Mediation and Moderation Analysis .pptx

Mediation and Moderation Analysis.

1. Mediation and Moderation Analysis Using Process

2. Mediation

3. Mediation Mediation is a hypothesized causal chain in which one variable (X) affects a second variable (M) which, in turn, affects a third variable (Y). Mediation implies a situation where the effect of the independent variable on the dependent variable can best be explained using a third mediator variable which is caused by the independent variable and is itself a cause for the dependent variable . Mediator is a variable that explain relationship of IV and DV. Time and order relationship between IV and Mediator. That is to say instead of X causing Y directly . X is causing the mediator M, and M is in turn causing Y.

4. The Four Paths X Y (without controlling M): path c X M : path a M Y: path b X Y (controlling M ): path c ′ Nature of Variable continuous

5. Conceptual Model M X Y

6. Statistical Model M X Y a c c’ b

7. Examples Coping Resilience Stress

8. Examples Self-esteem Grades Happiness

9. How to Identify M ediator There should be evidence in literature: IV M M DV IV DV (not necessary)

10. Baron and Kenny (1986) Conditions There should be significant correlation between independent and mediating variable ( Path a ). The mediating variable scores should correlate significantly with dependent variable scores ( Path b ). After controlling the effects of mediating variable, the correlation between independent and dependent variables is reduced indicating partial mediation. In case of occurrence of total mediation, this correlation coefficient becomes zero.

11. Total/Full and Partial M ediation Total/Full Mediation Total mediation is present when the independent variable no longer influences the dependent variable after the mediator has been controlled and c′ path is no more significant. Partial Mediation Partial mediation occurs when the independent variable’s influence on the dependent variable is reduced after the mediator is controlled and c′ path is still significant but c’ is smaller in absolute value than c.

12. Hayes (2018) Conditions Significant indirect Effect IV M DV

13. Multiple Mediators Parallel mediation Serial mediation

14. Moderation

15. Moderation A moderator is a variable that specifies conditions under which a given predictor is related to an outcome. Moderator change the nature of relationship. The moderator explains ‘ when ’ a DV and IV are related. Moderation implied an interaction effect, where introducing a moderating variable changes the direction or magnitude of the relationship between two variables.

16. Moderation Effects (a) Enhancing , where increasing the moderator would increase the effect of the predictor (IV) on the outcome (DV ); ( b) Buffering , where increasing the moderator would decrease the effect of the predictor on the outcome; or ( c) Antagonistic /exacerbating , where increasing the moderator would reverse the effect of the predictor on the outcome.

17. Conceptual Model X Y M

18. Statistical Model Y X M XM b1 b2 b3

19. Example Conceptual Model Stress Depression Social Support

20. Example Statistical Model Depression Stress Social Support Stress*Social Support b1 b2 b3

21. How to Identify Moderator Not required literature evidence T ime and order relationship not required Any predictor of DV

22. Main Effect & Interaction Effect Main Effect IV DV M DV Interaction Effect IV*M DV