pcIRT (version 0.2.4)

print.wt: Test for the scoring weights in the unidimensional polytomous Rasch model

Description

This functions tests the fit of fixed scoring parameters in a unidimensional polytomous Rasch model.

Usage

# S3 method for wt
print(x, ...)
# S3 method for wt
summary(object, ...)
weight_test(MPRMobj, score_param)

Arguments

x

object of class wt

…

…

object

object of class wt

MPRMobj

Object of class MPRM

score_param

Numerical vector with the scoring parameters that are tested

Value

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

unconstrLoglikelihood

log-likelihood of the unconstrained model

constrLoglikelihood

log-likelihood of the constrained model

unconstrNrPar

number of estimated parameters in the unconstrained model

constrNrPar

number of estimated parameters in the constrained model

unconstrItempar

estimated item parameters of the unconstrained model

constrItempar

estimated item parameters of the constrained model

unconstrScoreParameter

estimated scoring parameters of the unconstrained model

Details

If the unidimensional polytomous Rasch model fits the data, the weight test can be performed to test whether assumed scoring parameters are appropriate. An unconstrained unidimensional polytomous Rasch model is calculated including estimation of scoring parameters. Furthermore a constrained unidimensional polytomous Rasch model is estimated with fixed scoring parameters (according to the input). Subsequently a Likelihood Ratio test tests the fit of the fixed scoring parameters.

References

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

See Also

MPRM dLRT

Examples

# NOT RUN {
#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)

#tests the scoring parameter 0.5 for the unidimensional polytomous model
res_weight <- weight_test(res_mprm,  score_param=c(0.5))
summary(res_weight)


# }