Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/73532
Authors: 
Frick, Hannah
Strobl, Carolin
Leisch, Friedrich
Zeileis, Achim
Year of Publication: 
2011
Series/Report no.: 
Working Papers in Economics and Statistics 2011-21
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.
Subjects: 
mixed Rasch model
Rost model
mixture model
flexmix
R
JEL: 
C38
C52
C87
Document Type: 
Working Paper

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