Abstract
This article provides researchers with a guide to properly construe and conduct analyses of conditional indirect effects, commonly known as moderated mediation effects. We disentangle conflicting definitions of moderated mediation and describe approaches for estimating and testing a variety of hypotheses involving conditional indirect effects. We introduce standard errors for hypothesis testing and construction of confidence intervals in large samples but advocate that researchers use bootstrapping whenever possible. We also describe methods for probing significant conditional indirect effects by employing direct extensions of the simple slopes method and Johnson-Neyman technique for probing significant interactions. Finally, we provide an SPSS macro to facilitate the implementation of the recommended asymptotic and bootstrapping methods. We illustrate the application of these methods with an example drawn from the Michigan Study of Adolescent Life Transitions, showing that the indirect effect of intrinsic student interest on mathematics performance through teacher perceptions of talent is moderated by student math self-concept.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 185-227 |
| Number of pages | 43 |
| Journal | Multivariate Behavioral Research |
| Volume | 42 |
| Issue number | 1 |
| DOIs | |
| State | Published - 2007 |
Funding
This work was funded in part by National Institute on Drug Abuse Grant DA16883 awarded to the first author while at the University of North Carolina at Chapel Hill. We thank Li Cai for valuable input regarding derivations in the Technical Appendix, Stephanie Madon and Courtney Stevens for help with the applied example, and Daniel J. Bauer for helpful advice in improving the manuscript. The SPSS macro syntax is available online through http://www.quantpsy.org/.
ASJC Scopus subject areas
- Experimental and Cognitive Psychology
- Arts and Humanities (miscellaneous)
- Statistics and Probability