Biblio

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Book
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Wilson, M., & Bertenthal, M. W.. (2005). Systems for State Science Assessment. Washington, D.C.: National Academy Press.
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Bertenthal, M. W., Wilson, M., & ,. (2005). Systems for state science assessment. National Academies Press.
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Bertenthal, M. W., Wilson, M., & ,. (2005). Systems for state science assessment. National Academies Press.
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Book Chapter
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., & Adams, R. J.. (1996). Evaluating progress with alternative assessments: A model for Chapter 1. In M. B. Kane (Ed.), Implementing performance assessment: Promise, problems and challenges. Hillsdale, NJ: Erlbaum.
Kennedy, C., & Draney, K.. (2007). Interpreting and using multidimensional performance data to improve learning. In X. Liu (Ed.), Applications of Rasch Measurement to Science Education. Chicago: JAM Press.
Roberts, L., & Henke, R.. (1997). Mapping Middle School Students' Perceptions of the Relevance of Science. In M. Wilson & Engelhard, G. (Eds.), Objective Measurement: Theory into Practice. Volume 4) Norwood, NJ: Ablex.
Baek, S. - G., & Choi, I. - H.. (2009). The mastery learner judgment consistency rate of Rasch model-based standard setting method: Focused on the comparison with raw-score and Angoff methods. In Criterion-referenced testing: Practice analysis to score reporting using Rasch measurement. Chicago: JAM Press.
Wilson, M., Bejar, I., Scalise, K., Templin, J., Wiliam, D., & Torres Irribarra, D.. (2012). Perspectives on Methodological Issues. In Assessment and Teaching of 21st Century Skills (pp. 67–141). Springer.
Wilson, M., Bejar, I., Scalise, K., Templin, J., Wiliam, D., & Torres Irribarra, D.. (2012). Perspectives on Methodological Issues. In Assessment and Teaching of 21st Century Skills (pp. 67–141). Springer.
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Wilson, M. (2012). Responding To A Challenge That Learning Progressions Pose To Measurement Practice. In Learning progressions in science (pp. 317–343). Springer.
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Diakow, R., Torres Irribarra, D., & Wilson, M.. (2013). Some Comments on Representing Construct Levels in Psychometric Models. In New Developments in Quantitative Psychology (pp. 319–334). Springer New York.
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Wilson, M., & Draney, K.. (2013). A Strategy for the Assessment of Competencies in Higher Education. In Modeling and Measuring Competencies in Higher Education (pp. 61–80). Springer.
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Kennedy, C. (2005). Ten surprises about teaching. In M. Kallet & Morgan, A. (Eds.), The Art of College Teaching: 28 Takes. Knoxville, TN: University of Tennessee Press.
Lehrer, R., Kim, M., Ayers, E., & Wilson, M.. (In Press). Toward establishing a learning progression to support the development of statistical reasoning. In J. Confrey & Maloney, A. (Eds.), Learning over Time: Learning Trajectories in Mathematics Education. Charlotte, NC: Information Age Publishers.
Conference Paper
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.
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.

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