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Loglinear Multidimensional Item Response Models for Polytomously Scored Items

  • Chapter
Handbook of Modern Item Response Theory

Abstract

Over the last decade, there has been increasing interest in analyzing mental test data with loglinear models. Several authors have shown that the Rasch model for dichotomously scored items can be formulated as a loglinear model (Cressie and Holland, 1983; de Leeuw and Verhelst, 1986; Kelderman, 1984; Thissen and Mooney, 1989; Tjur, 1982). Because there are good procedures for estimating and testing Rasch models, this result was initially only of theoretical interest. However, the flexibility of loglinear models facilitates the specification of many other types of item response models. In fact, they give the test analyst the opportunity to specify a unique model tailored to a specific test. Kelderman (1989) formulated loglinear Rasch models for the analysis of item bias and presented a gamut of statistical tests sensitive to different types of item bias. Duncan and Stenbeck (1987) formulated a loglinear model specifying a multidimensional model for Likert type items. Agresti (1993) and Kelderman and Rijkes (1994) formulated a loglinear model specifying a general multidimensional response model for polytomously scored items.

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Authors
  1. Henk Kelderman
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Editor information

Editors and Affiliations

  1. Faculty of Educational Sciences and Technology, University of Twente, 7500 AE, Enschede, Netherlands

    Wim J. van der Linden

  2. School of Education, University of Massachusetts, 01003, Amherst, MA, USA

    Ronald K. Hambleton

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© 1997 Springer Science+Business Media New York

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Kelderman, H. (1997). Loglinear Multidimensional Item Response Models for Polytomously Scored Items. In: van der Linden, W.J., Hambleton, R.K. (eds) Handbook of Modern Item Response Theory. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-2691-6_17

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  • DOI: https://doi.org/10.1007/978-1-4757-2691-6_17

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4419-2849-8

  • Online ISBN: 978-1-4757-2691-6

  • eBook Packages: Springer Book Archive

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