Alwielland Q. Bello Before proceeding with mediation analysis, it’s essential to first assess whether the mediator variable is significantly influencing the relationship between the predictor and the outcome. This preliminary check ensures that mediation is appropriate for your model.
For guidance on how to conduct this analysis, you can refer to the JASP software manual, which provides detailed instructions on mediation analysis.
Two main methods for performing mediation analysis are structural equation modeling (SEM) and regression-based approaches. A highly recommended resource for regression-based mediation analysis is Hayes (2022), which provides detailed explanations and illustrative examples. The basic mediation model studies how an independent variable (X) affects an outcome (Y) through a mediator variable (M). Hayes (2022) discusses variations of this model, including multiple mediators (both parallel and sequential) and moderated mediation. Hayes also developed the PROCESS macro, compatible with SAS, SPSS, and R, to facilitate mediation analysis.
This approach is widely used and has been effectively applied in various research studies. In fact, in our recent study, titled “Do You See My Effort? An Investigation of the Relationship Between E-Government Service Quality and Trust in Government,” we applied a sequential mediation analysis using the PROCESS macro to examine how e-government service quality influences trust in government through intermediary variables.
References:
Hayes, A. F. (2022). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford Publications.
Alsarraf, H. A., Aljazzaf, S., & Ashkanani, A. M. (2022). Do you see my effort? An investigation of the relationship between e-government service quality and trust in government. Transforming Government: People, Process and Policy, 17(1), 116–133.
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