After specializing in psychometric methods and bioethics at the University of Minnesota, Remo taught 3020 and 4050 in the School of Psychology at UQ for a number of years. A stint in the School of Pharmacy at UQ, doing systematic reviews of medicine prescribing and some pharmacoepidemiology, followed. Now Remo has gone back to his roots, measuring health behaviors and community well being in the Healthy Communities Research Center at UQ. Remo′s research focuses on health literacy, including the effect of health literacy on health behaviors as well as predictors and components of health literacy. Of particular interest are the issues of health literacy motivation and responsibility. Remo maintains an honorary appointment in the School of Psychology and continues to supervise students in the school. Dr. Nering joined Measured Progress as a psychometrician in 1999. In this capacity as assistant vice president of psychometrics and research, he is responsible for all psychometric services provided by Measured Progress. He also oversees all research activities and both the summer internship program and the visiting scholar program.Prior to joining Measured Progress, Dr. Nering was a psychometrician for ACT, Inc., where he was extensively involved in research on the computerized adaptive test version of the ACT assessment. He also provided psychometric support for the Council of Chief State School Officers (CCSSO) science test and conducted research for the development division in the Support, Technological Applications, and Research department. In addition to his experience in psychometrics, Dr. Nering has taught at the college level, served as an item writer for a variety of assessment instruments, tested and developed software packages, and published and presented numerous papers on measurement and testing.Dr. Nering′s research interests include person fit, item response theory, computer-based testing, and equating. He has presented and published numerous articles on a wide range of psychometric topics, and he is actively involved in the research community in various capacities.Dr. Nering is a member of the National Council of Measurement in Education, American Educational Research Association, American Psychological Association, and the Psychometric Society. For the AERA 2005 conference he was a program chair for Division D – Measurement and Research Methodology. He has also served as treasurer of the Psychometric Society. In addition, Dr. Nering has served as reviewer for several peer journals, including the Journal of Educational Measurement, Applied Psychological Measurement, Psychometrika, and the Journal of Experimental Education.Dr. Nering has a Ph.D. in psychology with a specialization in psychometric methods from the University of Minnesota and a Bachelor′s degree in psychology from Kent State University, Kent, OH.

• Series Editor′s IntroductionAcknowledgments1. IntroductionMeasurement TheoryItem Response TheoryApplying the IRT ModelReasons for Using Polytomous IRT ModelsPolytomous IRT ModelsTwo Types of ProbabilitiesTwo Types of Polytomous ModelsCategory BoundariesItem Category Response Functions2. Nominal Response ModelThe Mathematical ModelInformationRelationship to Other IRT ModelsVariationsA Practical Example3. Polytomous Rasch ModelsPartial Credit ModelCategory StepsThe Mathematical ModelInformationRelationship to Other IRT ModelsVariationsPCM SummaryRating Scale ModelThe Mathematical ModelModel ParametersSufficient Statistics and Other ConsiderationsInformationExpected Values and Response FunctionsResponse Functions and InformationRelationship to Other IRT ModelsPCM Scoring Function Formulation and the NRMVariationsGeneralized Partial Credit ModelDiscrimination and Polytomous Rasch ModelsSummary of Polytomous Rasch ModelsThree Practical Examples4. Samejima ModelsFrameworkFrom Response Process to Specific ModelThe Homogeneous Case: Graded Response ModelsThe Mathematical ModelInformationInformation for Polytomous ModelsRelationship to Other IRT ModelsFrom Homogeneous Class to Heterogeneous Class and BackA Common MisconceptionVariationsSummary of Samejima ModelsPotential Weaknesses of the Cumulative Boundary ApproachPossible Strengths of the Cumulative Boundary ApproachA Practical Example5. Model SelectionGeneral CriteriaMathematical ApproachesFit Statistic ProblemsAn ExampleDifferences in Modeled OutcomeConclusionAcronyms and GlossaryNotesReferencesIndexAbout the Authors

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