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Differential Item Functioning Depending on General Covariates

  • Chapter
Essays on Item Response Theory

Part of the book series: Lecture Notes in Statistics ((LNS,volume 157))

Abstract

Item response theory (IRT) is a powerful tool for the detection of differential item functioning (DIF). It is shown that the class of IRT models with manifest predictors is a comprehensive framework for the detection of DIF. These models also support the investigation of the causes of DIF. In principle, the responses to every item in a test can be subject to DIF, and traditional IRT-based detection methods require one or more estimation runs for every single item. Therefore, (1998) proposed an alternative procedure that can be performed using only a single estimate of the item parameters. This procedure is based on the Lagrange multiplier test or the equivalent Rao efficient score test. In this chapter, the procedure is generalized in various directions, the most important one being the possibility of conditioning on general covariates. A small simulation study is presented to give an impression of the power of the test. In an example using real data it is shown how the method can be applied to the identification of main and interaction effects in DIF.

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Authors and Affiliations

  1. Department of Educational Measurement and Data Analysis, University of Twente, P.O. Box 217, 7500, AE Enschede, The Netherlands

    Cees A. W. Glas

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  1. Cees A. W. Glas
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Editors and Affiliations

  1. Department of Statistics and Measurement Theory, University of Groningen, Grote Kruisstraat 2/1, 9712 TS, Groningen, The Netherlands

    Anne Boomsma , Marijtje A. J. van Duijn  & Tom A. B. Snijders ,  & 

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Glas, C.A.W. (2001). Differential Item Functioning Depending on General Covariates. In: Boomsma, A., van Duijn, M.A.J., Snijders, T.A.B. (eds) Essays on Item Response Theory. Lecture Notes in Statistics, vol 157. Springer, New York, NY. https://doi.org/10.1007/978-1-4613-0169-1_7

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  • DOI: https://doi.org/10.1007/978-1-4613-0169-1_7

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-95147-8

  • Online ISBN: 978-1-4613-0169-1

  • eBook Packages: Springer Book Archive

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