Analyzing longitudinal data with missing values

Enders, Craig K.

doi:10.1037/a0025579

Cited by 322Statistical Methods and Bayesian InferencePsychometric Methodologies and TestingStatistical Methods in Clinical Trials

Analyzing longitudinal data with missing values

Enders, Craig K.·2011·Rehabilitation Psychology

DOI 10.1037/a0025579

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Abstract

Missing data methodology has improved dramatically in recent years, and popular computer programs now offer a variety of sophisticated options. Despite the widespread availability of theoretically justified methods, researchers in many disciplines still rely on subpar strategies that either eliminate incomplete cases or impute the missing scores with a single set of replacement values. This article provides readers with a nontechnical overview of some key issues from the missing data literature and demonstrates several of the techniques that methodologists currently recommend. This article begins by describing Rubin's missing data mechanisms. After a brief discussion of popular ad hoc approaches, the article provides a more detailed description of five analytic approaches that have received considerable attention in the missing data literature: maximum likelihood estimation, multiple imputation, the selection model, the shared parameter model, and the pattern mixture model. Finally, a series of data analysis examples illustrate the application of these methods.

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Full information maximum likelihood with robust standard errors is a better missing-data strategy than listwise deletion and matches multiple imputation when data are missing at random.
“We used full information maximum likelihood with robust standard errors in Mplus. This is a superior strategy to listwise deletion and equivalent to using multiple imputation techniques when data are assumed to be missing at random (Enders, 2011).”
From Influences of victimization and comorbid conditions on latency to illicit drug use among adolescents and young adults (2020) · cites this paper as Enders, 2011

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Authors: Enders, C. K.
Affiliations: Arizona State University (US)
Corresponding author: Enders, Craig K.
ORCID: Enders, Craig K.
Venue: Rehabilitation Psychology
Publisher: American Psychological Association (APA)
Published: 2011
Volume: 56
Issue: 4
Pages: 267-288
ISSN: 0090-5550 (ISSN-L)
1939-1544
DOI: 10.1037/a0025579
OpenAlex: W1970308312
Cited by: 321
References: 40
Citations by year: 2026: 29 · 2025: 17 · 2024: 26 · 2023: 24 · 2022: 23 · 2021: 39 · 2020: 30 · 2019: 22 · 2018: 23 · 2017: 25 · 2016: 15 · 2015: 22 · 2014: 14 · 2013: 10 · 2012: 1
FWCI: 7.01
Type: journal article
Language: en
Open access: closed
Locations: Rehabilitation Psychology (publishedVersion)
PubMed (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 › Statistics and Probability › Statistical Methods in Clinical Trials
Keywords: missing data, longitudinal data, maximum likelihood estimation, multiple imputation, selection model, shared parameter models, pattern-mixture models, complete case analysis, missing data methods
MeSH terms: Data Interpretation, Statistical, Humans, Longitudinal Studies, Research Design, Likelihood Functions

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