[R-sig-ME] Rasch with lme4

Reinhold Kliegl reinhold.kliegl at gmail.com
Mon Jun 8 17:00:08 CEST 2009

  Conditional modes (generated from the model parameters and the data)  
are not independent observations. Therefore, only the second method is  

Reinhold Kliegl

On 08.06.2009, at 13:04, Jeroen Ooms wrote:

> I have tried to use lme4 to analyze IRT like datasets, but now I am
> confused. I have a data set with intelligence items (i.e. score 0 or  
> 1), for
> completely crossed subjects and items. Furthermore, the data  
> contains some
> personality scores on the subject level. Actually the data is more
> complicated than this, but let's keep it simple for now. My research
> question is whether a personality charcteristic, say extraversion, is
> related to intelligence. My question is how I should incorporate the
> extraversion variable in the analysis.
> When I analyse this data using the Rasch model, I usually first fit  
> the
> model, then extract the 'latent trait scores', and relate these to the
> extraversion scores. I could do the same with lmer:
> myModel <- lmer(y~1+(1|item)+(1|subject),data=mydata,  
> family=binomial);
> intelligence <- ranef(myModel)$subject[[1]];
> lm(intelligence~extraversion);
> However, in the context of multilevel analysis, it is also possible to
> incorporate the extraversion variable directly into the model:
> myModel2 <- lmer(y~1+(1|item)+(1|subject)+extraversion,data=mydata,
> family=binomial);
> Conceptually both methods feel very similar, but they give different
> results. What is the most appropriate method? What are the  
> differences in
> interpretation?
> Thank you!
> Jeroen
> 	[[alternative HTML version deleted]]
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