Open AccessUsing factorial mediation analysis to better understand the effects of interventions
Author(s) -
Jillian C Strayhorn,
Linda M. Collins,
Timothy R. Brick,
Sara H. Marchese,
Angela Fidler Pfammatter,
Christine A. Pellegrini,
Bonnie Spring
Publication year - 2021
Publication title -
translational behavioral medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.819
H-Index - 39
eISSN - 1869-6716
pISSN - 1613-9860
DOI - 10.1093/tbm/ibab137
Subject(s) - mediation , psychological intervention , psychology , health psychology , factorial , cross cultural psychology , social psychology , public health , medicine , sociology , mathematics , social science , psychiatry , mathematical analysis , nursing
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.