FAQ/medmax - CBU statistics Wiki

You can upload content for the page named below. If you change the page name, you can also upload content for another page. If the page name is empty, we derive the page name from the file name.

File to load page content from
Page name
Comment
Finzd thee wrang lelters ino eacuh wosrd

location: FAQ / medmax

# A suggested effect size and bootstrap Confidence Interval for a mediation effect

Preacher and Kelley (2011) suggest using a standardized effect size tor epresent the strength of the indirect effect of the independent variable, IV, on outcome, Y, through a mediator, M. Their effect size, kappa-squared, represents the proportion of the total possible effect that is shown by the sample. It may vary between 0 (no indirect effect) to 1 (maximum possible indirect effect attained by the data) and, they suggest, interpreting in an analogous way to a R-squared with 0.01, 0.09 and 0.25 representing small, medium and large effects respectively. They suggest quoting the effect (the product of the regression coefficients for IV -> M and M -> Y given IV) and its 95% bootstrapped confidence interval when performing the mediation analysis.

This estimate, together with its 95% bootstrap confidence interval, may be obtained using this spreadsheet which uses the bootstrap add-in for EXCEL (details of how to add this in and implement it to obtain 95% bootstrap confidence intervals are given here.) In the example in this spreadsheet the bootraw6 sheet contains the 95% confidence interval of (0.009, 0.39) suggesting a large mediation effect.

The estimate and its 95% confidence interval together with other effect sizes and power calculations using these effect sizes may be used in R using the MBESS package (Its manual is available in pdf format from here or here.)

Example R code is below which can be used to obtain kappa-squared and its 95% confidence interval for the SPSS data given here. You will have to replace the SPSS filename in the read.spss command line. Using the spreadsheet or R syntax below gives a kappa-squared for this data of 0.1687234 with an approximate 95% bootstrap confidence interval of [0.00641602, 0.368722].

```install.packages(c("MBESS"))
install.packages(c("foreign"))
install.packages(c("gsl"))

library(MBESS)
library(foreign)
library(gsl)
library(MASS)
meg <- read.spss("C:\\Documents and Settings\\peterw\\Desktop\\My Documents\\My Documents2\\SOBEL'S TEST\\MBESS R MEB EFF SIZE BOOT CIS\\MED BOOT CI TEST DATA.sav")
meg <- data.frame(meg)
attach(meg)
meg <- na.omit(meg)
medci <- mediation(IV,M,Y,conf.level=0.95,bootstrap=TRUE,B=1000)```