Teacher’s Corner: An R Shiny App for Sensitivity Analysis for Latent Growth Curve Mediation

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

Research output: Contribution to journalArticlepeer-review

Abstract

Mechanisms of behavior change are the processes through which interventions are hypothesized to cause changes in outcomes. Latent growth curve mediation models (LGCMM) are recommended for investigating the mechanisms of behavior change because LGCMM models establish temporal precedence of change from the mediator to the outcome variable. The Correlated Augmented Mediation Sensitivity Analyses (CAMSA) App implements sensitivity analysis for LGCMM models to evaluate if a mediating path (mechanism) is robust to potential confounding variables. The CAMSA approach is described and applied to simulated data, and data from a research study exploring a mechanism of change in the treatment of substance use disorder.

Original languageEnglish (US)
JournalStructural Equation Modeling
DOIs
StateAccepted/In press - 2022

Keywords

  • Behavior change
  • confounding
  • latent growth curve mediation modeling
  • mediation
  • sensitivity analyses

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 'Teacher’s Corner: An R Shiny App for Sensitivity Analysis for Latent Growth Curve Mediation'. Together they form a unique fingerprint.

Cite this