Analyzing Longitudinal Data With Missing Values

Craig K. Enders

Research output: Contribution to journalArticle

126 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)267-288
Number of pages22
JournalRehabilitation Psychology
Volume56
Issue number4
DOIs
StatePublished - Nov 1 2011

Keywords

  • Longitudinal analyses
  • Maximum likelihood estimation
  • Missing data
  • Multilevel model
  • Multiple imputation

ASJC Scopus subject areas

  • Physical Therapy, Sports Therapy and Rehabilitation
  • Rehabilitation
  • Clinical Psychology
  • Psychiatry and Mental health

Fingerprint Dive into the research topics of 'Analyzing Longitudinal Data With Missing Values'. Together they form a unique fingerprint.

  • Cite this