Marginalized Maximum Likelihood Estimation for the 1PL-AG IRT Model

Ryoungsun Park, Keenan A. Pituch, Jiseon Kim, Barbara G. Dodd, Hyewon Chung

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Marginal maximum likelihood estimation based on the expectation–maximization algorithm (MML/EM) is developed for the one-parameter logistic model with ability-based guessing (1PL-AG) item response theory (IRT) model. The use of the MML/EM estimator is cross-validated with estimates from NLMIXED procedure (PROC NLMIXED) in Statistical Analysis System. Numerical data are provided for comparisons of results from MML/EM and PROC NLMIXED.

Original languageEnglish (US)
Pages (from-to)448-464
Number of pages17
JournalApplied Psychological Measurement
Volume39
Issue number6
DOIs
StatePublished - Sep 8 2015
Externally publishedYes

Keywords

  • 1PL-AG
  • EM
  • estimator
  • IRT
  • MML

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Psychology (miscellaneous)

Fingerprint Dive into the research topics of 'Marginalized Maximum Likelihood Estimation for the 1PL-AG IRT Model'. Together they form a unique fingerprint.

Cite this