This function performs the eigenvector approach to estimate item parameters which is based on a pairwise estimation approach (Garner & Engelhard, 2002). No assumption about person parameters is required for item parameter estimation. Statistical inference is performed by Jackknifing. If a group identifier is provided, tests for differential item functioning are performed.
rasch.evm.pcm(dat, jackunits=20, weights=NULL, pid=NULL,
group=NULL, powB=2, adj_eps=0.3, progress=TRUE )# S3 method for rasch.evm.pcm
summary(object, digits=3, file=NULL, ...)
# S3 method for rasch.evm.pcm
coef(object,...)
# S3 method for rasch.evm.pcm
vcov(object,...)
A list with following entries
Data frame with item parameters. The item parameter estimate
is denoted by est while a Jackknife bias-corrected estimate
is est_jack. The Jackknife standard error is se.
Item threshold parameters
Data frame with person parameters obtained (MLE)
Paired comparison matrix
Transformed paired comparison matrix
Vector of estimated coefficients
Covariance matrix of estimated item parameters
Number of jackknife units
Reduced number of jackknife units
Used power of comparison matrix \(B\)
Maximum number of categories per item
Number of groups
Some descriptives
Statistics for differential item functioning if group
is provided as an argument
Data frame with dichotomous or polytomous item responses
A number of Jackknife units (if an integer is provided as the argument value) or a vector in which the Jackknife units are already defined.
Optional vector of sample weights
Optional vector of person identifiers
Optional vector of group identifiers. In this case, item parameters are group wise estimated and tests for differential item functioning are performed.
Power created in \(B\) matrix which is the basis of parameter estimation
Adjustment parameter for person parameter estimation
(see mle.pcm.group)
An optional logical indicating whether progress should be displayed
Object of class rasch.evm.pcm
Number of digits after decimals for rounding in summary.
Optional file name if summary should be sunk into a file.
Further arguments to be passed
Choppin, B. (1985). A fully conditional estimation procedure for Rasch Model parameters. Evaluation in Education, 9, 29-42.
Garner, M., & Engelhard, G. J. (2002). An eigenvector method for estimating item parameters of the dichotomous and polytomous Rasch models. Journal of Applied Measurement, 3, 107-128.
Wang, J., & Engelhard, G. (2014). A pairwise algorithm in R for rater-mediated assessments. Rasch Measurement Transactions, 28(1), 1457-1459.
See the pairwise package for the alternative row averaging approach of Choppin (1985) and Wang and Engelhard (2014) for an alternative R implementation.