Week 8 Moderation & Mediation Flashcards
To Provide a revision of Week 8's lecture
What is Path Analysis?
*A path analysis involves developing a theoretical model of direct and indirect paths among a set of variables, and testing which paths are significant
What is a casual sequence in Path Analysis?
A variable can affect another variable directly or indirectly through other mediator variables.
- If there is a hypothetical causal sequence of 3 (or more) variables, the middle variable is considered a mediator (indirect effect).
- Suggesting representation of at least part of the chain of events leading to changes in the DV
When might we use Path Analysis?
when you want to model observed variables through the direct and indirect effects of a mediating variable or, alternatively when you want to assess the impact (change) of the interaction of two variables on the relationship between two other variables (moderation)
Baron & Kenny (1986) have done important work on moderation & mediation variables. But what are they?
*a third variable plays an important role in governing the relationship between two other variables
What is a path diagram?
A path model is a diagram that outlines independent, intermediary, and dependent variables.
What is replacing Path Analysis in popularity?
Mediation & Moderation Path Analysis may be undertaken as a series of multiple regression equations in SPSS; although, structural equation modelling is most often used and growing in popularity as it has greater complexity (AMOS, MPlus, Liseral).
What is a mediating relationship?
- A mediating relationship attempts to identify a variable or variables through the IV, which acts to influence the DV.
- A Mediator is an indirect effect
- To “mediate” something is to stand in between two other things and pass on the effect of one to the other
What is a moderating Relationship?
A moderating relationship is one where the relationship between the IV and DV change as a function of the level of a third (Moderator) variable
What is the difference between a moderator & a mediator?
A moderator is an interaction effect
whereas a mediator is an indirect effect which impacts the DV
How does Mediation & Moderation go beyond Multiple Regression?
Generally in regression we ask a question like “Does X predict Y?” we are looking at a direct relationship
*Moderators can look at when X causes Y
*Mediators can look at how or why X causes Y
Mediators have greater complexity & thus greater explanatory power
our assumptions are initially the same as that of regular regression analysis, what are they?
*Sample size
*Outliers
*Normality, linearity, homoscedasticity.
*Multicollinearity and Singularity
*Levels of measurement – ensure reliable measures
Care needs to be taken with
1. Curvilinear relationships – ensure you check linearity
2. Interaction terms – don’t forget to centre your data (i.e. convert data to deviation scores!)
What are the additional assumptions checks required for Mediation?
- There is a significant relationship between the IV & DV
- A significant relationship between IV & mediator exists
- The mediator predicts DV after controlling for IV - HMR
- The relationship between the DV and IV is reduced when the mediator is in the equation.
What do Preacher & Hayes (2008) propose, NB: This is an opposing view to Baron & Kenny (1986)?
they propose that it is not necessary to have a significant relationship between the 3 paths (a x b = indirect effect on c)
Jose (2013) has 3 requirements for mediation to occur, what are they?
- In mediation we’re trying to gain greater depth of understanding about the Direct association or effect by checking the indirect path as well in the model.
- Researchers need to predict all 3 relationships.
- For mediation to occur, the proposed indirect path would be anticipated to reduce the strength of the “direct effect” once it is included in the model.
When is a variable confirmed as a mediator?
A variable is confirmed as a mediator if;
- There is a sig relationship between the IV & DV
- There is a sig relationship between the IV & mediator
- The mediator still predicts the DV after controlling for the IV
- The relationship between the DV & the IV is reduced when the mediator is in the equation.