difNLR-package: DIF and DDF Detection by Non-Linear Regression Models.

In adelahladka/difNLR: DIF and DDF Detection by Non-Linear Regression Models

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: 1.5.0-1
Date: 2025-02-17
Depends: R (>= 4.0.0)
Imports: calculus, ggplot2 (>= 3.4.0), msm, nnet, plyr, stats, VGAM
Suggests: ShinyItemAnalysis
License: GPL-3
BugReports: https://github.com/adelahladka/difNLR/issues
Encoding: UTF-8

Functions

• ddfMLR

• difNLR

• difORD

• estimNLR

• formulaNLR

• MLR

• NLR

• ORD

• startNLR

Datasets

• GMAT

• GMAT2

• MSATB

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
[e-mail]

Patricia Martinkova
Institute of Computer Science of the Czech Academy of Sciences
[e-mail]

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, \Sexpr[results=rd]{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, \Sexpr[results=rd]{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. Accepted.

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, \Sexpr[results=rd]{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, \Sexpr[results=rd]{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.

See Also

Useful links:

• Report bugs at https://github.com/adelahladka/difNLR/issues

adelahladka/difNLR documentation built on Feb. 21, 2025, 11:52 a.m.