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

2 Scopus citations


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
Issue number6
StatePublished - Sep 8 2015
Externally publishedYes


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

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Psychology (miscellaneous)


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