MULTILEVEL MIXTURE ITEM RESPONSE THEORY MODEL โมเดลทฤษฎีการตอบสนองข้อสอบแบบผสมพหุระดับ
Article Sidebar
Main Article Content
Abstract
Item response theory (IRT) model has an assumption that test takers must come from a single homogeneous population to imply the invariance of measurement. However, this assumption may violate the nature of the data, as in general, test takers in the same population may have different characteristics. In addition, the IRT model still neglects the multilevel structure of the data, which may cause less accurate results due to aggregation. For this reason, IRT model is integrated with latent class model and multilevel model, multilevel mixture IRT model, to relax the limitations.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The owner of the article does not copy or violate any of its copyright. If any copyright infringement occurs or prosecution, in any case, the Editorial Board is not involved in all the rights to the owner of the article to be performed.
References
Cho, S. J. (2007). A multilevel mixture IRT model for DIF analysis (Unpublished doctoral dissertation). Athens, GA: University of Georgia.
Cho, S. J., & Cohen, A. S. (2010). A multilevel mixture model with applications to DIF. Journal of Educational and Behavioral Statistics, 35(3), 336-370.
Cohen, A., Wollack, J., Bolt, D., & Mroch, A. (2002). A mixture Rasch model analysis of test speededness. the annual meeting of the American Educational Research Association. New Orleans: LA.
Fox, J. P., & Glas, C. (2001). Bayesian estimation of a multilevel IRT model using Gibbs sampling. Psychometrika, 66(2), 271-288.
Goudie, R. J., Turner, R. M., De Angelis, D., & Thomas, A. (2017). Massively parallel MCMC for Bayesian hierarchical models (arXiv Preprint No.1704.03216). Retrieved from https://arxiv.org/abs/1704.03216
Hox, J. J., & Maas, C. J. (2001). The accuracy of multilevel structural equation modeling with pseudobalanced groups and small samples. Structural Equation Modeling, 8(2), 157-174.
Kamata, A. (2001). Item analysis by the hierarchical generalized linear model. Journal of Educational Measurement, 38(1), 79-93.
Kanjanawasee, S. (2012). Modern test theories (4th ed.). Bangkok: Chulalongkorn University Prss. [in Thai]
Lee, W. Y., Cho, S. J., & Sterba, S. K. (2018). Ignoring a multilevel structure in mixture item response models: impact on parameter recovery and model selection. Applied Psychological Measurement, 42(2), 136-154.
Lord, F. M., & Novick, M. (1968). Statistical theories of mental test scores. Reading, MA: Addison-Wesley.
Lubke, G., & Muthén, B. O. (2005). Investigating population heterogeneity with factor mixture models. Psychological Methods, 10(1), 21-39.
Maier, K. S. (2001). A Rasch hierarchical measurement model. Journal of Educational and Behavioral Statistics, 26(3), 307-330.
Muthén, L., & Muthén, B. O. (2015). Mplus user's guide (7th ed.). Los Angeles, CA: Muthén & Muthén.
Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods. Thousand Oaks, CA: SAGE.
Reckase, M. D. (2009). Multidimensional item response theory. New York, NY: Springer.
Rost, J. (1990). Rasch models in latent classes: An integration of two approaches to item analysis. Applied Psychological Measurement, 14(3), 271-282.
Sen, S., Cohen, A., & Kim, S. (2019). Model selection for multilevel mixture Rasch models. Applied Psychological Measurement, 43(4), 272-289.
Tay, L., Diener, E., Drasgow, F., & Vermunt, J. (2011). Multilevel mixed-measurement IRT analysis: An explication and application to self-reported emotions across the world. Organizational Research Methods, 14(1), 177-207.
Vermunt, J. (2003). Multilevel latent class models. Sociological Methodology, 33(1), 213-239.
Wongnam, P. (2012). Multilevel regression analysis with HLM 6. Journal of Education, 23(3), 27-42. [in Thai]