QuantPsyc (version 1.6)

distInd.ef: Complex Mediation for use in Bootstrapping

Description

Computes the 'total indirect effect' from distal.med for use in boot

Usage

distInd.ef(data, i)

Arguments

data

data.frame used in distal.med

i

i is a 'count' placeholder necessary for use in boot

Value

indirect effect that is passed to boot for each bootstrap sample

Details

This function is not useful of itself. It is specifically created as an intermediate step in bootstrapping the indirect effect.

References

Davison, A. C. & Hinkley, D. V. (1997). Bootstrap methods and their application. Cambridge, UK: Cambridge University Press.

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.

MacKinnon, D. P., Lockwood, C. M., & Williams, J. (2004). Confidence limit for indirect effect: distribution of the product and resampling methods. Multivariate Behavioral Research, 39, 99-128.

See Also

distal.med

Examples

# NOT RUN {
# }
# 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")

require(boot)
distmed.boot <- boot(d200, distInd.ef, R=999)
sort(distmed.boot$t)[c(25,975)]	#95% CI
plot(density(distmed.boot$t))	# Distribution of bootstapped indirect effect
summary(distmed.boot$t)
# }
# NOT RUN {
# }