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Statistics - Social Sciences & Law | Multidimensional Item Response Theory

Multidimensional Item Response Theory

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Multidimensional Item Response Theory is the first book to give thorough coverage to this emerging area of psychometrics. The book describes the commonly used multidimensional item response theory (MIRT) models and the important methods needed for their practical application. These methods include ways to determine the number of dimensions required to adequately model data, procedures for estimating model parameters, ways to define the space for a MIRT model, and procedures for transforming calibrations from different samples to put them in the same space. A full chapter is devoted to methods for multidimensional computerized adaptive testing.

The text is appropriate for an advanced course in psychometric theory or as a reference work for those interested in applying MIRT methodology. A working knowledge of unidimensional item response theory and matrix algebra is assumed. Knowledge of factor analysis is also helpful.

Mark D. Reckase is a professor of Measurement and Quantitative Methods in the College of Education at Michigan State University. He has been president of the National Council of Measurement in Education, Vice President of Division D of the American Educational Research Association, on the Board of Trustees of the Psychometric Society, and the editor of Applied Psychological Measurement and the Journal of Educational Measurement. He has been doing research in the area of MIRT since 1972.

Content Level » Research

Keywords » Factor Analysis - methodology - model - psychometrics

Related subjects » Psychology - Social Sciences - Social Sciences & Law - Theoretical Computer Science

Table of contents 

Introduction.- Historical background for Multidimensional Item Response Theory (MIRT).- Unidimensional Item Response Theory models.- Multidimensional Item Response Theory models.- Statistical descriptions of item and test functioning.- Estimation of item and person parameters.- Analyzing the structure of test data.- Transforming parameter estimates to a specified coordinate system.- Linking and scaling.- Computerized adaptive testing using MIRT.

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