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

Shaoyang Guo; Chanjin Zheng; Justin L. Kern

doi:10.1177/0146621620932654

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

Shaoyang Guo
, Chanjin Zheng^*
, Justin L. Kern

^*Corresponding author for this work

Faculty of Education

East China Normal University
University of Illinois at Urbana-Champaign

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

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 language	English
Pages (from-to)	566-567
Number of pages	2
Journal	Applied Psychological Measurement
Volume	44
Issue number	7-8
DOIs	https://doi.org/10.1177/0146621620932654
State	Published - 1 Oct 2020

Keywords

1PL-AG
3PLM
4PLM
IRTBEMM
R package

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10.1177/0146621620932654

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title = "IRTBEMM: An R Package for Estimating IRT Models With Guessing or Slipping Parameters",

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.",

keywords = "1PL-AG, 3PLM, 4PLM, IRTBEMM, R package",

author = "Shaoyang Guo and Chanjin Zheng and Kern, \{Justin L.\}",

note = "Publisher Copyright: {\textcopyright} The Author(s) 2020.",

year = "2020",

month = oct,

day = "1",

doi = "10.1177/0146621620932654",

language = "英语",

volume = "44",

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journal = "Applied Psychological Measurement",

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T2 - An R Package for Estimating IRT Models With Guessing or Slipping Parameters

AU - Guo, Shaoyang

AU - Zheng, Chanjin

AU - Kern, Justin L.

N1 - Publisher Copyright: © The Author(s) 2020.

PY - 2020/10/1

Y1 - 2020/10/1

N2 - 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.

AB - 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.

KW - 1PL-AG

KW - 3PLM

KW - 4PLM

KW - IRTBEMM

KW - R package

UR - https://www.scopus.com/pages/publications/85086880092

U2 - 10.1177/0146621620932654

DO - 10.1177/0146621620932654

M3 - 文章

AN - SCOPUS:85086880092

SN - 0146-6216

VL - 44

SP - 566

EP - 567

JO - Applied Psychological Measurement

JF - Applied Psychological Measurement

IS - 7-8

ER -

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

Abstract

Keywords

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