lordif (version 0.1-9)

lordif-package: LOgistic Regression Differential Item Functioning using IRT

Description

Analysis of Differential Item Functioning (DIF) for dichotomous and polytomous items using an iterative hybrid of (ordinal) logistic regression and item response theory (IRT).

Details

ll{ Package: lordif Type: Package Version: 0.1-9 Date: 2010-11-29 License: GPL (>=2) LazyLoad: yes } Ordinal logistic regression (OLR) provides a flexible framework for detecting various types of DIF. Previous efforts extended the framework by substituting the matching variable based on sum scores with IRT based trait scores and by employing an iterative process of purifying the matching variable with the use of group-specific item parameters (Crane et. al., 2006). This package represents an effort to integrate both statistical and IRT procedures into a single program. A Monte Carlo simulation approach was incorporated to derive empirical threshold values for various DIF statistics and effect size measures. The two most important functions are: lordif and montecarlo.

References

Crane, P. K., Gibbons, L. E., Jolley, L., & van Belle, G. (2006). Differential item functioning analysis with ordinal logistic regression techniques: DIF detect and difwithpar. Medical Care, 44(11 Suppl 3), S115-S123.

See Also

ltm, Design

Examples

##load PROMIS Anxiety sample data (n=766)
  data(Anxiety)
##age : 0=younger than 65 or 1=65 or older
##gender: 0=Male or 1=Female
##education: 0=some college or higher 1=high school of lower
##run age-related DIF on all 29 items (takes about a minute)
  age.dif <- lordif(Anxiety[paste("R",1:29,sep="")],Anxiety$age) 
##print output
##print(age.dif)
##print extended output
##summary(age.dif)
##generate plots for DIF items (reference group: <65)
##plot(age.dif,labels=c("Younger","Older")) 
##run Monte Carlo simulations for threshold values
##this may take several minutes
  age.dif.MC<-montecarlo(age.dif,alpha=0.05,nr=100)
##print output
##print(age.dif.MC)
##print extended output
##summary(age.dif.MC)
##generate plots for Monte Carlo threshold values
##plot(age.dif.MC)