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项目作者： adelahladka

项目描述 ：
DIF and DDF Detection by Non-Linear Regression Models.
高级语言： R
项目地址: git://github.com/adelahladka/difNLR.git
创建时间： 2016-11-15T14:24:15Z
项目社区：https://github.com/adelahladka/difNLR
开源协议：
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difNLR

DIF and DDF Detection by Non-Linear Regression Models.

R-CMD-check
GHversion
version
cranlogs

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.





Installation

The easiest way to get difNLR package is to install it from CRAN:

  1. install.packages("difNLR")

Or you can get the newest development version from GitHub:

  1. # install.packages("devtools")
  2. devtools::install_github("adelahladka/difNLR")

Version

Current version on CRAN is
1.5.1-1. The newest development version available on
GitHub is 1.5.1-2.

Reference

To cite difNLR package in publications, please, use:

    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,
    https://doi.org/10.1111/jedm.12158

To cite new estimation approaches provided in the difNLR() function, please, use:

    Hladka, A., Martinkova, P., & Brabec, M. (2024). New iterative algorithms for estimation of item functioning.
    Journal of Educational and Behavioral Statistics.
    Online first, https://doi.org/10.3102/10769986241312354

Try online

You can try some functionalities of the difNLR package
online using
ShinyItemAnalysis
application and package and its DIF/Fairness section.

Getting help

In case you find any bug or just need help with the difNLR package, you can leave
your message as an issue here or directly contact us at hladka@cs.cas.cz