Cited by 1,027Statistical Methods and Bayesian InferencePsychometric Methodologies and TestingStatistical and numerical algorithms

A Primer on Maximum Likelihood Algorithms Available for Use With Missing Data

Enders, Craig K.·2001·Structural Equation Modeling: A Multidisciplinary Journal

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.

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Crossref · 10.1207/s15328007sem0801_7

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  • The model was fit using robust Maximum Likelihood (MLR) to account for deviations from multivariate normality, robust Huber-White Standard errors, and Full-Information Maximum Likelihood (FIML) for missing data.

    “The model was fit using robust Maximum Likelihood (MLR) to account for deviations from multivariate normality, and robust Huber-White Standard errors, and missing data were accounted for by Full-Information Maximum Likelihood (FIML) estimation55.”

    From Windows of developmental sensitivity to social media (2022) · cites this paper as [55]

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Authors
Enders, C. K.
Affiliations
University of Miami (US)
Corresponding author
Enders, Craig K.
Venue
Structural Equation Modeling: A Multidisciplinary Journal
Published
2001-01
Volume
8
Issue
1
Pages
128-141
ISSN
  • 1070-5511 (ISSN-L)
  • 1532-8007
OpenAlex
W2037608980
Cited by
1,027
References
21
Citations by year
2026: 27 · 2025: 38 · 2024: 40 · 2023: 38 · 2022: 47 · 2021: 61 · 2020: 57 · 2019: 67 · 2018: 78 · 2017: 71 · 2016: 84 · 2015: 51 · 2014: 55 · 2013: 47 · 2012: 38
FWCI
13.96
Type
journal article
Language
en
Open access
closed
Locations
Structural Equation Modeling A Multidisciplinary Journal (publishedVersion)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
  • Physical Sciences › Mathematics › Applied Mathematics › Statistical and numerical algorithms
Keywords
maximum likelihood estimation, missing data, full information maximum likelihood, expectation-maximization algorithm, multiple imputation, estimator comparison

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