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Dear R-List, I tried to fit a partial credit model using the "pcmdat" from eRm-package comparing the results of mirt, eRm, ltm and winsteps. The results where quite different, though. I cannot figure out what went wrong and I do not know which result I can rely on. This is what I did in R library(mirt) #load(file="u3.RData") mirt<-mirt(as.data.frame(pcmdat),1,itemtype="graded",constrain=list(1,5,9,13,17,21,2...

Dear All, does anybody know of a package (working under Linux) for multilevel IRT modelling? I'd love to do this without having to go on WINSTEPS or the like.. thanks for the attention! Federico Andreis ----- Dr. Federico Andreis Universit? degli Studi di Milano-Bicocca, PhD Student MEB Department, Karolinska Institutet, Stockholm, Visiting PhD Student -- View this message in context: http://r.789695.n4.nabble.com/Multilevel-IRT-Modelli...

...s functions for miscellaneous psychometrics that may be useful for applied psychometricians. MML estimation already exists in the ltm package. Hence, a jml option is provided for users who prefer this method. The jml function gives back rasch difficulties and the same Infit and Outfit statistics as Winsteps. Also, jml is known to return biased parameter estimates. So, a correction for jml bias is provided for as an optional argument in the function. Currently, the package includes the following: 1) jml() a function for joint maximum likelihood estimation of the Rasch model 2) alpha() , coefficient...

...s functions for miscellaneous psychometrics that may be useful for applied psychometricians. MML estimation already exists in the ltm package. Hence, a jml option is provided for users who prefer this method. The jml function gives back rasch difficulties and the same Infit and Outfit statistics as Winsteps. Also, jml is known to return biased parameter estimates. So, a correction for jml bias is provided for as an optional argument in the function. Currently, the package includes the following: 1) jml() a function for joint maximum likelihood estimation of the Rasch model 2) alpha() , coefficient...

...users >From what I understand, the joint maximum likelihood procedure for Rasch (availabe in the package MiscPsycho) in R can only be used on binary data. I was wondering if the code is currently being adapted for application to ordinal data? I'm trying to replicate results obtained from Winsteps in R. Best wishes denn -- View this message in context: http://www.nabble.com/Joint-maximum-likelihood-estimation-for-ordinal-data-tp19606190p19606190.html Sent from the R help mailing list archive at Nabble.com.