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sirt (version 0.31-20)

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.

Usage

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

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

Value

  • An object of class rasch.pairwise with following entries
  • bItem difficulties
  • epsExponentiated item difficulties, i.e. eps=exp(-b)
  • iterNumber of iterations
  • convConvergence criterion
  • datOriginal data frame
  • freq.ijFrequency table of all item pairs
  • itemSummary 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

Run this code
data(data.read)
mod <- rasch.pairwise( data.read )
summary(mod)

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