Moderation Flashcards
variable that specifies conditions under which a given predictor is related to an outcome
moderator
Answers the question, “when?”
moderator
implies an interaction effect , where introducing a moderating variable changes the direction or magnitude of the relationship between two variables.
moderation
(a) Enhancing
(b) Buffering
(c) Antagonistic
moderation analysis (effect)
where increasing the moderator would increase the effect of the predictor (IV) on the outcome (DV).
Enhancing
where increasing the moderator would decrease the effect of the predictor on the outcome
what effect
Buffering
where increasing the moderator(m) would reverse the effect of the predictor(x) on the outcome(y).
what effect
Antagonistic
“especially if” (semantics) “depending on”
moderation analysis
IV is continuous
DV is continuous
MV is continuous OR categorical
moderation analysis
in moderation analysis IV is
continuous
predictor
x
in moderation analysis DV is
continuous
y
outcome
in moderation analysis MV is
continuous or categorical
M
MODERATOR
if IV is CATEGORICAL, use _
ANOVA
If DV is CATEGORICAL use _
LOGISTIC REGRESSION
Step 1: Estimate the interaction effect
Step 2: Statistical inference test
Step 3: If interaction is significant, then probe the interaction by doing a simple slopes analysis (or cheat sheet)
Sample
Moderation
Analysis Steps
used to assess the effects of a
moderating variable
Hierarchical multiple regression
To test moderation, we will, in particular, be looking at the _ effect between X and M and whether or not such an effect is significant in predicting Y
Interaction
effect between X and M and it’s significant
Hierarchical multiple regression
X is what variable
Independent Variable
Y is what variable
Dependent Variable
M is what variable
Moderator Variable
• Main Effects are Present
• Lack of Interaction
• Consistent Relationship
• Caution in Interpretation
Possible Conclusions in Moderation Analysis
The significant simple slopes suggest that there are meaningful relationships between the predictor and the outcome at different levels of the moderator.
This means that predictor consistently affects the outcome, regardless of the level of the moderator.
Main Effects are Present
This _ simple slopes suggest that there are meaningful relationships between the predictor and the outcome at different levels of the moderator. This means that predictor consistently affects the ourcome, regardless of the level of the moderator.
significant
The non-significant interaction effect implies that the strength or direction of the relationship between predictor (IV) and outcome(DV) does not significantly change across different levels of the moderator.
In other words, while the relationship exists, it is not influenced by the moderator to a degree that is statistically significant
Lack of Interaction