Multiple imputation strategies for multiple group structural equation models

Craig K. Enders, Amanda C. Gottschall

Research output: Contribution to journalArticle

37 Scopus citations

Abstract

Although structural equation modeling software packages use maximum likelihood estimation by default, there are situations where one might prefer to use multiple imputation to handle missing data rather than maximum likelihood estimation (e.g., when incorporating auxiliary variables). The selection of variables is one of the nuances associated with implementing multiple imputation, because the imputer must take special care to preserve any associations or special features of the data that will be modeled in the subsequent analysis. For example, this article deals with multiple group models that are commonly used to examine moderation effects in psychology and the behavioral sciences. Special care must be exercised when using multiple imputation with multiple group models, as failing to preserve the interactive effects during the imputation phase can produce biased parameter estimates in the subsequent analysis phase, even when the data are missing completely at random or missing at random. This study investigates two imputation strategies that have been proposed in the literature, product term imputation and separate group imputation. A series of simulation studies shows that separate group imputation adequately preserves the multiple group data structure and produces accurate parameter estimates.

Original languageEnglish (US)
Pages (from-to)35-54
Number of pages20
JournalStructural Equation Modeling
Volume18
Issue number1
DOIs
StatePublished - Jan 1 2011

ASJC Scopus subject areas

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

Fingerprint Dive into the research topics of 'Multiple imputation strategies for multiple group structural equation models'. Together they form a unique fingerprint.

  • Cite this