sirt (version 0.31-20)

dif.logistic.regression: Differential Item Functioning using Logistic Regression Analysis

Description

This function estimates differential item functioning using a logistic regression analysis (Zumbo, 1999).

Usage

dif.logistic.regression(dat, group, score)

Arguments

dat

Data frame with dichotomous item responses

group

Group identifier

score

Ability estimate, e.g. the WLE.

Value

• A data frame with following variables:
• item
  Item name
• N
  Sample size per item
• R
  Value of group variable for reference group
• F
  Value of group variable for focal group
• nR
  Sample size per item in reference group
• nF
  Sample size per item in focal group
• p
  Item $p$ value
• pR
  Item $p$ value in reference group
• pF
  Item $p$ value in focal group
• pdiff.adj
  Adjusted $p$ value difference
• uniformDIF
  Uniform DIF estimate
• se.uniformDIF
  Standard error of uniform DIF
• t.uniformDIF
  The $t$ value for uniform DIF
• sig.uniformDIF
  Significance label for uniform DIF
• nonuniformDIF
  Nonuniform DIF estimate
• se.nonuniformDIF
  Standard error of nonuniform DIF
• t.nonuniformDIF
  The $t$ value for nonuniform DIF
• sig.nonuniformDIF
  Significance label for nonuniform DIF

References

Zumbo, B. D. (1999). A Handbook on the Theory and Methods of Differential Item Functioning (DIF): Logistic Regression Modeling as a Unitary Framework for Binary and Likert-type (Ordinal) Item Scores. Ottawa ON: Directorate of Human Resources Research and Evaluation, Department of National Defense.

See Also

For assessing DIF variance see dif.variance and dif.strata.variance See the difR package for a large collection of DIF detection methods.

Examples

#####################################
# EXAMPLE 1: Mathematics data

data( data.math )
dat <- data.math$data
items <- grep( "M" , colnames(dat))

# estimate item parameters and WLEs
mod <- rasch.mml2( dat[,items] )
wle <- wle.rasch( dat[,items] , b=mod$item$b )$theta

# assess DIF by logistic regression
mod1 <- dif.logistic.regression( dat=dat[,items] ,
         score=wle , group=dat$female )

# calculate DIF variance
dif1 <- dif.variance( dif=mod1$uniformDIF , se.dif = mod1$se.uniformDIF )
dif1$unweighted.DIFSD
## > dif1$unweighted.DIFSD
## [1] 0.1963958

# calculate stratified DIF variance
# stratification based on domains
dif2 <- dif.strata.variance( dif=mod1$uniformDIF , se.dif = mod1$se.uniformDIF ,
        itemcluster = data.math$item$domain )
## $unweighted.DIFSD
## [1] 0.1455916