ExampleCode/Chapter 2 Observed Score Methods/07_DIF Logistic Regression More than 2 groups.R

In cswells1/MeasInv: Collection of Methods to Detect Dichotomous and Polytomous Differential Item Functioning (DIF)

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# The following code uses Logistic Regression to test for DIF using the difLogistic             #
# function that is located in the difR package.                                       		#
#################################################################################################

# Load rms package: It performs logistic (binary and ordinal) that is used by  #
# the DIF.Logistic function.                                                  #
library(rms)

# Read data: The data are in in a csv file in which first 30 columns #
# represent item responses and the last column contains the grouping #
# variable.                                                          #
myfile <- system.file("extdata", "MCData3Group.csv", package = "MeasInv")
MC.data <- read.csv(myfile, sep=",", header=T)

MC.data$group <- factor(MC.data$group) # Convert the grouping variable, "group", to a factor #
						   # which means R treats it as an unordered-categorical #
						   # (i.e., grouping) variable.                          #


# Use logistic regression to test for DIF using the lrm function which is in the rms package. #
raw.score <- apply(X = MC.data[,1:34], MARGIN = 1, FUN = sum)    # Compute raw score #
grp <- MC.data$group

lrm(MC.data[,4] ~ raw.score + grp + raw.score*grp)          # Perform logistic regression: Model 3 #
lrm(MC.data[,4] ~ raw.score)                                # Perform logistic regression: Model 1 #

# Perform DIF using logistic regression via the DIF.Logistic function. #
Logistic.results <- DIF.Logistic.MG(data = MC.data[,1:34], group = MC.data$group, 
		                        sig.level = .05, purify = FALSE, output.filename = "MG LogReg Output")