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This volume presents a wide-ranging handbook to item response theory - and its applications to educational and psychological testing. It will serve as both an introduction to the subject and also as a comprehensive reference volume for practitioners and researchers.
Dec 12, 2014 · Examples using real data, some from NIH-PROMIS, show how to apply these models in actual research situations. Chapters review fundamental issues of IRT, modern estimation methods, testing assumptions, evaluating fit, item banking, scoring in multidimensional models, and advanced IRT methods.
- 1st Edition
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Apr 30, 2010 · Item Response Theory is used to describe the application of mathematical models to data from questionnaires and tests as a basis for measuring abilities, attitudes, or other variables. It is used for statistical analysis and the development of assessments, often for high stakes tests such as the Graduate Record Examination.
- Christine Demars
• A thorough introduction to the foundations of Item Response Theory, including its logic and origins, model-based measurement, psychological scaling, and classical test theory • An exploration of selected mathematical and statistical results, including points, point sets, and set operations, probability, sampling, and joint, conditional ...
Aug 18, 2016 · It describes models for different response formats or response processes, the need of deeper parameterization due to a multilevel or hierarchical structure of the response data, and other extensions and insights.
- 1st Edition
Fundamentals of Item Response Theory. Using familiar concepts from classical measurement methods and basic statistics, Hambleton and colleagues introduce the basics of item response theory (IRT) and explain the application of IRT methods to problems in test construction, identification of potentially biased test items, test equating, and ...
Examples using real data, some from NIH-PROMIS, show how to apply these models in actual research situations. Chapters review fundamental issues of IRT, modern estimation methods, testing assumptions, evaluating fit, item banking, scoring in multidimensional models, and advanced IRT methods.
