3: Mediating effects Flashcards

1
Q

What is the rationale of mediation?

A
  • Although an IV may have an effect on the DV, this effect is sometimes indirect; mediated through another variable.
  • Whether there is a mediating variable relationship depends on theory (i.e. is it justified to expect that the effect is mediated?)
  • In general, the mediating variable helps us understand the process through which the independent variable produces the outcome.
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2
Q

What are linear models?

A

Simplified models/representations of reality, contains errors but can still be interpreted

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3
Q

With what variables is the indirect effect stated for mediation?

A

a*b

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4
Q

With what variable is the direct effect stated for mediation?

A

c’

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5
Q

How is the total effect stated for mediation?

A

c = c’ + ab

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6
Q

What is full mediation?

A

When the indirect effect is significant (both a and b), but the direct effect is not significant (c´).

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7
Q

What is partial mediation?

A

When the indirect effect is significant (both a and b), and the direct effect is significant but has lower effect size than when the mediating variable is not in the model.

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8
Q

What are the two procedures to investigate mediating effects?

A
  • Baron and Kenny.
  • Preacher and Hayes.
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9
Q

What are the four steps for investigating conditions for mediation as outlined by Baron and Kenny?

A
  1. Regress Y on X.
    Show that the independent variable significantly predicts the dependent variable.
  2. Regress M on X.
    Show that the independent variable significantly predicts the mediator.
  3. Regress Y on M and X.
    Show that the mediator significantly predicts the dependent variable while controlling for the independent variable.
  4. Check the significance of X on Y in step 3.
    If the independent variable becomes no longer significant in step 3, there is full mediation.
    If the independent variable is still significant (but has a lower effect size) in step 3, there is partial mediation.
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10
Q

What are the three steps for investigating conditions for mediation as outlined by Preacher and Hayes?

A
  1. Regress Y on M and X.
    Show that the mediator significantly predicts the dependent variable while controlling for the independent variable.
  2. Regress M on X.
    Show that the independent variable significantly predicts the mediator.
  3. Check the significance of the mediated effect using bootstrapping.

Bootstrapping = sampling your sample (creating the sample distribution).

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11
Q

What is the Sobel test?

A

The Sobel test calculates a z-score. We can use it to compare the effect in a model with and without mediation.

The mediating effect is significant when p-value < 0.05, which is when z-score >1.96

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12
Q

What are some problems with the Sobel test?

A
  • Depends on distributional assumptions.
  • The distribution of effect is normal only with a large sample size.
  • The resulting p-value is not correct
    Proposed solution: Using a non-parametric approach - bootstrapping.
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13
Q

What is ACME and ADE?

A

From Preacher and Hayes method.

ACME: Average Causal Mediation Effect. Indirect effect = ab

ADE: Average Direct Effect. Direct effect = c’

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14
Q

What is bootstrapping?

A

Using our sample to create more samples.

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15
Q

What is a limitation of using Baron/Kenny and the Sobel test for mediation?

A

They assume normality of the distribution of the indirect effect. It has been shown that this assumption is rarely met in practice. Hence, our p-value will probably be too small (too significant).

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