lordif {lordif}R Documentation

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=0.02,  
  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

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.

Value

options Options used for the run
selection all or a subset of items analyzed
stats a matrix containing output statistics
flag a logical vector of final flags indicating whether each item is displaying DIF or not
recoded a data frame containing recoded item responses
group a vector of group designation values
ng a scalar for the number of groups
ncat a vector of the number of response categories after collapsing/recoding
calib a vector of theta estimates based on the overall (non-group-specific) item parameters
calib.sparse a vector of theta estimates based on the group-specific item parameters (for DIF items)
iteration a scalar for the number of iterations
ipar a data frame of the overall (non-group-specific) item parameter estimates
ipar.sparse a data frame of the group-specific item parameter estimates
stats.raw a matrix containing output statistics (the same components as stats above but based on raw scores)
meanraw a vector containing mean raw scores
flag.raw a logical vector of DIF final flags based on raw scores

Note

requires the ltm and Design packages

Author(s)

Seung W. Choi <s-choi@northwestern.edu>

References

Choi, S. W., Gibbons, L. E., & Crane, P. K. (under review). Development of an iterative hybrid ordinal logistic regression/IRT DIF: A Monte Carlo simulation approach for determining cutoff values. Behavior Research Methods.

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.

See Also

rundif

Examples

##data(Anxeity)
## Not run:  resp.data <- Anxiety[paste("R",1:29,sep="")] 
## Not run:  age <- Anxiety$age 
## Not run:  age.DIF <- lordif(resp.data,age) 

[Package lordif version 0.1-4 Index]