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Software Review : XCALIBRE™ Marginal Maximum-Likelihood Estimation Program, Windows™ Version 1. 10

Software Review : XCALIBRE™ Marginal Maximum-Likelihood Estimation Program, Windows™ Version 1. 10 In this, the first software review for Applied Psychological Measurement, XCALIBRE™, a marginal maxi mum-likelihood estimation (MMLE) program for computing item and trait (θ) parameters for the two-pa rameter logistic (2PLM) and the three-parameter logistic (3PLM) item response theory (IRT) models, is evaluated. The criteria used to evaluate this program were established, in part, by the Editor for Software Reviews, Richard Luecht, as well as by us. We identified criteria that should be satisfied to produce a computer program that is useful to both researchers and practitioners using IRT. The criteria are discussed throughout this paper and are also presented, more succinctly, in the Appendix. The review is divided into five sections: program description, program interface and performance, output, documentation, and our evaluative conclusion. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Psychological Measurement SAGE

Software Review : XCALIBRE™ Marginal Maximum-Likelihood Estimation Program, Windows™ Version 1. 10

Abstract

In this, the first software review for Applied Psychological Measurement, XCALIBRE™, a marginal maxi mum-likelihood estimation (MMLE) program for computing item and trait (θ) parameters for the two-pa rameter logistic (2PLM) and the three-parameter logistic (3PLM) item response theory (IRT) models, is evaluated. The criteria used to evaluate this program were established, in part, by the Editor for Software Reviews, Richard Luecht, as well as by us. We identified criteria that should be satisfied to produce a computer program that is useful to both researchers and practitioners using IRT. The criteria are discussed throughout this paper and are also presented, more succinctly, in the Appendix. The review is divided into five sections: program description, program interface and performance, output, documentation, and our evaluative conclusion.
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