Utilizing Moderation Analysis to Understand Construction Activity's Impact on Pollution: Insights from My Doctoral Research

         Moderation is a statistical concept employed in quantitative research to examine how much a third variable influences the relationship between two variables. The third variable (the moderator variable) can be categorical (e.g., gender, race) or continuous (e.g., age, income). Understanding moderation allows researchers to explore how the relationship between variables is affected by other factors (Memon et al., 2019).

Using moderation in my project:

        In my doctoral project on construction pollution, I can utilize moderation analysis to investigate how various factors influence the relationship between construction activity and pollution levels. These factors may include the type of construction, location of the construction, or weather conditions. For instance, I could propose that the relationship between construction activity and pollution levels is more robust in urban areas than in rural areas due to increased population, business activities, traffic, and construction. To test this hypothesis, I would collect data on construction activity, pollution levels, and the location of construction sites (urban or rural) in a sample of cities (Lou et al., 2023). Through statistical analysis using moderation techniques, I would determine if the relationship between construction activity and pollution levels is more robust in urban areas.

Example in my research:

          I could investigate how factors like the type of construction or weather conditions influence the relationship between construction activity and pollution levels. For example, I might hypothesize that construction projects involving heavy machinery (e.g., bulldozers, excavators) have a more substantial impact on pollution levels. Similarly, I could explore whether the relationship intensifies during high winds, which can carry pollutants over greater distances. By examining these and other factors, I can enhance my understanding of the causes of construction pollution and develop strategies to mitigate its effects.

Mediation vs. Moderation:

          According to Jose (2013). we can conclude the following:

Mediation

Moderation

Explains how a relationship between two variables occurs

Explains when a relationship between two variables occurs

Involves a causal pathway between the IV and the DV

Involves an interaction between the IV and the moderator variable

The mediator variable is a variable that is affected by the IV, and that affects the DV

The moderator variable is a variable that affects the strength or direction of the relationship between the IV and the DV

Conclusion:

         Moderation analysis is a valuable tool for comprehending the intricate relationship between variables. By applying moderation techniques in my doctoral research project, I can gain insights into the factors influencing construction pollution. This understanding will enable the development of strategies to reduce pollution and improve air quality. Ultimately, incorporating moderation analysis allows researchers to delve deeper into how different factors interact with the dependent variable, creating effective interventions and programs that benefit individuals and communities (MacKinnon, 2012).

References

Jose, P. E. (2013). Doing statistical mediation and moderation. Guilford Press.

https://www.guilford.com/books/Doing-Statistical-Mediation-and-Moderation/Paul-Jose/9781462508150

Lou, X., Zhi, F., Sun, X., Wang, F., Hou, X., Lv, C., & Hu, Q. (2023). Construction of co-immobilized laccase and mediator based on MOFs membrane for

enhancing organic pollutants removal. Chemical Engineering Journal, 451, 138080.

MacKinnon, D. P. (2012). Introduction to statistical mediation analysis. Routledge.

https://www.routledge.com/Introduction-to-Statistical-Mediation-Analysis/MacKinnon/p/book/9780805864298

Memon, M. A., Cheah, J. H., Ramayah, T., Ting, H., Chuah, F., & Cham, T. H. (2019). Moderation analysis: issues and guidelines. Journal of Applied

Structural Equation Modeling, 3(1), 1-11.

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