piRFA: R package for DIF detection using Product of Indicators (PI) for MIMIC/RFA Models

piRFA is an R package for detecting Differential Item Functioning (DIF) using the Product of Indicators (PI) approach within a MIMIC/RFA framework (Multiple Indicators Multiple Causes/Restricted Factor Analysis).

Installation

You can install the development version of piRFA like so:

# Install from GitHub
devtools::install_github("cmerinos/piRFA")
library(piRFA)

Example

Basic example:

library(piRFA)

# Load data
set.seed(123)
example_data <- data.frame(
  group = sample(0:1, 100, replace = TRUE), 
  item1 = sample(1:5, 100, replace = TRUE),
  item2 = sample(1:5, 100, replace = TRUE),
  item3 = sample(1:5, 100, replace = TRUE))

# Run DIF analysis
results <- piRFA(data = example_data, items = c("item1", "item2", "item3"), cov = "group")

# Show output
results

# View specific results
print(results$DIF_Global)
print(results$SEPC)


# Plot results
piRFA.plot(results, cov = "group")

References

Kolbe, L., & Jorgensen, T. D. (2018). Using product indicators in restricted factor analysis models to detect nonuniform measurement bias. In M. Wiberg, S. A. Culpepper, R. Janssen, #' J. González, & D. Molenaar (Eds.), Quantitative psychology: The 82nd Annual Meeting of the Psychometric Society, Zurich, Switzerland, 2017 (pp. 235–245). New York, NY: Springer. https://doi.org/10.1007/978-3-319-77249-3_20{.uri}

Kolbe, L., & Jorgensen, T. D. (2019). Using restricted factor analysis to select anchor items and detect differential item functioning. Behavior Research Methods, 51, 138–151. https://doi.org/10.3758/s13428-018-1151-3

Kolbe, L., Jorgensen, T. D., & Molenaar, D. (2020). The Impact of Unmodeled Heteroskedasticity on Assessing Measurement Invariance in Single-group Models. Structural Equation Modeling: A Multidisciplinary Journal, 28(1), 82–98. https://doi.org/10.1080/10705511.2020.1766357