Module 2 Flashcards
1
Q
According to Dr Levesque what types of questions are moderation and mediation useful for?
A
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Moderation: Useful for When Questions
- Under what conditions does X influence Y?
- Explores the boundary conditions of associations
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Mediation: Useful for How/Why Questions
- How does X influence Y?
- Explores causal factors and mechanisms of associations
2
Q
What is Moderation?
A
- Moderation is a regression format of an interaction effect
- Is the effect of X on Y moderated by M?
- Note: M can also have a direct effect on Y
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Centering Variables: Continuous variables need to be mean centered to avoid multicollinearity
- MC by subtracting the mean from the score (new mean = 0)
- Interaction Variables: Created by multiplying the MC scores
3
Q
What is Mediation?
A
- Examines how variables are causally related (also known as path analysis)
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Size is measured using R2 of the direct pathway compared to R2 of the direct effect when M is included.
- Partial vs Complete/perfect mediator
- M is both an antecedent and consequent variable
- In modern models paths a and c don’t need to be significant
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Size is measured using R2 of the direct pathway compared to R2 of the direct effect when M is included.
4
Q
What is Simple Slopes Analysis?
A
- Simple Slopes Analysis provides information about the interaction effect in a moderation analysis
- Creates 3 regression lines for X - Y at low, mean, and high M values
- Parallel = no interaction
- If high M sharper slope than low M = M increases effect of X on Y
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In SPSS: Select conditioning -1SD, Mean, 1SD
- Results found under “Conditional Effects of X on Y” section of output
5
Q
What is the difference between direct, total and indirect effects in a mediation analysis?
A
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Total Effect: Simple relationship between X and Y without M included in model
- Represented by c
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Direct Effect of X on Y: Effect of X on Y when M is included in model
- Represented by c’
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Indirect Effect of X on Y: Effect of X on Y via M
- Product of a and b pathways
- Product will be positive if a and b have same sign (even if both negative)
- Significance tested using confidence interval (if includes 0 not significant)
6
Q
How do you run a moderation analysis in SPSS?
A
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Analysis -> Regression -> Process by Hayes
- Model 1 = Moderation
- Outcome = Y variable, Predictor = X variable, Moderator = Moderator W
- Select HC3 (Davidson MacKinnon) for Heteroscadescity
- Tick Generate Code, Mean Center
- Select -1SD, Mean, 1SD for Conditioning and tick Johnson Neyman
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Interpreting Output
- INT_1 = Interaction term - if this is significant then there is a moderation effect
- Check whether confidence intervals include 0
- Check Simple Slopes under Conditional effects
7
Q
How do you run a Mediation analysis through SPSS?
A
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Analyse -> Regression -> Process by Hayes
- Model 4 - mediation
- Outcome = Y variable, Predictor = X, Mediator = mediator
- Tick Effect size, Pairwise, and Total Model
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Interpreting the output - is the indirect effect significant?
- 1st model shows effect of predictor on mediator
- 2nd model shows combined effect of predictor and mediator on outcome
- Under Total, Direct and Indirect effects
- Check indirect effect of X on Y -> if the BOOT confidence intervals do not include 0 it is significant
8
Q
How can you check for normality and missing data?
A
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Analyse -> Descriptives -> Frequencies
- Will show you any missing data, range and sample size
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Analyse -> Descriptives -> Explore
- Check Skewness and Kurtosis values
- Close to 0 values indicate normality, positive = leptokurtic, +skew
- Check Graphs of Normality
- Finally check the Shapiro Wilk (significant value = not normal)
- Make a educated judgement
- Check Skewness and Kurtosis values
9
Q
How do you check for outliers?
A
- Run a linear regression
- Select Statistics, casewise diagnostics, MAH and Cook Distances
- Z-scores; Check for any z-score > 3SD
- Univariate: Check Casewise Diagnostics
- If the table doesn’t appear there are no univariate outliers that need to be worried about
- Multivariate: Check Mah and Cook values against critical
- Mah < 13 is good for 2 IVs (indicates no major outliers)
- Cook < 1 indicates no one response is too influential
10
Q
How do you check for linearity, homoscedasticity, independence and multicollinearity?
A
- Run regression - select Dyan Watson, Collinearity, Zpre-Zresid plot, Normality PLot
- Zpred-Zresid Plot: assesses linearity and homescedascity
- look for a random distribution
- Durbin Watson; assesses independence of errors
- Value between 1-3 is good. Closer to 2 the better.
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Multicollinearity
- Tolerance > 1 and VIF < 10 indicates no multicollinearity
- Double check by verifying the correlations