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

Shaoyang Guo, Chanjin Zheng, Justin L. Kern

Research output: Contribution to journalArticlepeer-review

Abstract

A recently released R package IRTBEMM is presented in this article. This package puts together several new estimation algorithms (Bayesian EMM, Bayesian E3M, and their maximum likelihood versions) for the Item Response Theory (IRT) models with guessing and slipping parameters (e.g., 3PL, 4PL, 1PL-G, and 1PL-AG models). IRTBEMM should be of interest to the researchers in IRT estimation and applying IRT models with the guessing and slipping effects to real datasets.

Original languageEnglish (US)
Pages (from-to)566-567
Number of pages2
JournalApplied Psychological Measurement
Volume44
Issue number7-8
DOIs
StatePublished - Oct 1 2020

Keywords

  • 1PL-AG
  • 3PLM
  • 4PLM
  • IRTBEMM
  • R package

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Psychology (miscellaneous)

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