Literature DB >> 25106393

Assessing item fit for unidimensional item response theory models using residuals from estimated item response functions.

Shelby J Haberman1, Sandip Sinharay, Kyong Hee Chon.   

Abstract

Residual analysis (e.g. Hambleton & Swaminathan, Item response theory: principles and applications, Kluwer Academic, Boston, 1985; Hambleton, Swaminathan, & Rogers, Fundamentals of item response theory, Sage, Newbury Park, 1991) is a popular method to assess fit of item response theory (IRT) models. We suggest a form of residual analysis that may be applied to assess item fit for unidimensional IRT models. The residual analysis consists of a comparison of the maximum-likelihood estimate of the item characteristic curve with an alternative ratio estimate of the item characteristic curve. The large sample distribution of the residual is proved to be standardized normal when the IRT model fits the data. We compare the performance of our suggested residual to the standardized residual of Hambleton et al. (Fundamentals of item response theory, Sage, Newbury Park, 1991) in a detailed simulation study. We then calculate our suggested residuals using data from an operational test. The residuals appear to be useful in assessing the item fit for unidimensional IRT models.

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Year:  2012        PMID: 25106393     DOI: 10.1007/s11336-012-9305-1

Source DB:  PubMed          Journal:  Psychometrika        ISSN: 0033-3123            Impact factor:   2.500


  1 in total

1.  Bayesian item fit analysis for unidimensional item response theory models.

Authors:  Sandip Sinharay
Journal:  Br J Math Stat Psychol       Date:  2006-11       Impact factor: 3.380

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Journal:  Psychometrika       Date:  2019-03-20       Impact factor: 2.500

2.  A Generalized Speed-Accuracy Response Model for Dichotomous Items.

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Journal:  Psychometrika       Date:  2017-11-21       Impact factor: 2.500

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Authors:  Gerhard Tutz
Journal:  Psychometrika       Date:  2022-04-27       Impact factor: 2.500

4.  Modified Item-Fit Indices for Dichotomous IRT Models with Missing Data.

Authors:  Xue Zhang; Chun Wang
Journal:  Appl Psychol Meas       Date:  2022-09-19
  4 in total

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