The impact of nonnormality on full information maximum-likelihood estimation for structural equation models with missing data

Craig K. Enders

Research output: Contribution to journalArticle

365 Scopus citations

Abstract

A Monte Carlo simulation examined full information maximum-likelihood estimation (FIML) in structural equation models with nonnormal indicator variables. The impacts of 4 independent variables were examined (missing data algorithm, missing data rate, sample size, and distribution shape) on 4 outcome measures (parameter estimate bias, parameter estimate efficiency, standard error coverage, and model rejection rates). Across missing completely at random and missing at random patterns, FIML parameter estimates involved less bias and were generally more efficient than those of ad hoc missing data techniques. However, similar to complete-data maximum-likelihood estimation in structural equation modeling, standard errors were negatively biased and model rejection rates were inflated. Simulation results suggest that recently developed correctives for missing data (e.g., rescaled statistics and the bootstrap) can mitigate problems that stem from nonnormal data.

Original languageEnglish (US)
Pages (from-to)352-370
Number of pages19
JournalPsychological Methods
Volume6
Issue number3
StatePublished - Dec 1 2001

ASJC Scopus subject areas

  • Psychology (miscellaneous)

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