I have matrix of dichotomus correct/false answers with many random missing data. (The data comes from an ability test where questions were randomly drawn from an item bank.)
I am trying to find out how well items fit to a Rasch model.
I have tried different packages so far.
eRm - this worked very well with fewer (60) items but now that I am trying to run it with cc.160 items it takes an unbearably long time to complete.
library('mirt') raschfit <- mirt(data, 1, itemtype='Rasch') Theta <- fscores(raschfit, method = 'ML', full.scores=TRUE, scores.only=TRUE) ifit<-itemfit(raschfit, impute = 10, Theta=Theta)
This works but I am afraid that there is so much imputation going on that it would mask bad items. The main reason I believe this is that by calculating a simple item-total correlation I found an item with -0.3. This item was clearly wrong. However when I looked at the statistics produced by mirt itemfit this item never popped up as a particularly wrong one.
library(ltm) mod <- rasch(keyed_response) item.fit(mod)
I couldn't figure out how to do this in ltm. The code above doesn't give me any sensible data (all zeros for X^2)
Could somebody suggest me a good method?