regDIF: Regularized Differential Item Functioning

An R package that performs regularization of differential item functioning (DIF) parameters in item response theory (IRT) models using a penalized expectation-maximization algorithm.

Version 1.0.0 Features

Handles multiple continuous and categorical DIF covariates.
Supports binary and ordinal item responses.
Includes LASSO, ridge, MCP, and elastic net penalty functions for regularization.

Installation

To get the current development version from Github:

# install.packages("devtools")
devtools::install_github("wbelzak/regDIF")

Getting Started

A simulated data example with 6 item responses (binary) and 3 background variables (gender, age, study) is used to demonstrate the regDIF package.

library(regDIF)
head(ida)
#>   item1 item2 item3 item4 item5 item6 age gender study
#> 1     0     0     0     0     0     0  -2     -1    -1
#> 2     0     0     0     0     0     0   0     -1    -1
#> 3     0     0     0     0     0     0   3     -1    -1
#> 4     0     1     1     1     1     1   1     -1    -1
#> 5     0     0     0     0     0     0  -2     -1    -1
#> 6     1     0     0     0     0     0   1     -1    -1

First, the item responses and predictor values are separately specified.

item.data <- ida[, 1:6]
pred.data <- ida[, 7:9]

Second, the regDIF() function fits a sequence of 10 tuning parameter values.

fit <- regDIF(item.data, pred.data, num.tau = 10)

Finally, the DIF results are shown.

summary(fit)
#> Call:
#> regDIF(item.data = item.data, pred.data = pred.data, num.tau = 10)
#> 
#> Optimal model (out of 10):
#>          tau          bic 
#>    0.1934603 4074.7270000 
#> 
#> Non-zero DIF effects:
#>    item4.int.age    item5.int.age item5.int.gender  item5.int.study 
#>            0.263           -0.247           -0.656            0.349

Questions

Please send any questions to wbelzak@gmail.com.