholar 

  • Maris, E. (1999). Estimating multiple classification latent class models. Psychometrika, 64, 187–212.

    Article  Google Scholar 

  • Millon, T., Millon, C., Davis, R., & Grossman, S. (2006). MCMI-III manual (3rd ed.). Minneapolis: Pearson Assessments.

    Google Scholar 

  • Rossi, G., Elklit, A., & Simonsen, E. (2010). Empirical evidence for a four factor framework of personality disorder organization: multigroup confirmatory factor analyses of the Millon Clinical Multiaxial Inventory—III personality disorder scales across Belgian and Danish data samples. Journal of Personality Disorders, 24, 128–150.

    Article  PubMed  Google Scholar 

  • Rossi, G., Sloore, H., & Derksen, J. (2008). The adaptation of the MCMI-III in two non-English-speaking countries: state of the art of the Dutch language version. In Millon, T., & Bloom, C. (Eds.), The Millon inventories: a practitioner’s guide to personalized clinical assessment (2nd ed., pp. 369–386). New York: Guilford.

    Google Scholar 

  • Rossi, G., van der Ark, L.A., & Sloore, H. (2007). Factor analysis of the Dutch language version of the MCMI-III. Journal of Personality Assessment, 88, 144–157.

    PubMed  Google Scholar 

  • Roussos, L.A., DiBello, L.V., Stout, W., Hartz, S.M., Henson, R.A., & Templin, J.L. (2007). The fusion model skills diagnosis system. In Leighton, J.P., & Gierl, M.J. (Eds.), Cognitively diagnostic assessment for education: theory and applications (pp. 275–318). Cambridge: Cambridge University Press.

    Chapter  Google Scholar 

  • Stout, W. (2007). Skills diagnosis using IRT-Based continuous latent trait models. Journal of Educational Measurement, 44, 313–324.

    Article  Google Scholar 

  • Tatsuoka, C. (2002). Data-analytic methods for latent partially ordered classification models. Journal of the Royal Statistical Society, Series C (Applied Statistics), 51, 337–350.

    Article  Google Scholar 

  • Tatsuoka, C. (2005). Corrigendum: data analytic methods for latent partially ordered classification models. Journal of the Royal Statistical Society, Series C (Applied Statistics), 54, 465–467.

    Article  Google Scholar 

  • Tatsuoka, K. (1983). Rule space: an approach for dealing with misconceptions based on item response theory. Journal of Educational Measurement, 20, 345–354.

    Article  Google Scholar 

  • Tatsuoka, K. (1990). Toward an integration of item-response theory and cognitive error diagnosis. In Frederiksen, N., Glaser, R., Lesgold, A., & Safto, M. (Eds.), Monitoring skills and knowledge acquisition (pp. 453–488). Hillsdale: Erlbaum.

    Google Scholar 

  • Templin, J., & Henson, R. (2006). Measurement of psychological disorders using cognitive diagnosis models. Psychological Methods, 11, 287–305.

    Article  PubMed  Google Scholar 

  • von Davier, M. (2005). A general diagnostic model applied to language testing data (ETS Research Report RR-05-16). Princeton: Educational Testing Service.

  • von Davier, M. (2009). Some notes on the reinvention of latent structure models as diagnostic classification models. Measurement, 7, 67–74.

    Google Scholar 

  • von Davier, M., & Yamamoto, K. (2004, October). A class of models for cognitive diagnosis. Paper presented at the 4th Spearman Conference, Philadelphia, PA.

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Correspondence to Jimmy de la Torre.

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An erratum to this article can be found at http://dx.doi.org/10.1007/s11336-011-9214-8

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de la Torre, J. The Generalized DINA Model Framework. Psychometrika 76, 179–199 (2011). https://doi.org/10.1007/s11336-011-9207-7

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  • DOI: https://doi.org/10.1007/s11336-011-9207-7

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