Ph.D., Michigan State University
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Dr. Kamata is a Professor at Southern Methodist University since August 2013 (Department of Education Policy & Leadership, and Center on Research and Evaluation, Simmons School of Education & Human Development; Department of Psychology, Dedman College of Humanities and Sciences). Prior to joining SMU, Dr. Kamata was a faculty member at the University of Oregon and Florida State University.
Dr. Kamata's primary research interest is psychometrics and educational and psychological measurement, focusing on implementation of item-level test data analysis methodology through various modeling framework, including item response theory, multilevel modeling, and structural equation modeling. He did pioneering work on multilevel item response theory modeling, where item response data from individuals are nested within group units, such as schools. This line of work is represented by his 2001 publication in Journal of Educational Measurement, a special issue on multilevel measurement modeling in Journal of Applied Measurement in 2005, and several book chapters on the topic, including a recent chapter in the Handbook of Advanced Multilevel Analysis (2011). Other recent interests include developing effect size measures for testlet modeling, developing reliability measures of growth trajectory for longitudinal data modeling, and Bayesian inference for complex psychometric models.
Dr. Kamata received his doctoral degree in Measurement and Quantitative Methods from Michigan State University in 1998.
Kamata, A., Nese, J. F. T., Patarapichayatham, C., & Lai, C. F. (2013). Modeling nonlinear growth the three data points: Illustration with benchmarking data. Assessment for Effective Intervention, 32, 105-116.
Jiao, H., Kamata, A., Wang, S., & Jin, Y. (2012). A multilevel testlet model for dual local dependence. Journal of Educational Measurement, 49, 82-100.
Patarapichayatham, C., Kamata, A., & Kanjanawasee, S. (2012). Evaluation of model selection strategies for cross-level two-way differential item functioning analysis. Educational and Psychological Measurement, 72, 44-51
Kamata, A. & Vaughn, B. K. (2011). Multilevel Item Response Theory Modeling. In J. Hox & J. K. Roberts (Eds.). Handbook of Advanced Multilevel Analysis (pp.41-57). New York: Routledge.
Fukuhara, H. & Kamata, A. (2011). A bi-factor multidimensional item response theory model for differential item functioning analysis on testlet-based items. Applied Psychological Measurement, 35, 604-622.
Kamata, A. & Bauer, D. J. (2008). A note on the relationship between factor analytic and item response theory models. Structural Equation Modeling. 15, 136-153.
Kamata, A., Bauer, D. J., & Miyazaki, Y. (2008). Multilevel Measurement Model. In A. A. O’Connell & D. B. McCoach (Eds.). Multilevel Analysis of Educational Data. (pp.345-388). Charlotte, NC: Information Age Publishing.
Bilir, M. K., Binici, S., & Kamata, A. (2008). Growth mixture modeling (GMM): Application to reading achievement data from a large scale assessment. Measurement and Evaluation in Counseling and Development, 41, 104-117.
Chaimongkol, S., Huffer, W. F., & Kamata A. (2006). A Bayesian approach for fitting a random effect differential item functioning across group units. Thailand Statistician, 4, 27-41.
Kamata, A., & Tate, R. L. (2005). The performance of a method for the long-term equating of mixed format assessment. Journal of Educational Measurement. 42, 193-213.
Hayashi, K. & Kamata, A. (2005). A note on the stability of Alpha Coefficient with standardized variables. Psychometrika, 70, 579-586.
Jiao, H, Wang, S., & Kamata, A. (2005). Exploring the possibility of using hierarchical generalized linear model to model local item dependence. Journal of Applied Measurement, 6, 311-321.
Chu, K. L., & Kamata, A. (2005). Test equating with the presence of DIF. Journal of Applied Measurement, 6, 342-354.
Kahraman, N., & Kamata, A. (2004). Increasing the precision of subscale scores by using out-of-scale information. Applied Psychological Measurement. 28, 407-426.
Kamata, A. (2001). Item analysis by the hierarchical generalized linear model. Journal of Educational Measurement, 38, 79-93.