QuantPsyc (version 1.6)

distal.med: Distal Indirect Effect

Description

Computes the indirect effect (and all paths) in a 4 variable system, assuming all paths estimated.

Usage

distal.med(data)

Arguments

data

data.frame containing the variables labeled 'x', 'm1', 'm2', and 'y' respectively.

Value

Returns a table with all the effects and decomposition of effects in the above 4 variable system inclucing the standard errors and t-values.

a

Effect of X on M1

b

Effect of M1 on M2 controlling for X

c

Effect of M2 on Y controlling for X and M1

e

Effect of X on M2 controlling for M1

f

Effect of M1 on Y controlling for X and M2

abc

'Direct' Indirect Effect of X on Y

af

Indirect Effect of X on Y through M1 only

ef

Indirect Effect of M1 on Y though M2

ind.xy

'Total' Indirect effect of X on Y

t

Total Effect of X on Y

t'

Direct Effect of X on Y accounting for all mediators

Warning

This function is primative in that it is based on a simplistic model AND forces the user to name the variables in the dataset x, m1, m2, and y.

Details

Computes the paths in the model system: /cr Y = t'X + fM1 + cM2 M2 = eX + bM1 M1 = aX and the indirect effect a*b*c + a*f + e*c

References

Fletcher, T. D. (2006, August). Methods and approaches to assessing distal mediation. Paper presented at the 66th annual meeting of the Academy of Management, Atlanta, GA.

MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002). A comparison of methods to test mediation and other intervening variable effects. Psychological Methods, 7, 83-104.

See Also

proximal.med, distInd.ef

Examples

# NOT RUN {
cormat <- matrix (c(1,.3,.15,.075,.3,1,.3,.15,.15,.3,1,.3,.075,.15,.3,1),ncol=4)
require(MASS)
d200 <- data.frame(mvrnorm(200, mu=c(0,0,0,0), cormat))
names(d200) <- c("x","m1","m2","y")
distal.med(d200)

# }