Mediator Variable Analysis with the Hayes Process Method: An Application in the Field of Health
DOI:
https://doi.org/10.5281/zenodo.18084340Keywords:
Mediator Analysis, Hayes Process, Bootstrap, Biochemical MarkersAbstract
This study examines the role of mediators in the relationships between independent and dependent variables in health sciences, analyzing these relationships using the Hayes Process method. It also aims to demonstrate the methodological advantages of the Hayes Process over traditional methods like Baron and Kenny.
A quantitative research design was employed using an open-source dataset (Kaggle Inc: Peyman, 2020). Age was the independent variable (X), LDH was the dependent variable (Y), and CREA, KAL, ALT, NAT, PCR, GLU, AST were the mediators (M). Mediation analysis was conducted using Hayes Process Macro Model 4, with bootstrap confidence intervals (5000 samples, 95% CI) calculated for indirect effects.
The total effect of Age on LDH was significant (b=1.9449, p<0.001). When mediators were included, the direct effect of Age on LDH became non-significant (b=0.1869, p=0.5939), while the total indirect effect was significant (b=1.7580, BootLLCI=1.2689, BootULCI=2.4781). Among the mediators, PCR (b=1.1028) and AST (b=0.7110) had the strongest and most significant indirect effects.
The effect of Age on LDH is indirect, occurring through specific biochemical markers, particularly PCR and AST. The Hayes Process method with bootstrapping provided a reliable analysis without normal distribution assumptions. This study underscores the importance of mediator analysis in health sciences and demonstrates the applicability of the Hayes Process method.
Downloads
References
Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. J Pers Soc Psychol. 1986;51(6):1173-82. https://doi.org/10.1037//0022-3514.51.6.1173
Hayes AF. Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach. 2nd ed. New York: Guilford Press; 2017.
Preacher KJ, Hayes AF. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav Res Methods. 2008;40(3):879-91. https://doi.org/10.3758/BRM.40.3.879
Kaggle Inc: Peyman, MH. Covid19 blood Sample and biochemistry tests. 2020. Available from: https://www.kaggle.com/datasets/paymanmh/covid19-blood-sample-and-biochemistry-tests
Zhao X, Lynch Jr JG, Chen Q. Reconsidering Baron and Kenny: Myths and truths about mediation analysis. J Consum Res. 2010;37(2):197-206. https://doi.org/10.1086/651257
Franceschi C, Garagnani P, Parini P, Giuliani C, Santoro A. Inflammaging: a new immune–metabolic viewpoint for age-related diseases. Nat Rev Endocrinol. 2018;14(10):576-90. https://doi.org/10.1038/s41574-018-0059-4
Shrout PE, Bolger N. Mediation in experimental and nonexperimental studies: new procedures and recommendations. Psychol Methods. 2002;7(4):422-45. https://doi.org/10.1037//1082-989X.7.4.422
Downloads
Published
How to Cite
