Provides a comprehensive introduction to the range of polytomous models available within item response theory. Practical examples of major models using real data are provided, as is a chapter on choosing an appropriate model. Figures are used throughout to illustrate important elements, as they are described.
Provides a comprehensive introduction to the range of polytomous models available within item response theory. Practical examples of major models using real data are provided, as is a chapter on choosing an appropriate model. Figures are used throughout to illustrate important elements, as they are described.
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. Remös 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.
Inhaltsangabe
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
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
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