Bias Correction of Item Parameters for Joint Maximum Likelihood Estimation in the Rasch model
This function computes an analytical bias correction for the Rasch model according to the method of Arellano and Hahn (2007).
rasch.jml.biascorr(jmlobj,itemfac=NULL)
jmlobj |
An object which is the output of the |
itemfac |
Number of items which are used for bias correction. By default it is the average number of item responses per person. |
A list with following entries
b.biascorr |
Matrix of item difficulty estimates. The column
|
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 |
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.
See rasch.jml.jackknife1 for bias correction based on
Jackknife.
See also the bife R package for analytical bias corrections.
############################################################################# # 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 ## [...]
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