A Primer on maximum likelihood algorithms available for use with missing data

Craig K. Enders

Research output: Contribution to journalArticle

643 Scopus citations

Abstract

Maximum likelihood algorithms for use with missing data are becoming commonplace in microcomputer packages. Specifically, 3 maximum likelihood algorithms are currently available in existing software packages: the multiple-group approach, full information maximum likelihood estimation, and the EM algorithm. Although they belong to the same family of estimator, confusion appears to exist over the differences among the 3 algorithms. This article provides a comprehensive, nontechnical overview of the 3 maximum likelihood algorithms. Multiple imputation, which is frequently used in conjunction with the EM algorithm, is also discussed.

Original languageEnglish (US)
Pages (from-to)128-141
Number of pages14
JournalStructural Equation Modeling
Volume8
Issue number1
DOIs
StatePublished - Dec 1 2001

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Modeling and Simulation
  • Sociology and Political Science
  • Economics, Econometrics and Finance(all)

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