The relative performance of full information maximum likelihood estimation for missing data in structural equation models

Craig K. Enders, Deborah L. Bandalos

Research output: Contribution to journalArticle

2056 Scopus citations

Abstract

A Monte Carlo simulation examined the performance of 4 missing data methods in structural equation models: full information maximum likelihood (FIML), listwise deletion, pairwise deletion, and similar response pattern imputation. The effects of 3 independent variables were examined (factor loading magnitude, sample size, and missing data rate) on 4 outcome measures: convergence failures, parameter estimate bias, parameter estimate efficiency, and model goodness of fit. Results indicated that FIML estimation was superior across all conditions of the design. Under ignorable missing data conditions (missing completely at random and missing at random), FIML estimates were unbiased and more efficient than the other methods. In addition, FIML yielded the lowest proportion of convergence failures and provided near-optimal Type 1 error rates across both simulations.

Original languageEnglish (US)
Pages (from-to)430-457
Number of pages28
JournalStructural Equation Modeling
Volume8
Issue number3
DOIs
StatePublished - Dec 1 2001

ASJC Scopus subject areas

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

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