Structural equation modeling: Strengths, limitations, and misconceptions

Andrew J. Tomarken, Niels G. Waller

Research output: Contribution to journalReview articlepeer-review

423 Scopus citations

Abstract

Because structural equation modeling (SEM) has become a very popular data-analytic technique, it is important for clinical scientists to have a balanced perception of its strengths and limitations. We review several strengths of SEM, with a particular focus on recent innovations (e.g., latent growth modeling, multilevel SEM models, and approaches for dealing with missing data and with violations of normality assumptions) that underscore how SEM has become a broad data-analytic framework with flexible and unique capabilities. We also consider several limitations of SEM and some misconceptions that it tends to elicit. Major themes emphasized are the problem of omitted variables, the importance of lower-order model components, potential limitations of models judged to be well fitting, the inaccuracy of some commonly used rules of thumb, and the importance of study design. Throughout, we offer recommendations for the conduct of SEM analyses and the reporting of results.

Original languageEnglish (US)
Pages (from-to)31-65
Number of pages35
JournalAnnual Review of Clinical Psychology
Volume1
DOIs
StatePublished - 2005

Keywords

  • Causal models
  • Confirmatory factor analysis
  • Covariance structure analysis
  • Latent variables
  • Path analysis
  • Statistical modeling

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