pirfa
Product of Indicators (PI) for MIMIC/RFA Models in DIF detection
Science Score: 57.0%
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Repository
Product of Indicators (PI) for MIMIC/RFA Models in DIF detection
Basic Info
- Host: GitHub
- Owner: cmerinos
- License: gpl-3.0
- Language: R
- Default Branch: main
- Homepage: https://github.com/cmerinos/piRFA
- Size: 1.94 MB
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- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 1
- Releases: 0
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Metadata Files
README.md
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:
``` r
Install from GitHub
devtools::install_github("cmerinos/piRFA") library(piRFA) ```
Example
Basic example:
``` r 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
Owner
- Name: Cesar Merino-Soto
- Login: cmerinos
- Kind: user
- Repositories: 1
- Profile: https://github.com/cmerinos
PhD; Psychologist; quantitative methodology, psychometric analysis, nonparametric analysis, content validity.
Citation (CITATION.cff)
cff-version: 1.2.0 message: "If you use this software, please cite it as below." authors: - family-names: "Merino-Soto" given-names: "Cesar" orcid: "https://orcid.org/0000-0000-0000-0000" title: "piRFA: R package for DIF detection using Product of Indicators (PI) for MIMIC/RFA Models" version: 0.1.0 date-released: 2025-02-22 url: "https://github.com/cmerinos/piRFA"
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