UZH-Logo

Maintenance Infos

Flexible Rasch Mixture Models with package psychomix


Frick, H; Strobl, Carolin; Leisch, F; Zeileis, A (2012). Flexible Rasch Mixture Models with package psychomix. Journal of Statistical Software, 48(7):1-25.

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 esti- mation 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.

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 esti- mation 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.

Citations

5 citations in Web of Science®
5 citations in Scopus®
Google Scholar™

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Psychology
Dewey Decimal Classification:150 Psychology
Language:English
Date:2012
Deposited On:19 Nov 2012 13:40
Last Modified:05 Apr 2016 16:05
Publisher:University of California, Los Angeles
Series Name:Journal of Statistical Software
ISSN:1548-7660
Free access at:Official URL. An embargo period may apply.
Official URL:http://www.jstatsoft.org/v48/i07

Download

Full text not available from this repository.

TrendTerms

TrendTerms displays relevant terms of the abstract of this publication and related documents on a map. The terms and their relations were extracted from ZORA using word statistics. Their timelines are taken from ZORA as well. The bubble size of a term is proportional to the number of documents where the term occurs. Red, orange, yellow and green colors are used for terms that occur in the current document; red indicates high interlinkedness of a term with other terms, orange, yellow and green decreasing interlinkedness. Blue is used for terms that have a relation with the terms in this document, but occur in other documents.
You can navigate and zoom the map. Mouse-hovering a term displays its timeline, clicking it yields the associated documents.

Author Collaborations