sirt (version 1.5-0)

rasch.pairwise: Pairwise Estimation Method of the Rasch Model

Description

This function estimates the Rasch model with a minimum chi square estimation method (cited in Fischer, 2007, p. 544) which is a pairwise conditional likelihood estimation approach.

Usage

rasch.pairwise(dat, conv = 1e-04, maxiter = 3000, progress = TRUE, 
        b.init = NULL)

## S3 method for class 'rasch.pairwise':
summary(object,...)

Arguments

dat

An $N \times I$ data frame of dichotomous item responses

conv

Convergence criterion

maxiter

Maximum number of iterations

progress

Display iteration progress?

b.init

An optional vector of length $I$ of item difficulties

object

Object of class rasch.pairwise

...

Further arguments to be passed

Value

• An object of class rasch.pairwise with following entries
• b
  Item difficulties
• eps
  Exponentiated item difficulties, i.e. eps=exp(-b)
• iter
  Number of iterations
• conv
  Convergence criterion
• dat
  Original data frame
• freq.ij
  Frequency table of all item pairs
• item
  Summary table of item parameters

References

Fischer, G. H. (2007). Rasch models. In C. R. Rao and S. Sinharay (Eds.), Handbook of Statistics, Vol. 26 (pp. 515-585). Amsterdam: Elsevier.

See Also

See summary.rasch.pairwise for a summary. A slightly different implementation of this conditional pairwise method is implemented in rasch.pairwise.itemcluster. Pairwise marginal likelihood estimation (also labeled as pseudolikelihood estimation) can be conducted with rasch.pml3.

Examples

#############################################################################
# EXAMPLE 1: Reading data set | pairwise estimation Rasch model
#############################################################################

data(data.read)
mod <- rasch.pairwise( data.read )
summary(mod)