difNLR-package {difNLR} | R Documentation |
DIF and DDF Detection by Non-Linear Regression Models.
Description
The difNLR package provides methods for detecting differential item functioning (DIF) using non-linear regression models. Both uniform and non-uniform DIF effects can be detected when considering a single focal group. Additionally, the method allows for testing differences in guessing or inattention parameters between the reference and focal group. DIF detection is performed using either a likelihood-ratio test, an F-test, or Wald's test of a submodel. The software offers a variety of algorithms for estimating item parameters.
Furthermore, the difNLR package includes methods for detecting differential distractor functioning (DDF) using multinomial log-linear regression model. It also introduces DIF detection approaches for ordinal data via adjacent category logit and cumulative logit regression models.
Details
Package: difNLR
Type: Package
Version: 1.5.1-1
Date: 2025-03-03
Depends: R (>= 4.0.0)
Imports: calculus, ggplot2 (>= 3.4.0), msm, nnet, plyr, stats, VGAM
Suggests: ShinyItemAnalysis, testthat (>= 3.0.0)
License: GPL-3
BugReports: https://github.com/adelahladka/difNLR/issues
Encoding: UTF-8
Functions
Datasets
Note
This package was supported by grant funded by Czech Science foundation under number GJ15-15856Y.
Author(s)
Adela Hladka (nee Drabinova)
Institute of Computer Science of the Czech Academy of Sciences
hladka@cs.cas.cz
Patricia Martinkova
Institute of Computer Science of the Czech Academy of Sciences
martinkova@cs.cas.cz
References
Agresti, A. (2010). Analysis of ordinal categorical data. Second edition. John Wiley & Sons.
Drabinova, A. & Martinkova, P. (2017). Detection of differential item functioning with nonlinear regression: A non-IRT approach accounting for guessing. Journal of Educational Measurement, 54(4), 498–517, doi:10.1111/jedm.12158.
Hladka, A. (2021). Statistical models for detection of differential item functioning. Dissertation thesis. Faculty of Mathematics and Physics, Charles University.
Hladka, A. & Martinkova, P. (2020). difNLR: Generalized logistic regression models for DIF and DDF detection. The R Journal, 12(1), 300–323, doi:10.32614/RJ-2020-014.
Hladka, A., Martinkova, P., & Brabec, M. (2024). New iterative algorithms for estimation of item functioning. Journal of Educational and Behavioral Statistics. Online first, doi:10.3102/10769986241312354.
Kingston, N., Leary, L., & Wightman, L. (1985). An exploratory study of the applicability of item response theory methods to the Graduate Management Admission Test. ETS Research Report Series, 1985(2): 1–64.
Martinkova, P., Drabinova, A., Liaw, Y. L., Sanders, E. A., McFarland, J. L., & Price, R. M. (2017). Checking equity: Why differential item functioning analysis should be a routine part of developing conceptual assessments. CBE–Life Sciences Education, 16(2), rm2, doi:10.1187/cbe.16-10-0307.
Swaminathan, H. & Rogers, H. J. (1990). Detecting differential item functioning using logistic regression procedures. Journal of Educational Measurement, 27(4), 361–370, doi:10.1111/j.1745-3984.1990.tb00754.x
Vlckova, K. (2014). Test and item fairness. Master's thesis. Faculty of Mathematics and Physics, Charles University.
See Also
Useful links:
Report bugs at https://github.com/adelahladka/difNLR/issues