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

Educational Psychology

Research output: Contribution to journal › Article › peer-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 language	English (US)
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 - Oct 1 2020

Keywords

1PL-AG
3PLM
4PLM
IRTBEMM
R package

ASJC Scopus subject areas

Social Sciences (miscellaneous)
Psychology (miscellaneous)

Online availability

10.1177/0146621620932654

Library availability

Discover UIUC Full Text

Cite this

@article{471c12194b214684bf88fd3a2a271651,

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 = "Funding Information: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was partially supported by the Peak Discipline Construction Project of Education at East China Normal University and the Flower of Happiness Project in social science of East China Normal University (2019ECNU-XFZH015). Publisher Copyright: {\textcopyright} The Author(s) 2020.",

year = "2020",

month = oct,

day = "1",

doi = "10.1177/0146621620932654",

language = "English (US)",

volume = "44",

pages = "566--567",

journal = "Applied Psychological Measurement",

issn = "0146-6216",

publisher = "SAGE Publishing",

number = "7-8",

}

TY - JOUR

T1 - IRTBEMM

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

AU - Guo, Shaoyang

AU - Zheng, Chanjin

AU - Kern, Justin L.

N1 - Funding Information: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was partially supported by the Peak Discipline Construction Project of Education at East China Normal University and the Flower of Happiness Project in social science of East China Normal University (2019ECNU-XFZH015). 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 - http://www.scopus.com/inward/record.url?scp=85086880092&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85086880092&partnerID=8YFLogxK

U2 - 10.1177/0146621620932654

DO - 10.1177/0146621620932654

M3 - Article

C2 - 34565936

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

ASJC Scopus subject areas

Online availability

Library availability

Related links

Fingerprint

Cite this