Wk 8 - Mediation Flashcards
What is mediation used for? (x2)
To characterise indirect effect of predictor on outcome, via 3rd variable
To explore/infer causal factors underlying an effect
Describe the case of Pellagra in US, early 1900s (x4)
Sores, lethargy, vomiting, diarrhea killed 11Ks/yr
Common in prisoners/mental patients, but not guards/nurses
Linked to poverty/poor sewerage/germ, but mediated by diet
*Could be cured with balanced food/higher protein
Describe Baron & Kenny’s Causal Steps approach to mediation (x4)
Path A: effect of Predictor on Mediator
Path B: Mediator on Outcome
Path C is Total effect of Predictor/all Mediators on Outcome
Path C’: Is Path C removed/reduced when AxB is accounted for?
What 4 key regression analyses are involved in the Causal Steps approach to mediation?
Path C: Outcome = b times Predictor + c
Path A: Mediator = b times Predictor + c
Path B: Outcome = b times Mediator + c
Path C’: Outcome =b1 x Predictor + b2 times Mediator + c
How do we interpret Path C’ in Causal Steps approach to mediation? (x2)
If significantly difference form 0, argue partial mediation
If not, argue for full
What 2 ways can the strength of the indirect effect be calculated in Causal Steps approach to mediation?
Path A x Path B
Path C - Path C’
How is the indirect effect tested for significance in Causal Steps approach tomediation?
Sobel test:
Indirect Effect is divided by SE to give z-score
Which is compared to z-distribution (+/- 1.96)
What are 3 limitations of the Causal Steps approach to mediation?
Low power
Traditionally requires significant Path C
Sobel test assumes normal distribution of mediation effects
Explain how low power is a limitation of Causal steps approach to mediation (x2)
Huge samples/large effects needed to detect mediation
So only really practical when testing large effects
Explain how the requirement of a significant Path C limits the Causal Steps approach to mediation (x3)
But it should be remembered that… (x1)
Suppression effect if Path A and B are positive/negative
(Or multiple indirect effects)
‘Cancel out’ Path C when added to C’
Logically, Path C doesn’t need to significant for an indirect pathway to exist
Explain how the Sobel test assumption of normality limits the Causal steps approach to mediation (x3)
z-test assumes normal sampling distribution of indirect effects
But smaller samples often notably skewed/kurtotic
Reducing diagnostic power of test
What statistical technique addresses the limitations of the Causal steps approach to mediation? (x1)
Bootstrapping
Define bootstrapping (x2)
Resampling technique used to estimate the variability of a sample statistic
Mean, standard deviation, etc. …
How does bootstrapping address the limitations of the Causal steps approach to mediation? (x2, x2)
Minimal assumptions
*That sample data represent the population
Recovers important properties of sampling distribution of a statistic
*ie, shape/spread of distribution
How is bootstrapping relevant to mediation analysis? (x2, x3)
Estimates sampling distribution of indirect effect
Allowing inference about magnitude/significance of it
Data is resampled eg 5000 times Summary stats (indirect effect)/mediation model computed for each resample Giving estimate of variability of indirect effect
How does bootsrapping resample data? (x3)
With replacement:
Randomly selected individual’s data copied to resample
And then they’re put back in the pool
What use to us is the estimates sampling distribution of the indirect effect in bootstrapped mediation? (x2)
Which we interpret by asking… (x2)
Can compute 95% CI
Of plausible estimates of indirect effect
Does this include zero?
*If so, no significant mediation
What are 3 main benefits of bootstrapping for mediation? (x2, x2, x1)
Higher power
*Practical sample sizes for small/medium effect sizes
Gives estimate of variability of indirect approach
*Rather than ‘point estimate’ of Causal steps
Doesn’t erroneously assume normal sampling distribution of indirect effect
As SPSS doesn’t provide bootstrapped mediation function, we… (x1)
Use Hayes’ Process macro
What kind of variables can be used in the Process macro?
Predictors and outcomes can be continuous or categorical
Mediators must be continuous
What output does Process give for mediation? (x3)
Results for each regression in Causal steps method
Bootrapped indirect effect results
Sobel test for indirect effects
What Causal steps regression mediation results are produced by Process? (x2)
- b and t-test for each path
* Fit stats (R-square and F-test for each regression model)
What bootstrapping results does Process output for mediation? (x3)
- Original sample indirect effect
- CIs for indirect effects in model
- CIs for contrasts of multiple effects
Why might we want to consider multiple indirect effects? (x5)
Maybe theoretically interesting/important
Look at total of combined effects
Examine impact of individual indirect effects
Compare magnitude of indirect effects
Effects of 1 mediator may change with presence of another
