rasch.jml.biascorr: Bias Correction of Item Parameters for Joint Maximum Likelihood Estimation in the Rasch model

Description

This function computes an analytical bias correction for the Rasch model according to the method of Arellano and Hahn (2007).

Usage

rasch.jml.biascorr(jmlobj,itemfac=NULL)

Value

A list with following entries

b.biascorr: Matrix of item difficulty estimates. The column b.analytcorr1 contains item difficulties by analytical bias correction of Method 1 in Arellano and Hahn (2007) whereas b.analytcorr2 corresponds to Method 2.
b.bias1: Estimated bias by Method 1
b.bias2: Estimated bias by Method 2
itemfac: Number of items which are used as the factor for bias correction

Arguments

jmlobj: An object which is the output of the rasch.jml function
itemfac: Number of items which are used for bias correction. By default it is the average number of item responses per person.

References

Arellano, M., & Hahn, J. (2007). Understanding bias in nonlinear panel models: Some recent developments. In R. Blundell, W. Newey & T. Persson (Eds.): Advances in Economics and Econometrics, Ninth World Congress, Cambridge University Press.

Examples

Run this code

#############################################################################
# EXAMPLE 1: Dataset Reading
#############################################################################
data(data.read)
dat <- data( data.read )

# estimate Rasch model
mod <- sirt::rasch.jml( data.read  )

# JML with analytical bias correction
res1 <- sirt::rasch.jml.biascorr( jmlobj=mod  )
print( res1$b.biascorr, digits=3 )
  ##        b.JML b.JMLcorr b.analytcorr1 b.analytcorr2
  ##   1  -2.0086   -1.8412        -1.908        -1.922
  ##   2  -1.1121   -1.0194        -1.078        -1.088
  ##   3  -0.0718   -0.0658        -0.150        -0.127
  ##   4   0.5457    0.5002         0.393         0.431
  ##   5  -0.9504   -0.8712        -0.937        -0.936
  ##  [...]