Testing the Causal Direction of Mediation Effects in Randomized Intervention Studies
- 237 Downloads
In a recent update of the standards for evidence in research on prevention interventions, the Society of Prevention Research emphasizes the importance of evaluating and testing the causal mechanism through which an intervention is expected to have an effect on an outcome. Mediation analysis is commonly applied to study such causal processes. However, these analytic tools are limited in their potential to fully understand the role of theorized mediators. For example, in a design where the treatment x is randomized and the mediator (m) and the outcome (y) are measured cross-sectionally, the causal direction of the hypothesized mediator-outcome relation is not uniquely identified. That is, both mediation models, x → m → y or x → y → m, may be plausible candidates to describe the underlying intervention theory. As a third explanation, unobserved confounders can still be responsible for the mediator-outcome association. The present study introduces principles of direction dependence which can be used to empirically evaluate these competing explanatory theories. We show that, under certain conditions, third higher moments of variables (i.e., skewness and co-skewness) can be used to uniquely identify the direction of a mediator-outcome relation. Significance procedures compatible with direction dependence are introduced and results of a simulation study are reported that demonstrate the performance of the tests. An empirical example is given for illustrative purposes and a software implementation of the proposed method is provided in SPSS.
KeywordsMediation analysis Randomized design Direction of effects Direction dependence Non-normality
No funding was received for this work.
Compliance with Ethical Standards
Conflict of Interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
Informed consent was not required for this study.
- Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Thousand Oaks, CA: Sage.Google Scholar
- Chen, H. T. (1990). Theory-driven evaluations. Newbury Park: Sage.Google Scholar
- Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.Google Scholar
- de Wit, M., & Hajos, T. (2013). Health-related quality of life. In M. D. Gellman & J. Rick Tuner (Eds.), Encyclopedia of behavioral medicine (pp. 929–931). New York, NY: Springer.Google Scholar
- Fox, J. (2008). Applied regression analysis and generalized linear models (2nd ed.). Thousand Oaks, CA: Sage.Google Scholar
- Gottfredson, D. C., Cook, T. D., Gardner, F. E., Gorman-Smith, D., Howe, G. W., Sandler, I. N., & Zafft, K. M. (2015). Standards of evidence for efficacy, effectiveness, and scale-up research in prevention science: Next generation. Prevention Science, 16, 893–926. https://doi.org/10.1007/s11121-015-0555-x.CrossRefGoogle Scholar
- Gretton, A., Fukumizu, K., Teo, C. H., Song, L., Schölkopf, B., & Smola, A. J. (2008). A kernel statistical test of independence. Advances in Neural Information Processing Systems, 20, 585–592.Google Scholar
- Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York, NY: Guilford.Google Scholar
- MacKinnon, D. P. (2008). Introduction to statistical mediation analysis. New York, NY: Erlbaum.Google Scholar
- Pearl, J. (2001). Direct and indirect effects. In Proceedings of the 17th conference in uncertainly in artificial intelligence (pp. 411–420). San Francisco, CA: Morgan Kaufmann Publishers Inc..Google Scholar
- Shimizu, S., Inazumi, T., Sogawa, Y., Hyvärinen, A., Kawahara, Y., Washio, T., Hoyer, P. O., & Bollen, K. (2011). DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model. Journal of Machine Learning Research, 12, 1225–1248.Google Scholar
- Stewart, A. L., & Ware Jr., J. E. (Eds.). (1992). Measuring functioning and well-being: The medical outcomes study approach. Durham, NC: Duke University Press.Google Scholar
- Vickers, A. J. (2006). Whose data set is it anyway? Sharing raw data from randomized trials. Trials, 7. https://doi.org/10.1186/1745-6215-7-15.
- Vickers, A. J., Rees, R. W., Zollman, C. E., McCarney, R., Smith, C. M., Ellis, N., ... & Van Haselen, R. (2004). Acupuncture for chronic headache in primary care: Large, pragmatic, randomised trial. BMJ, 328. doi:bmj.38029.421863.EB.Google Scholar
- Wiedermann, W., & Li, X. (2018). Direction dependence analysis: A framework to test the direction of effects in linear models with an implementation in SPSS. Behavior Research Methods. https://doi.org/10.3758/s13428-018-1031-x.
- Wiedermann, W., Arntner, R., & von Eye, A. (2017). Heteroscedasticity as a basis of direction dependence in reversible linear regression models. Multivariate Behavioral Research. https://doi.org/10.1080/00273171.2016.1275498.