Polytomous Item Response Theory Models

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 Introduction Acknowledgments 1. Introduction Measurement Theory Item Response Theory Applying the IRT Model Reasons for Using Polytomous IRT Models Polytomous IRT Models Two Types of Probabilities Two Types of Polytomous Models Category Boundaries Item Category Response Functions 2. Nominal Response Model The Mathematical Model Information Relationship to Other IRT Models Variations A Practical Example 3. Polytomous Rasch Models Partial Credit Model Category Steps The Mathematical Model Information Relationship to Other IRT Models Variations PCM Summary Rating Scale Model The Mathematical Model Model Parameters Sufficient Statistics and Other Considerations Information Expected Values and Response Functions Response Functions and Information Relationship to Other IRT Models PCM Scoring Function Formulation and the NRM Variations Generalized Partial Credit Model Discrimination and Polytomous Rasch Models Summary of Polytomous Rasch Models Three Practical Examples 4. Samejima Models Framework From Response Process to Specific Model The Homogeneous Case: Graded Response Models The Mathematical Model Information Information for Polytomous Models Relationship to Other IRT Models From Homogeneous Class to Heterogeneous Class and Back A Common Misconception Variations Summary of Samejima Models Potential Weaknesses of the Cumulative Boundary Approach Possible Strengths of the Cumulative Boundary Approach A Practical Example 5. Model Selection General Criteria Mathematical Approaches Fit Statistic Problems An Example Differences in Modeled Outcome Conclusion Acronyms and Glossary Notes References Index About the Authors