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
The _ 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
non-significant
Since the simple slopes are significant at both low and high levels of the moderator, you can conclude that the predictor’s effect on the outcome is relatively stable, even if it varies slightly in magnitude.
Consistent Relationship
While the simple slopes analysis provides insights into the effect at specific levels of the moderator, the lack of a significant interaction means that one should be cautious about overinterpreting the differences in slopes as indicating a nuanced moderating effect.
Caution in Interpretation
The effect of IQ on reading is not moderated by method of
teaching. What hypotheses?
Ho
The effect of IQ on reading is moderated by method of teaching. What hypotheses?
Ha
Step 1: Estimate the interaction effect - use
ANOVA
Step 2: Statistical inference test - check the
significance
Step 2: Statistical inference test - check the look at the _ (interaction effect)
p-value or F
(interaction effect)
p-value or F, below the alpha level (<.05),
then it is
significant
it is much better if the p-value is
decreasing
is a follow-up procedure to the hierarchical regression
slope analysis
Step 3: If interaction is significant, then
probe the interaction by doing a _ analysis (or cheat sheet).
○ compute values that are higher or
lower
simple slopes analysis
actual value, the impact of IV to
DV
Estimates
estimate increase = _ if p-
value is small
significant
● the intersection of slopes is significant
● only a secondary analysis
Simple Slope Analysis
To test the hypothesis that [IV] affects [DV] , and whether [M] moderates the relationship between [IV] and [DV] , a hierarchical multiple regression analysis was conducted. Results show that IV and [M] (B = ___, p = ___) have [in]significant main effects on [DV]. The interaction between [IV] and [M] is [also] [in]significant (B =___, p = ___). Furthermore, it is indicated that IV [do not] leads to DV depending on the presence of M.
Reporting Results
Format for Moderation:
Simple Slopes Analysis indicate that when M scores are high (B = ___, p = ___), the effect of IV on DV is _______. On the other hand, when M scores are low (B = ___, p=___), the effect of the IV on the DV is _______.
Format for Simple Slopes Analysis
