Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
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Authors: Hannah Frick, Carolin Strobl, Friedrich Leisch, Achim Zeileis
Title: Flexible Rasch Mixture Models with Package psychomix
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.

Page views:: 4131. Submitted: 2011-10-06. Published: 2012-05-24.
Paper: Flexible Rasch Mixture Models with Package psychomix     Download PDF (Downloads: 3600)
psychomix_1.0-0.tar.gz: R source package Download (Downloads: 739; 288KB)
v48i07.R: R example code from the paper Download (Downloads: 839; 5KB)

DOI: 10.18637/jss.v048.i07

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.