A primer on the use of modern missing-data methods in psychosomatic medicine research

Craig K. Enders

Research output: Contribution to journalArticle

175 Scopus citations

Abstract

This paper summarizes recent methodologic advances related to missing data and provides an overview of two "modern" analytic options, direct maximum likelihood (DML) estimation and multiple imputation (MI). The paper begins with an overview of missing data theory, as explicated by Rubin. Brief descriptions of traditional missing data techniques are given, and DML and MI are outlined in greater detail; special attention is given to an "inclusive" analytic strategy that incorporates auxiliary variables into the analytic model. The paper concludes with an illustrative analysis using an artificial quality of life data set. Computer code for all DML and MI analyses is provided, and the inclusion of auxiliary variables is illustrated.

Original languageEnglish (US)
Pages (from-to)427-436
Number of pages10
JournalPsychosomatic Medicine
Volume68
Issue number3
DOIs
StatePublished - May 1 2006

Keywords

  • Attrition
  • Direct maximum likelihood
  • Full information maximum likelihood
  • Maximum likelihood
  • Missing data
  • Multiple imputation

ASJC Scopus subject areas

  • Applied Psychology
  • Psychiatry and Mental health

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