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Flexible Rasch Mixture Models with Package psychomix

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  • Hannah Frick

    ()

  • Carolin Strobl

    ()

  • Friedrich Leisch

    ()

  • Achim Zeileis

    ()

Abstract

Measurement invariance is an important assumption in the Rasch model and mixture models constitute a flexible way of checking for a violation of this assumption by detecting unobserved heterogeneity in item response data. Here, a general class of Rasch mixture models is established and implemented in R, using conditional maximum likelihood estimation of the item parameters (given the raw scores) along with flexible specification of two model building blocks: (1) Mixture weights for the unobserved classes can be treated as model parameters or based on covariates in a concomitant variable model. (2) The distribution of raw score probabilities can be parametrized in two possible ways, either using a saturated model or a specification through mean and variance. The function raschmix() in the R package "psychomix" provides these models, leveraging the general infrastructure for fitting mixture models in the "flexmix" package. Usage of the function and its associated methods is illustrated on artificial data as well as empirical data from a study of verbally aggressive behavior.

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File URL: http://eeecon.uibk.ac.at/wopec2/repec/inn/wpaper/2011-21.pdf
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Bibliographic Info

Paper provided by Faculty of Economics and Statistics, University of Innsbruck in its series Working Papers with number 2011-21.

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Length: 28
Date of creation: Oct 2011
Date of revision:
Handle: RePEc:inn:wpaper:2011-21

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Keywords: mixed Rasch model; Rost model; mixture model; flexmix; R;

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  1. Carolin Strobl & Julia Kopf & Achim Zeileis, 2011. "A new method for detecting differential item functioning in the Rasch model," Working Papers 2011-01, Faculty of Economics and Statistics, University of Innsbruck.
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Cited by:
  1. Hannah Frick & Carolin Strobl & Achim Zeileis, 2013. "Rasch Mixture Models for DIF Detection: A Comparison of Old and New Score Specifications," Working Papers 2013-36, Faculty of Economics and Statistics, University of Innsbruck.

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