Mehmet Sıddık Çetinel, Sadi Elasan
Neuro-Cell Molecular Research - 2025;2(3):80-83
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.
