Multiple Mediation and Moderation Variables Effect and Review: Statistical Analysis with a Multiple Independent Variables

This study briefly discusses the process of mediating and moderating analysis in a management research process. The modern approaches to statistical mediation and moderation analysis focus on estimation and inference about the indirect effect of independent variables X on dependent variable Y through proposed intervening variable M and moderation variable Z. Mediators and moderators are often overlooked in research designs, or the terms are used incorrectly. To date, virtually all discussions of these approaches have assumed X is either dichotomous or continuous, even though investigators frequently are interested in testing mediation hypotheses involving multiple independent variables. The aim of the research study is to learn from past practice and to use that knowledge to signal to researchers the importance of correctly applying mediation and moderation tests as well as to facilitate the valid testing of mediation and moderation models and the reporting of mediations and moderators results in future management research studies. This research article summarizes the conceptual differences between mediators and moderators. The statistical analysis of mediators and moderators in multiple regressions is briefly described and presented. The authors describe the estimation of indirect effects in statistical mediation analysis with a multi-independent variables and moderation variables. The authors introduce the concept of the relative indirect effects, show how relative indirect effects are estimated in multiple regressions and structural equation modeling and how they are interpreted as functions of how the variables are analyzed. The authors describe inferential tests for the relative indirect effects and provide examples.