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/.
| Identifer | oai:union.ndltd.org:VIENNA/oai:epub.wu-wien.ac.at:3869 |
| Date | January 2009 |
| Creators | Rusch, Thomas, Hatzinger, Reinhold |
| Publisher | Pabst Science Publishers |
| Source Sets | Wirtschaftsuniversität Wien |
| Language | English |
| Detected Language | English |
| Type | Article, PeerReviewed |
| Format | application/pdf |
| Relation | http://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/ |
Page generated in 0.002 seconds