Item Response Theory – ScienceOpen
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      Item Response Theory

      1 , 1 , 1 , 1
      Annual Review of Statistics and Its Application
      Annual Reviews

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          Abstract

          This review introduces classical item response theory (IRT) models as well as more contemporary extensions to the case of multilevel, multidimensional, and mixtures of discrete and continuous latent variables through the lens of discrete multivariate analysis. A general modeling framework is discussed, and the applications of this framework in diverse contexts are presented, including large-scale educational surveys, randomized efficacy studies, and diagnostic measurement. Other topics covered include parameter estimation and model fit evaluation. Both classical (numerical integration based) and more modern (stochastic) parameter estimation approaches are discussed. Similarly, limited information goodness-of-fit testing and posterior predictive model checking are reviewed and contrasted. The review concludes with a discussion of some emerging strands in IRT research such as response time modeling, crossed random effects models, and non-standard models for response processes.

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          Equation of State Calculations by Fast Computing Machines

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            Multiple Imputation for Nonresponse in Surveys

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              mirt: A Multidimensional Item Response Theory Package for theREnvironment

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                Author and article information

                Journal
                Annual Review of Statistics and Its Application
                Annu. Rev. Stat. Appl.
                Annual Reviews
                2326-8298
                2326-831X
                June 01 2016
                June 01 2016
                : 3
                : 1
                : 297-321
                Affiliations
                [1 ]CRESST, University of California, Los Angeles, California 90095-1521;
                Article
                10.1146/annurev-statistics-041715-033702
                fa4dd8cf-03dc-4033-8427-7161ac4d76cb
                © 2016
                History

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