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IRTBEMM: An R Package for Estimating IRT Models With Guessing or Slipping Parameters.

Shaoyang Guo1, Chanjin Zheng1, Justin L Kern2

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|September 27, 2021
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Summary
This summary is machine-generated.

A new R package, IRTBEMM, offers advanced Bayesian and maximum likelihood estimation algorithms for Item Response Theory (IRT) models, including those with guessing and slipping parameters. This tool aids researchers in complex IRT analyses.

Keywords:
1PL-AG3PLM4PLMIRTBEMMR package

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Modeling

Background:

  • Item Response Theory (IRT) models are widely used in educational and psychological assessments.
  • Standard IRT models often do not account for response behaviors like guessing and item parameter drift.
  • Accurate estimation of these parameters is crucial for reliable test scoring and analysis.

Purpose of the Study:

  • To introduce the IRTBEMM R package, a novel tool for Item Response Theory (IRT) model estimation.
  • To provide researchers with efficient algorithms for IRT models incorporating guessing and slipping parameters.
  • To facilitate the application of advanced IRT models to real-world datasets.

Main Methods:

  • The IRTBEMM package implements Bayesian Expectation-Maximization (EMM) and Bayesian Expectation-Maximization-Maximization (E3M) algorithms.
  • Maximum likelihood versions of these algorithms are also included for comparative analysis.
  • The package supports various IRT models, including 3-parameter logistic (3PL), 4-parameter logistic (4PL), 1-parameter logistic with guessing (1PL-G), and 1-parameter logistic with assumed guessing (1PL-AG) models.

Main Results:

  • The IRTBEMM package provides a unified framework for estimating complex IRT models.
  • New estimation algorithms are available for models with guessing and slipping parameters.
  • The package is designed for practical application with real datasets.

Conclusions:

  • The IRTBEMM R package offers valuable new tools for psychometricians and researchers.
  • It enhances the ability to model and analyze Item Response Theory data with complex response patterns.
  • This package is expected to advance the application of IRT in various research fields.