mediate: Estimate and display direct and indirect effects of mediators and moderator in path models

Description

Find the direct and indirect effects of a predictor in path models of mediation and moderation. Bootstrap confidence intervals for the indirect effects.

Usage

mediate(y, x, m, data, mod = NULL, n.obs = NULL, use = "pairwise", n.iter = 5000, 
       alpha = 0.05, std = FALSE)
mediate.diagram(medi,digits=2,ylim=c(2,8),xlim=c(-1,10),main="Mediation model",...)
moderate.diagram(medi,digits=2,ylim=c(2,8),main="Moderation model",...)

Arguments

The dependent variable (or a formula suitable for a linear model)

One or more predictor variables

One (or more) mediating variables

data

A data frame holding the data or a correlation matrix.

mod

A moderating variable, if desired

n.obs

If the data are from a correlation or covariance matrix, how many observations were used. This will lead to simulated data for the bootstrap.

use

use="pairwise" is the default when finding correlations or covariances

n.iter

Number of bootstrap resamplings to conduct

alpha

Set the width of the confidence interval to be 1 - alpha

std

standardize the covariances to find the standardized betas

digits

The number of digits to report in the mediate.diagram.

medi

The output from mediate may be imported into mediate.diagram

ylim

The limits for the y axis in the mediate and moderate diagram functions

xlim

The limits for the x axis. Make the minimum more negative if the x by x correlations do not fit.

main

The title for the mediate and moderate functions

...

Additional graphical parameters to pass to mediate.diagram

Value

totalThe total direct effect of x on y (c)
directThe beta effects of x (c') and m (b) on y
indirectThe indirect effect of x through m on y (c-ab)
mean.bootmean bootstrapped value of indirect effect
sd.bootStandard deviation of bootstrapped values
ci.quantThe upper and lower confidence intervals based upon the quantiles of the bootstrapped distribution.
bootThe bootstrapped values themselves.
aThe effect of x on m
bThe effect of m on y
b.intThe interaction of x and mod (if specified)

Details

When doing linear modeling, it is frequently convenient to estimate the direct effect of a predictor controlling for the indirect effect of a mediator. See Preacher and Hayes (2004) for a very thorough discussion of mediation. The mediate function will do some basic mediation and moderation models, with bootstrapped confidence intervals for the mediation/moderation effects.

In the case of being provided just a correlation matrix, the bootstrapped values are based upon bootstrapping from data matching the original correlation matrix with the addition of normal errors. This allows us to test the mediation/moderation effect even if not given raw data.

The number of y variables is currently limited to 1. The number of predictor (x) variables may be 1 or more. The number of mediating variables (m) can be one or more.

References

Preacher, Kristopher J and Hayes, Andrew F (2004) SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, & Computers 36, (4) 717-731.

Data from Preacher and Hayes (2004), and from Kerchoff (1974)

Examples

Run this code

#data from Preacher and Hayes (2004)
sobel <- structure(list(SATIS = c(-0.59, 1.3, 0.02, 0.01, 0.79, -0.35, 
-0.03, 1.75, -0.8, -1.2, -1.27, 0.7, -1.59, 0.68, -0.39, 1.33, 
-1.59, 1.34, 0.1, 0.05, 0.66, 0.56, 0.85, 0.88, 0.14, -0.72, 
0.84, -1.13, -0.13, 0.2), THERAPY = structure(c(0, 1, 1, 0, 1, 
1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 
1, 1, 1, 0), value.labels = structure(c(1, 0), .Names = c("cognitive", 
"standard"))), ATTRIB = c(-1.17, 0.04, 0.58, -0.23, 0.62, -0.26, 
-0.28, 0.52, 0.34, -0.09, -1.09, 1.05, -1.84, -0.95, 0.15, 0.07, 
-0.1, 2.35, 0.75, 0.49, 0.67, 1.21, 0.31, 1.97, -0.94, 0.11, 
-0.54, -0.23, 0.05, -1.07)), .Names = c("SATIS", "THERAPY", "ATTRIB"
), row.names = c(NA, -30L), class = "data.frame", variable.labels = structure(c("Satisfaction", 
"Therapy", "Attributional Positivity"), .Names = c("SATIS", "THERAPY", 
"ATTRIB")))
 #n.iter set to 50 (instead of default of 5000) for speed of example
mediate(1,2,3,sobel,n.iter=50)  #The example in Preacher and Hayes

#Data from sem package taken from Kerckhoff (and in turn, from Lisrel manual)
R.kerch <- structure(list(Intelligence = c(1, -0.1, 0.277, 0.25, 0.572, 
0.489, 0.335), Siblings = c(-0.1, 1, -0.152, -0.108, -0.105, 
-0.213, -0.153), FatherEd = c(0.277, -0.152, 1, 0.611, 0.294, 
0.446, 0.303), FatherOcc = c(0.25, -0.108, 0.611, 1, 0.248, 0.41, 
0.331), Grades = c(0.572, -0.105, 0.294, 0.248, 1, 0.597, 0.478
), EducExp = c(0.489, -0.213, 0.446, 0.41, 0.597, 1, 0.651), 
    OccupAsp = c(0.335, -0.153, 0.303, 0.331, 0.478, 0.651, 1
    )), .Names = c("Intelligence", "Siblings", "FatherEd", "FatherOcc", 
"Grades", "EducExp", "OccupAsp"), class = "data.frame", row.names = c("Intelligence", 
"Siblings", "FatherEd", "FatherOcc", "Grades", "EducExp", "OccupAsp"
))

 #n.iter set to 50 (instead of default of 5000) for speed of demo
mediate("OccupAsp","Intelligence",m= 2:5,data=R.kerch,n.obs=767,n.iter=50)

#Compare the following solution to the path coefficients found by the sem package
mediate(y="OccupAsp",x=c("Intelligence","Siblings","FatherEd","FatherOcc"),
      m= 5:6,data=R.kerch,n.obs=767,n.iter=50)

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