A mediating variable...

• Tells how or why a relationship occurs.
• Tells a generative mechanism through which IV(s) influence DV(s).
• Specifies how a given effect occurs (Baron & Kenney, 1986).

Mediation is best done in the case of a strong relation between the predictor & criterion variable (Baron & Kenny, 1986).

Mediation generally presents when (McKinnon et al., 1995)...

1. IV significantly affects the mediator,
2. IV significantly affects the DV in the absence of the mediator,
3. Mediator has a significant unique effect on the DV, and
4. The effect of IV on DV shrinks upon the addition of the mediator to the model.

Four steps approach of assessing mediating effect (Baron & Kenny, 1986)

1. Regressing Y on X (path c).
2. Regressing M on X (path a).
3. Regressing Y on both X and M.
4. To establish that M completely mediates the X-Y relationship, the effect of X on Y controlling for M (path c') should be zero.

Baron and Kenny recommended Sobel Test to confirm the existence of indirect effect.

Critics on Baron & Kenny (Zhao et al., 2010)

1. The strength of mediation should be measured by the size of indirect effect (a x b), not by the existence of indirect effect and no direct effect.
2. There should be only one requirement to establish mediation (i.e., the indirect effect (a x b) is significant). No need for significant “effect to be mediated” (path c).
3. Sobel test is low in power compared to a bootstrap test.

Suggested Test for Mediation Model: Bootstrapping

Bootstrapping is a computerized-based statistical re-sampling technique by taking repeated samples with replacement from an original data set.

Objective: To generate estimates and standard error for hypothesis testing.

Further Readings

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(6), 1173-1182. Click here

Hayes, A. F. (2009). Beyond Baron and Kenny: Statistical mediation analysis in the new millennium. Communication Monographs, 76(4), 408-420. doi: 10.1080/03637750903310360. Click here.

Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York: The Guilford Press.

Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879-891. doi: 10.3758/BRM.40.3.879. Click here.

MacKinnon, D. P., Coxe, S., & Baraldi, A. N. (2012). Guidelines for the investigation of mediating variables in business research. Journal of Business and Psychology, 27(1), 1-14.

Zhao, X., Lynch Jr., J. G., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197-206. doi: 10.1086/651257. Click here.

Nawanir, G., Kong Teong, L., & Norezam Othman, S. (2013). Impact of lean practices on operations performance and business performance: some evidence from Indonesian manufacturing companies. Journal of Manufacturing Technology Management, 24(7), 1019-1050. Click here.

Nawanir, G., Lim, K. T., Ramayah, T., Mahmud, F., Lee, K. L., & Maarof, M. G. (2020). Synergistic effect of lean practices on lead time reduction: mediating role of manufacturing flexibility. Benchmarking: An International Journal, 27(5), 1815-1842. doi:10.1108/BIJ-05-2019-0205. Click here.