Using factorial mediation analysis to better understand the effects of interventions

Jillian C. Strayhorn*, Linda M. Collins, Timothy R. Brick, Sara H. Marchese, Angela Fidler Pfammatter, Christine Pellegrini, Bonnie Spring

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

To improve understanding of how interventions work or why they do not work, there is need for methods of testing hypotheses about the causal mechanisms underlying the individual and combined effects of the components that make up interventions. Factorial mediation analysis, i.e., mediation analysis applied to data from a factorial optimization trial, enables testing such hypotheses. In this commentary, we demonstrate how factorial mediation analysis can contribute detailed information about an intervention's causal mechanisms. We briefly review the multiphase optimization strategy (MOST) and the factorial experiment. We use an empirical example from a 25 factorial optimization trial to demonstrate how factorial mediation analysis opens possibilities for better understanding the individual and combined effects of intervention components. Factorial mediation analysis has important potential to advance theory about interventions and to inform intervention improvements.

Original languageEnglish (US)
Pages (from-to)84-89
Number of pages6
JournalTranslational behavioral medicine
Volume12
Issue number1
DOIs
StatePublished - Jan 1 2022

Keywords

  • Factorial experiment
  • Mediation analysis
  • Multiphase optimization strategy
  • Optimization trial

ASJC Scopus subject areas

  • Applied Psychology
  • Behavioral Neuroscience

Fingerprint

Dive into the research topics of 'Using factorial mediation analysis to better understand the effects of interventions'. Together they form a unique fingerprint.

Cite this