lordif: performs Logistic Regression Differential Item Functioning using IRT

Description

Performs iterative hybrid ordinal logistic regression/IRT DIF.

Usage

lordif(resp.data, group, selection=NULL,  
  criterion="Chisqr", pseudo.R2="McFadden",  
  alpha=0.01, beta.change=0.1, R2.change=0.035,  
  maxIter=10, minCell=5, minTheta=-4.0, maxTheta=4.0, inc=0.1)

Arguments

resp.data

a data frame or matrix containing item responses

group

a vector of group designations

selection

a vector specifying a subset of items to be analyzed or NULL for all items

criterion

criterion for flagging (i.e., "CHISQR", "R2", or "BETA")

pseudo.R2

pseudo R-squared measure (i.e., "McFadden", "Nagelkerke", or "CoxSnell")

alpha

significance level for Chi-squared criterion

beta.change

proportional change for Beta criterion

R2.change

R-squared change for pseudo R-squared criterion

maxIter

maximum number of iterations for purification

minCell

minimum cell frequncy

minTheta

minimum for theta grid

maxTheta

maximum for theta grid

inc

increment for theta grid

Value

optionsOptions used for the run
selectionall or a subset of items analyzed
statsa matrix containing output statistics
flaga logical vector of final flags indicating whether each item is displaying DIF or not
recodeda data frame containing recoded item responses
groupa vector of group designation values
nga scalar for the number of groups
ncata vector of the number of response categories after collapsing/recoding
caliba vector of theta estimates based on the overall (non-group-specific) item parameters
calib.sparsea vector of theta estimates based on the group-specific item parameters (for DIF items)
iterationa scalar for the number of iterations
ipara data frame of the overall (non-group-specific) item parameter estimates
ipar.sparsea data frame of the group-specific item parameter estimates
stats.rawa matrix containing output statistics (the same components as stats above but based on raw scores)
meanrawa vector containing mean raw scores
flag.rawa logical vector of DIF final flags based on raw scores

Details

Performs a ordinal (common odds-ratio) logistic regression DIF analysis using IRT theta estimates as the conditioning variable. The graded response (GR) model is used for IRT trait estimation. Flagged items are treated as separate items and group-specific item parameters are obtained. The procedure runs iteratively until the same set of items is flagged over two iterations.

References

Choi, S. W., Gibbons, L. E., and Crane, P. K. (April, 2009). Development of freeware for an iterative hybrid ordinal logistic regression/IRT DIF. Paper Presented at the National Council of Measurement in Education. San Diego, CA. Crane, P. K., Gibbons, L. E., Jolley, L., and 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.

Examples

Run this code

##data(Anxeity)
resp.data <- Anxiety[paste("R",1:29,sep="")]
age <- Anxiety$age
age.DIF <- lordif(resp.data,age)

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