Issues in evaluating model fit with missing data

Adam Davey, Jyoti Savla, Zupei Luo

Research output: Contribution to journalArticlepeer-review

43 Scopus citations


Effects of incomplete data on fit indexes remain relatively unexplored. We evaluate a wide set of fit indexes (χ2, root mean squared error of appproximation, Normed Fit Index [NFI], Tucker-Lewis Index, comparative fit index, gamma-hat, and McDonald's Centrality Index) varying conditions of sample size (100-1,000 in increments of 50), factor loadings (.4 or .8), factor covariances (.4 or .8), type of missing data (missing completely at random or missing at random), and extent of missing data (0-95% on 3 of 9 indicators in increments of 5%) for correct and 2 misspecified (measurement or structural) models. Incremental and absolute fit indexes indicate better fit with higher proportions of missing data. Effects of missing data on the NFI were more varied, indicating poorer model fit as missing data increased for the correct model, and indicating better or poorer fit as an interaction of all the other factors for misspecified models. Recommendations are made for researchers and software developers.

Original languageEnglish (US)
Pages (from-to)578-597
Number of pages20
JournalStructural Equation Modeling
Issue number4
StatePublished - 2005

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Modeling and Simulation
  • Sociology and Political Science
  • Economics, Econometrics and Finance(all)


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