Lecture slides stats1.13.l14.air

Statistics One

Lecture 14 Mediation

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Two segments

•  Standard approach •  Path analysis

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Lecture 14 ~ Segment 1

Mediation: Standard approach

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Mediation

•  Mediation and moderation may sounds alike but they are quite different – Moderation (Lecture 13) – Mediation (Lecture 14) – Both demonstrated in R (Lab 7)

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Mediation

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Mediator

An example

•  X: Experimental manipulation – Stereotype threat

•  Y: Behavioral outcome –  IQ score

•  M: Mediator (Mechanism) – Working memory capacity (WMC)

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Mediation

•  A mediation analysis is typically conducted to better understand an observed effect of an IV on a DV or a correlation between X and Y

•  Why, and how, does stereotype threat influence IQ test performance?

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Mediation

•  If X and Y are correlated then we can use regression to predict Y from X

•  Y = B0 + B1X + e

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Mediation

•  If X and Y are correlated BECAUSE of the mediator M, then (X à M à Y):

•  Y = B0 + B1M + e & •  M = B0 + B1X + e

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Mediation

•  If X and Y are correlated BECAUSE of the mediator M, and:

•  Y = B0 + B1M + B2X + e •  What will happen to the predictive value of X •  In other words, will B2 be significant?

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Mediation

•  A mediator variable (M) accounts for some or all of the relationship between X and Y –  Some: Partial mediation –  All: Full mediation

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Mediation

•  CAUTION! –  Correlation does not imply causation! –  In other words, there is a BIG difference between

statistical mediation and true causal mediation

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How to test for mediation

•  Run three regression models •  lm(Y ~ X) •  lm(M ~ X) •  lm(Y ~ X + M)

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•  Run three regression models •  lm(Y ~ X)

–  Regression coefficient for X should be significant

•  lm(M ~ X) –  Regression coefficient for X should be significant

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•  Run three regression models •  lm(Y ~ X + M)

–  Regression coefficient for M should be significant –  Regression coefficient for X?

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Back to the example

•  X: Experimental manipulation – Stereotype threat

•  Y: Behavioral outcome –  IQ score

•  M: Mediator (Mechanism) – Working memory capacity (WMC)

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Simulated experiment & data

•  Students randomly assigned to one of two experimental conditions – Threat – Control

•  Students completed a working memory task •  Students completed an IQ test

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Results

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Results Control group Threat group

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Interpretation

•  Full mediation – The direct effect is no longer significant after

adding the mediator into the regression equation

– The Sobel test is significant

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END SEGMENT

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Lecture 14 ~ Segment 2

Mediation: Path analysis approach

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Mediation

•  Mediation analyses are typically illustrated using “path models”

•  Rectangles: Observed variables (X, Y, M) •  Circles: Unobserved variables (e) •  Triangles: Constants •  Arrows: Associations (more on these later)

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Path model

•  Y = B0 + B1X + e

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X Y e

1 B0 B1 1

Path model with a mediator

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X Y e

1 B0 B1

M e

1

1


•  To avoid confusion, let’s label the paths •  a: Path from X to M •  b: Path from M to Y •  c: Direct path from X to Y (before including M) •  c’: Direct path from X to Y (after including M) •  Note: (a*b) is known as the indirect path

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Path model

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X Y e

1 B0 c 1


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X Y e

1 B0 c’

M e

1

1

a b


•  Three regression equations can now be re-written with new notation:

•  Y = B0 + c(X) + e •  Y = B0 + c’(X) + b(M) + e •  M = B0 + a(X) + e

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•  The Sobel test

– The null hypothesis •  The indirect effect is zero •  (Ba*Bb) = 0

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z = (Ba* Bb) / SQRT[(Ba2 * SEb

2) + (Bb2 * SEa

2)]

Results

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Path model

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Threat IQ e

1 97.32 -11.00 1


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Threat IQ e

1 56.00 -2.41

WMC e

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1 -11.42 .75

Mediation: Final comments

•  Here we used path analysis to *illustrate* the mediation analysis

•  It is also possible to test for mediation using a statistical procedure called: – Structural Equation Modeling (SEM)

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END SEGMENT

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END LECTURE 14

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Lecture slides stats1.13.l14.air

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