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IRT models with relaxed assumptions in eRm: A manual-like instruction

Linear logistic models with relaxed assumptions (LLRA) as introduced by Fischer (1974) are a
flexible tool for the measurement of change for dichotomous or polytomous responses. As opposed to
the Rasch model, assumptions on dimensionality of items, their mutual dependencies and the
distribution of the latent trait in the population of subjects are relaxed. Conditional maximum likelihood
estimation allows for inference about treatment, covariate or trend effect parameters without taking the
subjects' latent trait values into account. In this paper we will show how LLRAs based on the LLTM,
LRSM and LPCM can be used to answer various questions about the measurement of change and how
they can be fitted in R using the eRm package. A number of small didactic examples is provided that
can easily be used as templates for real data sets. All datafiles used in this paper are available from
http://eRm.R-Forge.R-project.org/.

Identiferoai:union.ndltd.org:VIENNA/oai:epub.wu-wien.ac.at:3869
Date January 2009
CreatorsRusch, Thomas, Hatzinger, Reinhold
PublisherPabst Science Publishers
Source SetsWirtschaftsuniversität Wien
LanguageEnglish
Detected LanguageEnglish
TypeArticle, PeerReviewed
Formatapplication/pdf
Relationhttp://www.psychologie-aktuell.com/index.php?id=inhaltlesen&tx_ttnews[pointer]=2&tx_ttnews[tt_news]=896&tx_ttnews[backPid]=204&cHash=3e4a79ec17, http://www.pabst-publishers.com/, http://www.psychologie-aktuell.com/index.php?id=200, http://erm.r-forge.r-project.org/, http://epub.wu.ac.at/3869/

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