Module 2 Flashcards

1
Q

According to Dr Levesque what types of questions are moderation and mediation useful for?

A
  • Moderation: Useful for When Questions
    • Under what conditions does X influence Y?
    • Explores the boundary conditions of associations
  • Mediation: Useful for How/Why Questions
    • How does X influence Y?
    • Explores causal factors and mechanisms of associations
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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
  • 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
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3
Q

What is Mediation?

A
  • Examines how variables are causally related (also known as path analysis)
    • 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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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
  • In SPSS: Select conditioning -1SD, Mean, 1SD
    • Results found under “Conditional Effects of X on Y” section of output
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5
Q

What is the difference between direct, total and indirect effects in a mediation analysis?

A
  • Total Effect: Simple relationship between X and Y without M included in model
    • Represented by c
  • Direct Effect of X on Y: Effect of X on Y when M is included in model
    • Represented by c’
  • 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)
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6
Q

How do you run a moderation analysis in SPSS?

A
  • 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
  • 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
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7
Q

How do you run a Mediation analysis through SPSS?

A
  • Analyse -> Regression -> Process by Hayes
    • Model 4 - mediation
    • Outcome = Y variable, Predictor = X, Mediator = mediator
    • Tick Effect size, Pairwise, and Total Model
  • 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
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8
Q

How can you check for normality and missing data?

A
  • Analyse -> Descriptives -> Frequencies
    • Will show you any missing data, range and sample size
  • 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
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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
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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.
  • Multicollinearity
    • Tolerance > 1 and VIF < 10 indicates no multicollinearity
    • Double check by verifying the correlations
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