Missing data? Plan on it!

Research output: Contribution to journalReview articlepeer-review

35 Scopus citations

Abstract

Longitudinal study designs are indispensable for investigating age-related functional change. There now are well-established methods for addressing missing data in longitudinal studies. Modern missing data methods not only minimize most problems associated with missing data (e.g., loss of power and biased parameter estimates), but also have valuable new applications such as research designs that use modern missing data methods to plan missing data purposefully. This article describes two state-of-the-art statistical methodologies for addressing missing data in longitudinal research: growth curve analysis and statistical measurement models. How the purposeful planning of missing data in research designs can reduce subject burden, improve data quality and statistical power, and manage costs is then described.

Original languageEnglish (US)
Pages (from-to)S343-S348
JournalJournal of the American Geriatrics Society
Volume58
Issue numberSUPPL. 2
DOIs
StatePublished - Oct 1 2010

Keywords

  • longitudinal aging research
  • missing data methods
  • statistical measurement

ASJC Scopus subject areas

  • Geriatrics and Gerontology

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