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Essays on Item Response Theory pp 43–68Cite as

Gain Scores Revisited Under an IRT Perspective

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

Abstract

For the measurement and statistical assessment of individual gain scores based on item sets that satisfy the assumptions of the Rasch, Rating Scale, or Partial Credit Models, a conditional maximum likelihood estimator, Clopper-Pearson confidence intervals, uniformly most accurate confidence intervals, and uniformly most powerful unbiased tests of the hypothesis of no change are presented. All methods are grounded on the exact conditional distribution of the gain score, given the total score for both time points, so that no asymptotic approximations are required. Typical applications of the methods are mentioned.

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Author information

Authors and Affiliations

  1. Department of Psychology, University of Vienna, Liebiggasse 5, A-1010, Wien, Austria

    Gerhard H. Fischer

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  1. Gerhard H. Fischer
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Editor information

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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Fischer, G.H. (2001). Gain Scores Revisited Under an IRT Perspective. 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_3

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  • DOI: https://doi.org/10.1007/978-1-4613-0169-1_3

  • 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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