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      Local Dependence Diagnostics in IRT Modeling of Binary Data

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      Educational and Psychological Measurement
      SAGE Publications

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          Most cited references17

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          Item factor analysis: Current approaches and future directions.

          The rationale underlying factor analysis applies to continuous and categorical variables alike; however, the models and estimation methods for continuous (i.e., interval or ratio scale) data are not appropriate for item-level data that are categorical in nature. The authors provide a targeted review and synthesis of the item factor analysis (IFA) estimation literature for ordered-categorical data (e.g., Likert-type response scales) with specific attention paid to the problems of estimating models with many items and many factors. Popular IFA models and estimation methods found in the structural equation modeling and item response theory literatures are presented. Following this presentation, recent developments in the estimation of IFA parameters (e.g., Markov chain Monte Carlo) are discussed. The authors conclude with considerations for future research on IFA, simulated examples, and advice for applied researchers. (c) 2007 APA, all rights reserved.
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            Likelihood-Based Item-Fit Indices for Dichotomous Item Response Theory Models

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              Fitting a response model forn dichotomously scored items

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                Author and article information

                Journal
                Educational and Psychological Measurement
                Educational and Psychological Measurement
                SAGE Publications
                0013-1644
                1552-3888
                September 18 2012
                August 16 2012
                : 73
                : 2
                : 254-274
                Article
                10.1177/0013164412453841
                5b94a449-b7a5-4009-96e5-8b35c0c5c916
                © 2012

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

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