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

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

DIF and DDF Detection by Non-Linear Regression Models.

Description

The difNLR package contains method for detection of differential item functioning (DIF) based on non-linear regression. Both uniform and non-uniform DIF effects can be detected when considering one focal group. The method also allows to test the difference in guessing or inattention parameters between reference and focal group. DIF detection method is based either on likelihood-ratio test, F-test, or Wald's test of a submodel. Package also offers methods for detection of differential distractor functioning (DDF) based on multinomial log-linear regression model and newly methods for DIF detection among ordinal data via adjacent category logit and cumulative logit regression models.

Details

Package: difNLR
Type: Package
Version: 1.4.2-1
Date: 2023-05-03
Depends: R (>= 3.1)
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
Faculty of Mathematics and Physics, Charles University
[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")}.

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

difNLR documentation built on May 3, 2023, 5:11 p.m.