Testing the Causal Direction of Mediation Effects in Randomized Intervention Studies
Tóm tắt
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.
Tài liệu tham khảo
Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Thousand Oaks, CA: Sage.
Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173.
Bullock, J. G., Green, D. P., & Ha, S. E. (2010). Yes, but what’s the mechanism? (Don’t expect an easy answer). Journal of Personality and Social Psychology, 98, 550–558. https://doi.org/10.1037/a0018933.
Cain, M. K., Zhang, Z., & Yuan, K. H. (2017). Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation. Behavior Research Methods, 49, 1716–1735. https://doi.org/10.3758/s13428-016-0814-1.
Chen, H. T. (1990). Theory-driven evaluations. Newbury Park: Sage.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
Davison, A. C., & Hinkley, D. V. (1997). Bootstrap methods and their application. Cambridge, UK: Cambridge University Press.
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.
Dodge, Y., & Rousson, V. (2000). Direction dependence in a regression line. Communications in Statistics: Theory and Methods, 29, 1957–1972. https://doi.org/10.1080/03610920008832589.
Farahani, M. A., & Assari, S. (2010). Relationship between pain and quality of life. In V. R. Preedy & R. R. Watson (Eds.), Handbook of disease burdens and quality of life measures (pp. 3933–3953). New York, NY: Springer.
Fox, J. (2008). Applied regression analysis and generalized linear models (2nd ed.). Thousand Oaks, CA: Sage.
Gelfand, L. A., Mensinger, J. L., & Tenhave, T. (2009). Mediation analysis: A retrospective snapshot of practice and more recent directions. Journal of General Psychology, 136, 153–178. https://doi.org/10.3200/GENP.136.2.153-178.
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.
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.
Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York, NY: Guilford.
Huang, F. L. (2016). Alternatives to multilevel modeling for the analysis of clustered data. Journal of Experimental Education, 84, 175–196. https://doi.org/10.1080/00220973.2014.952397.
Hyvärinen, A., Karhunen, J., & Oja, E. (2001). Independent components analysis. New York, NY: Wiley & Sons.
Iacobucci, D., Saldanha, N., & Deng, X. (2007). A meditation on mediation: Evidence that structural equations models perform better than regressions. Journal of Consumer Psychology, 17, 139–153. https://doi.org/10.1016/S1057-7408(07)70020-7.
Imai, K., Keele, L., & Yamamoto, T. (2010). Identification, inference and sensitivity analysis for causal mediation effects. Statistical Science, 5, 1–71. https://doi.org/10.1214/10-sts321.
Imai, K., Keele, L., Tingley, D., & Yamamoto, T. (2011). Unpacking the black box of causality: Learning about causal mechanisms from experimental and observational studies. American Political Science Review, 105, 765–789. https://doi.org/10.1017/S0003055411000414.
MacKinnon, D. P. (2008). Introduction to statistical mediation analysis. New York, NY: Erlbaum.
Micceri, T. (1989). The unicorn, the normal curve, and other improbable creatures. Psychological Bulletin, 105, 156–166. https://doi.org/10.1037/0033-2909.105.1.156.
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..
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.
Shrout, P. E., & Bolger, N. (2002). Mediation in experimental and nonexperimental studies: New procedures and recommendations. Psychological Methods, 7, 422–445. https://doi.org/10.1037//1082-989x.7.4.422.
Stelzl, I. (1986). Changing the causal hypothesis without changing the fit: Some rules for generating equivalent path models. Multivariate Behavioral Research, 21, 309–331. https://doi.org/10.1207/s15327906mbr2103_3.
Stewart, A. L., & Ware Jr., J. E. (Eds.). (1992). Measuring functioning and well-being: The medical outcomes study approach. Durham, NC: Duke University Press.
Székely, G. J., Rizzo, M. L., & Bakirov, N. K. (2007). Measuring and testing dependence by correlation of distances. Annals of Statistics, 35, 2769–2794. https://doi.org/10.1214/009053607000000505.
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.
von Eye, A., & DeShon, R. P. (2012). Directional dependence in developmental research. International Journal of Behavioral Development, 36, 303–312. https://doi.org/10.1177/0165025412439968.
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., & von Eye, A. (2015a). Direction of effects in mediation analysis. Psychological Methods, 20, 221–244. https://doi.org/10.1037/met0000027.
Wiedermann, W., & von Eye, A. (2015b). Direction-dependence analysis: A confirmatory approach for testing directional theories. International Journal of Behavioral Development, 39, 570–580. https://doi.org/10.1177/0165025415582056.
Wiedermann, W., & von Eye, A. (2016). Directionality of effects in causal mediation analysis. In W. Wiedermann & A. von Eye (Eds.), Statistics and causality: Methods for applied empirical research (pp. 63–106). Hoboken, NJ: Wiley and Sons.
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.