Several decades of psychometric research have led to the development of sophisticated models for multidimensional test data, and in recent years, multidimensional item response theory (MIRT) has become a burgeoning topic in psychological and educational measurement. Considered a cutting-edge statistical technique, the methodology underlying MIRT can be complex, and therefore doesn’t receive much attention in introductory IRT courses. However author Wes Bonifay shows how MIRT can be understood and applied by anyone with a firm grounding in unidimensional IRT modeling. His volume includes practical examples and illustrations, along with numerous figures and diagrams. Multidimensional Item Response Theory includes snippets of R code interspersed throughout the text (with the complete R code included on an accompanying website) to guide readers in exploring MIRT models, estimating the model parameters, generating plots, and implementing the various procedures and applications discussed throughout the book.

MIRT Model Diagnostics and Evaluation

An important component of any statistical modeling scenario is the diagnosis and evaluation of the model. A thorough statistical model appraisal can uncover its strengths and/or shortcomings, thereby identifying ways to improve the statistical rigor and usefulness of the model. IRT offers a number of evaluative and diagnostic techniques that can help us answer several important questions about the psychometric quality of a model. Is a MIRT model appropriate or necessary for the data that have been collected? If so, how can we select the correct model from among several competing models? Is each item response surface an accurate representation of the true probability of responding correctly? Does the multidimensionality in the overall model reflect the true structure of ...

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