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.
ddfMLR
difNLR
difORD
estimNLR
formulaNLR
MLR
NLR
ORD
startNLR
GMAT
GMAT2
MSATB
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
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
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, tools:::Rd_expr_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, tools:::Rd_expr_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, tools:::Rd_expr_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, tools:::Rd_expr_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, tools:::Rd_expr_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.
Useful links:
Report bugs at https://github.com/adelahladka/difNLR/issues