psych (version 1.5.1)

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,...)

Arguments

y

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

x

The principal Independent Variable

m

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.

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

...

Additional graphical parameters to pass to mediate.diagram

Value

• total
  The total direct effect of x on y (c)
• direct
  The beta effects of x (c') and m (b) on y
• indirect
  The indirect effect of x through m on y (c-ab)
• mean.boot
  mean bootstrapped value of indirect effect
• sd.boot
  Standard deviation of bootstrapped values
• ci.quant
  The upper and lower confidence intervals based upon the quantiles of the bootstrapped distribution.
• boot
  The bootstrapped values themselves.
• a
  The effect of x on m
• b
  The effect of m on y
• b.int
  The 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

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).

See Also

setCor and setCor.diagram

Examples

#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")))

mediate(1,2,3,sobel)  #The example in Preacher and Hayes