The impact of missing data on sample reliability estimates: Implications for reliability reporting practices

Craig K. Enders

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

Abstract

A method for incorporating maximum likelihood (ML) estimation into reliability analyses with item-level missing data is outlined. An ML estimate of the covariance matrix is first obtained using the expectation maximization (EM) algorithm, and coefficient alpha is subsequently computed using standard formulae. A simulation study demonstrated that the EM approach yields (a) less bias in reliability estimates, (b) dramatically reduces cross-sample fluctuation of estimates, and (c) yields more accurate confidence intervals. Implications for reliability reporting practices are discussed, and the EM procedure is demonstrated using a heuristic data set.

Original languageEnglish (US)
Pages (from-to)419-436
Number of pages18
JournalEducational and Psychological Measurement
Volume64
Issue number3
DOIs
StatePublished - Jun 2004

Keywords

  • EM algorithm
  • Maximum likelihood
  • Missing data
  • Reliability
  • Reliability generalization

ASJC Scopus subject areas

  • Education
  • Developmental and Educational Psychology
  • Applied Psychology
  • Applied Mathematics

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