Biblio
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. (1994, April). What technical issues might be major concerns in implementing new assessments and new technologies. Presented at the National Council for Measurement in Education annual meeting. New Orleans, LA.
. (1996, April). What have we learned from developing an embedded assessment system?. Presented at the annual meeting of the American Educational Research Association, New York, NY.
. (2010). Validation of the International Classification of Functioning Disability and Health framework using multidimensional item response modeling. Disability & Rehabilitation, 32, 1397–1405.
(214.15 KB). (2011). Validating a learning progression in mathematical functions for college readiness. Mathematical Thinking and Learning, 13, 259–291.
(1.22 MB). (2013). Using the concept of a measurement system to characterize measurement models used in psychometrics. Measurement, 46, 3766–3774.
(1.23 MB). (2007). Using progress variables to map intellectual development. Berkeley, CA: Berkeley Evaluation & Assessment Research Center.
(402.92 KB). (2006). Using Progress Variables to Interpret Student Achievement and Progress. University of California, Berkeley.
(551.59 KB). (2005, April). Using progress variables and embedded assessment to improve teaching and learning. Presented at the Annual Meeting of the American Educational Research Association, Montreal, Canada.
. (2000). Using criterion-referenced maps to produce meaningful evaluation measures: Evaluating middle school science teacher change in assessment, collegial and instructional practices. In , Objective Measurement: Theory into Practice (Volume 5). Norwood, NJ: Ablex.
. (2010). Unfair treatment? The case of Freedle, the SAT, and the standardization approach to differential item functioning. Harvard Educational Review, 80, 106–134.
(257.42 KB). (2004). Towards coherence between classroom assessment and accountability. 103rd Yearbook of the National Society for the Study of Education, Part II. Chicago: University of Chicago Press.
. (2013). Toward establishing a learning progression to support the development of statistical reasoning. Learning over time: learning trajectories in mathematics education. Charlotte: Information Age Publishers.
(13.26 MB). (In Press). Toward establishing a learning progression to support the development of statistical reasoning. In , Learning over Time: Learning Trajectories in Mathematics Education. Charlotte, NC: Information Age Publishers.
. (2005). Theoretical issues in the Desired Results Developmental Profile. Presented at the
Annual meeting of the American Educational Research Association, Montreal, Canada.
.. (2005). Systems for state science assessment. National Academies Press.
(473.17 KB). (2005). Systems for State Science Assessment. Washington, D.C.: National Academy Press.
(473.17 KB). (2005). Systems for state science assessment. National Academies Press.
(473.17 KB). (2008). A Study of Confidence and Accuracy Using the Rasch Modeling Procedures. ETS Research Report Series, 2008, i–25.
(334.27 KB). (2008). A Study of Confidence and Accuracy Using the Rasch Modeling Procedures. ETS Research Report Series, 2008, i–25.
(334.27 KB). (2013). A Strategy for the Assessment of Competencies in Higher Education. In Modeling and Measuring Competencies in Higher Education (pp. 61–80). Springer.
(1.46 MB). (2012, April 12). Special Education Learning Progressions in Mathematics: An Investigation Using the BEAR Assessment System. Presented at the International Objective Measurement Workshop, Vancouver, BC.
. (2009). Sources of self-efficacy belief: development and validation of two scales. Journal of applied measurement, 11, 24–37.
(480.15 KB). (2010). Sources of self-efficacy belief: development and validation of two scales. Journal of applied measurement, 11, 24.
. (2004). Some Links Between Large-Scale and Classroom Assessments: The Case of the BEAR Assessment System. Yearbook of the National Society for the Study of Education, 103, 132–154.
. (2013). Some Comments on Representing Construct Levels in Psychometric Models. In New Developments in Quantitative Psychology (pp. 319–334). Springer New York.
(981.89 KB)