Sensitivity Analysis of the No-Omitted Confounder Assumption in Latent Growth Curve Mediation Models

Davood Tofighi, Yu Yu Hsiao, Eric S. Kruger, David Mackinnon, M. Lee Van Horn, Katie Witkiewitz

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


Latent growth curve mediation models are increasingly used to assess mechanisms of behavior change. For latent growth mediation model, like any another mediation model, even with random treatment assignment, a critical but untestable assumption for valid and unbiased estimates of the indirect effects is that there should be no omitted variable that confounds indirect effects. One way to address this untestable assumption is to conduct sensitivity analysis to assess whether the inference about an indirect effect would change under varying degrees of confounding bias. We developed a sensitivity analysis technique for a latent growth curve mediation model. We compute the biasing effect of confounding on point and confidence interval estimates of the indirect effects in a structural equation modeling framework. We illustrate sensitivity plots to visualize the effects of confounding on each indirect effect and present an empirical example to illustrate the application of the sensitivity analysis.

Original languageEnglish (US)
Pages (from-to)94-109
Number of pages16
JournalStructural Equation Modeling
Issue number1
StatePublished - Jan 2 2019


  • Multiple mediation analysis
  • correlated augmented model
  • indirect effect
  • latent growth curve
  • sensitivity analysis

ASJC Scopus subject areas

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


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