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Hoskens, M., Wilson, M., & Stavisky, H.. (1997, June). Accounting for rater effects in large scale testing using IRT. Presented at the European meeting of the Psychometric Society.
Kennedy, C., Burmester, K., Harrell, S. Veeragouda, Timms, M., Bernbaum, D., Scalise, K., & Wilson, M.. (2007). Adaptive Technology for e-Learning: Principles and Case Studies of an Emerging Field. Journal of the American Society for Information Science and Technology, 58(14). doi:10.1002
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Xie, Y. (2002). An Application of a Special Two-Class Item Response Model Using Markov Chain Monte Carlo Method. Position Paper; Policy, Organization, Measurement, & Evaluation; Graduate School of Education; University of California, Berkeley.
Draney, K., & Wilson, M.. (2007). Application of the Saltus model to stagelike data: Some applications and current developments. In Multivariate and mixture distribution Rasch models (pp. 119–130). Springer.
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Wilson, M. (1997, Jannuary). Assessment and evaluation together in the service of instruction and learning. Presented at the National Science Foundation Status of Middle School Science Conference. Washington, DC.
Scalise, K., Bernbaum, D. J., Timms, M., Veeragoudar Harrell, S., Burmester, K. O. 'Rourke, Kennedy, C., & Wilson, M.. (2006, April). Assessment for e-Learning: Case studies of an emerging field. Presented at the International Objective Measurement Workshop, Berkeley, CA.
Wilson, M. (2009). Assessment from the ground up. Phi Delta Kappan, 91, 68–71.
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Briggs, D. C. (2004). Causal Inference and the Heckman Model. Journal of Educational and Behavioral Statistics, 29, 397-420. doi:10.3102/10769986029004397
Wilson, M. (2013). Challenges and Opportunities of Learning Progressions for the Psychometric Community. Presented at the annual meeting of the American Educational Research Association, San Francisco.
Wilson, M. (2003). On Choosing a Model for Measuring. Methods of Psychological Research.