51²è¹Ý

Dr. Kamata is a Professor at 51²è¹Ý (Department of Education Policy & Leadership, Center on Research and Evaluation, Simmons School of Education & Human Development; Department of Psychology, Dedman College of Humanities and Sciences). Prior to joining 51²è¹Ý in 2013, Dr. Kamata was a Professor 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. Currently, Dr. Kamata's primary focus is on psychometric model development for oral reading fluency (ORF) assessment data, through three grant projects funded by IES: U.S. Department of Education. Previously, Dr. Kamata 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).  

Dr. Kamata received his doctoral degree in Measurement and Quantitative Methods from Michigan State University in 1998.

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Selected Publications

Nese, J. F. T., & Kamata, A. (2021). Evidence for automated scoring and shorter passages of CBM-R in early elementary school. School Psychology. 36 (1), 47-59. doi: 10.1037/spq0000415

Kara, Y., & Kamata, A. (2020). Multilevel item response model with heterogeneous within-cluster variances. The Journal of Experimental Education. doi: 10.1080/00220973.2020.1858015

Nese, J. F. A., & Kamata, A. (2020). Addressing the large standard error of traditional CBM-R: Estimating the conditional standard error of a model-based estimate of CBM-R. Assessment for Effective Intervention. Published Online First. https://doi.org/10.1177/1534508420937801

Liang, X., Kamata, A., & Li, J. (2020). Hierarchical Bayes approach to estimate the treatment effect for randomized controlled trials. Educational and Psychological Measurement, 80, 1090-1114. doi:10.1177/0013164420909885

Kara, Y., Kamata, A., Potgieter, C., & Nese, J. F. A. (2020). Estimating model-based oral reading fluency: Bayesian approach. Educational and Psychological Measurement, 80, 847-869. doi:10.1177/0013164419900208

Kamata, A., Kara, Y., Patarapichayatham, C., & Lan, P. (2018). Evaluation of analysis approaches for latent class analysis with auxiliary linear growth model. Frontiers in Psychology.

Nese, J. F. T., Kamata, A., & Tindal, J. (2017). A two-step sampling weight approach to growth mixture modeling for emergent and developing skills with distributional changes over time. Journal of School Psychology, 61, 55-74.

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.