Edited By Everett V. Smith, Jr. and Richard M. Smith
24 chapters (700 pages, $47), written by the leading experts in
Rasch measurement. More details are at
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I. Foundations
1. An Overview of the Family of Rasch Measurement Models. Benjamin Wright, University of Chicago Magdalena Mo Ching Mok, Hong Kong Institute of Education
2. Estimation Methods for Rasch Measures. J. Michael Linacre, University of Sunshine Coast
3. Rasch Model Estimation: Further Topics. J. Michael Linacre, University of Sunshine Coast
4. Fit Analysis in Latent Trait Measurement Models. Richard Smith, Data Recognition Corporation
5. Evidence of the Reliability of Measures and Validity of Measure Interpretation: A Rasch Measurement Perspective. Everett Smith, University of Illinois at Chicago
6. On Choosing a Model for Measuring. Mark Wilson, University of California, Berkeley
7. Controversy and the Rasch Model: A Characteristic of Incompatible Paradigms? David Andrich, Murdoch University
8. Understanding Resistance to the Data-Model Relationship in Rasch's Paradigm: A Reflection for the Next Generation. David Andrich, Murdoch University
II. Basic Applications
9. Substantive Scale Construction. Mark H. Stone, Adler School of Professional Psychology
10. Rasch Measurement: The Dichotomous Model. Randall Schumacker, University of North Texas
11. Optimizing Rating Scale Category Effectiveness. J. Michael Linacre, University of Sunshine Coast
12. Partial Credit Model and Pivot Anchoring. Rita Bode, Rehabilitation Institute of Chicago
13. Construction of Measures from Many-Facet Data. J. Michael Linacre, University of Sunshine Coast Benjamin Wright, University of Chicago
14. An Introduction to Multidimensional Measurement using Rasch Models. Derek C. Briggs, University of Colorado Mark Wilson, University of California, Berkeley
15. Metric Development and Score Reporting in Rasch Measurement. Everett Smith, University of Illinois at Chicago
III. Advanced Applications
16. Equating and Item Banking with the Rasch Model. Edward Wolfe, Michigan State University
17. Detecting Item Bias with the Rasch Model. Richard Smith, Data Recognition Corporation
18. Rasch Techniques for Detecting Bias in Performance Assessments: An Example Comparing the Performance Native and Non-Native Speakers on a Test of Academic English. Catherine Elder, University of Auckland Tim McNamara, University of Melbourne Peter Congdon, Victorian Curriculum and Assessment Authority
19. Objective Standard Setting (or Truth in Advertising). Gregory Stone, University of Toledo
20. Detecting and Measuring Rater Effects using Many-Facet Rasch Measurement: Part I. Carol Myford, University of Illinois at Chicago Edward Wolfe, Michigan State University
21. Detecting and Measuring Rater Effects using Many-Facet Rasch Measurement: Part II. Carol Myford, University of Illinois at Chicago Edward Wolfe, Michigan State University
22. Detecting and Evaluating the Impact of Multidimensionality using Item Fit Statistics and Principal Component Analysis of Residuals. Everett Smith, University of Illinois at Chicago
23. Computer Adaptive Testing. Richard Gershon, Northwestern University
24. The Rasch Model, Additive Conjoint Measurement, and New Models of Probabilistic Measurement Theory. George Karabatsos, University of Illinois at Chicago
Book: Introduction to Rasch Measurement, Smith E. V. & Smith R. M. Rasch Measurement Transactions, 2004, 18:2 p. 979
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Coming Rasch-related Events | |
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Oct. 4 - Nov. 8, 2024, Fri.-Fri. | On-line workshop: Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com |
Jan. 17 - Feb. 21, 2025, Fri.-Fri. | On-line workshop: Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com |
May 16 - June 20, 2025, Fri.-Fri. | On-line workshop: Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com |
June 20 - July 18, 2025, Fri.-Fri. | On-line workshop: Rasch Measurement - Further Topics (E. Smith, Facets), www.statistics.com |
Oct. 3 - Nov. 7, 2025, Fri.-Fri. | On-line workshop: Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com |
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