lrt function

Computes Andersen's Likelihood Ratio Test for the multidimensional polytomous Rasch model

Andersen's Likelihood Ratio Test is a model test for Rasch models (based on CML estimation) and splits the data set into subsamples to test the person homogeneity

## S3 method for class 'DRM'
LRT(object, splitcrit = "score", ...)

## S3 method for class 'MPRM'
LRT(object, splitcrit = "score", ...)

LRT(object, ...)

## S3 method for class 'aLR'
print(x, ...)

## S3 method for class 'aLR'
summary(object, ...)

Arguments

• object: Object of class MPRM or DRM or aLR
• splitcrit: Vector or the character vector "score" to define the split criterion. The default split criterion "score" splits the sample according to the median of the raw score. Vector can be numeric, factor or character. (see details)
• x: Object of class aLR
• ``: further arguments

Returns

• emp_Chi2: $\chi^2$ distributed value of the Likelihood Ratio test - df: degrees of freedom of the test statistic

• pval: p value of the test statistic - itempar: estimated item parameters for each subsample - item_se: estimated standard errors for the item parameters for each subsample

Details

The default split criterion "score" computes the raw score of every person according to the category values in the data set. The sample is split by the median of this raw score.

Examples

#simulate data set
simdat <- simMPRM(rbind(matrix(c(-1.5,0.5,0.5,1,0.8,-0.3, 0.2,-1.2), 
                  ncol=4),0), 500)

#estimate MPRM item parameters
res_mprm <- MPRM(simdat$datmat)

#compute Andersen's Likelihood Ratio test
res_lrt <- LRT(res_mprm)
summary(res_lrt)

References

Andersen, E. B. (1973). A goodness of fit test for the Rasch model. Psychometrika, 38, 123- 140.

Fischer, G. H. (1974). Einfuehrung in die Theorie psychologischer Tests [Introduction to test theory]. Bern: Huber.

See Also

MPRM dLRT

Author(s)

Christine Hohensinn