Dealing With Missing Data in Developmental Research

Craig K. Enders

Research output: Contribution to journalArticle

101 Citations (Scopus)

Abstract

Approaches to handling missing data have improved dramatically in recent years and researchers can now choose from a variety of sophisticated analysis options. The methodological literature favors maximum likelihood and multiple imputation because these approaches offer substantial improvements over older approaches, including a strong theoretical foundation, less restrictive assumptions, and the potential for bias reduction and greater power. These benefits are especially important for developmental research where attrition is a pervasive problem. This article provides a brief introduction to modern methods for handling missing data and their application to developmental research.

Original languageEnglish (US)
Pages (from-to)27-31
Number of pages5
JournalChild Development Perspectives
Volume7
Issue number1
DOIs
StatePublished - Mar 2013

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Keywords

  • Attrition
  • Imputation
  • Maximum likelihood
  • Missing data
  • Multiple imputation

ASJC Scopus subject areas

  • Life-span and Life-course Studies
  • Developmental and Educational Psychology
  • Pediatrics, Perinatology, and Child Health

Cite this

Dealing With Missing Data in Developmental Research. / Enders, Craig K.

In: Child Development Perspectives, Vol. 7, No. 1, 03.2013, p. 27-31.

Research output: Contribution to journalArticle

Enders, Craig K. / Dealing With Missing Data in Developmental Research. In: Child Development Perspectives. 2013 ; Vol. 7, No. 1. pp. 27-31.
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