A Primer on Maximum Likelihood Algorithms Available for Use With Missing Data
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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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.
- ORCID
- Enders, Craig K.
- Venue
- Structural Equation Modeling: A Multidisciplinary Journal
- Publisher
- Informa UK Limited
- 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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