\name{Garcia}
\alias{Garcia}
\alias{protest}
\alias{GSBE}
\docType{data}
\title{Data from the sexism (protest) study of Garcia, Schmitt, Branscome, and Ellemers (2010)
}
\description{
Garcia, Schmitt, Branscombe, and Ellemers (2010) report data for 129 subjects on the effects of perceived sexism on anger and liking of women's reactions to ingroup members who protest discrimination. This data set is also used as the `protest' data set by Hayes (2013 and 2018).  It is a useful example of mediation and moderation in regression. It may also be used as an example of plotting interactions.
}
\usage{data("GSBE")}
\format{
  A data frame with 129 observations on the following 6 variables.
  \describe{
    \item{\code{protest}}{0 = no protest, 1 = Individual Protest, 2 = Collective Protest}
    \item{\code{sexism}}{Means of an 8 item Modern Sexism Scale.}
    \item{\code{anger}}{Anger towards the target of discrimination. ``I feel angry towards Catherine".}
    \item{\code{liking}}{Mean rating of 6 liking ratings of the target.}
    \item{\code{respappr}}{Mean of four items of appropriateness of the target's response.}
    \item{\code{prot2}}{A recoding of protest into two levels (to match Hayes, 2013).}
  }
}
\details{
The reaction of women to women who protest discriminatory treatment was examined in an experiment reported by Garcia et al. (2010). 129 women  were given a description of sex discrimination in the workplace (a male lawyer was promoted over a clearly more qualified female lawyer).  Subjects then read that the target lawyer felt that the decision was unfair.  Subjects were then randomly assigned to three conditions: Control (no protest), Individual Protest (``They are treating me unfairly") , or Collective Protest (``The firm is is treating women unfairly"). 

Participants were then asked how much they liked the target (liking), how angry they were to the target (anger) and to evaluate the appropriateness of the target's response (respappr).  

Garcia et al (2010) report a number of interactions (moderation effects) as well as moderated-mediation effects.

This data set is used as an example in Hayes (2013) for moderated mediation.  It is used here to show how to do moderation (interaction terms) in regression (see \code{\link{setCor}}) , how to do moderated mediation (see \code{\link{mediate}}) and how draw interaction graphs (see help).

}
\source{
The data were downloaded from the webpages of Andrew Hayes (https://www.afhayes.com/public/hayes2018data.zip)  supporting the first and second edition of his book.  The second edition includes 6 variables, the first, just four.  The protest variable in 2018 has three levels, but just two in the 2013 source. 

The data are used by kind permission of Donna M. Garcia, Michael T. Schmitt, Nyla R. Branscombe, and Naomi Ellemers.  
}
\references{Garcia, Donna M. and Schmitt, Michael T. and Branscombe, Nyla R. and Ellemers, Naomi (2010). Women's reactions to ingroup members who protest discriminatory treatment: The importance of beliefs about inequality and response appropriateness. European Journal of Social Psychology, (40) 733-745.

Hayes, Andrew F. (2013)  Introduction to mediation, moderation, and conditional process analysis: A regression-based approach.  Guilford Press. 

}
\examples{
data(GSBE)  #alias to Garcia data set

## Just do regressions with interactions
lmCor(respappr ~ prot2 * sexism,std=FALSE,data=Garcia,main="Moderated (mean centered )")
lmCor(respappr ~ prot2 * sexism,std=FALSE,data=Garcia,main="Moderated (don't center)", zero=FALSE)
#demonstrate interaction plots
plot(respappr ~ sexism, pch = 23- protest, bg = c("black","red", "blue")[protest], 
data=Garcia, main = "Response to sexism varies as type of protest")
by(Garcia,Garcia$protest, function(x) abline(lm(respappr ~ sexism,
   data =x),lty=c("solid","dashed","dotted")[x$protest+1])) 
text(6.5,3.5,"No protest")
text(3,3.9,"Individual")
text(3,5.2,"Collective")

 
#compare two models  (bootstrapping n.iter set to 50 for speed
# 1) mean center the variables prior to taking product terms
mod1 <- mediate(respappr ~ prot2 * sexism +(sexism),data=Garcia,n.iter=50
 ,main="Moderated mediation (mean centered)")
# 2) do not mean center
mod2 <- mediate(respappr ~ prot2 * sexism +(sexism),data=Garcia,zero=FALSE, n.iter=50,   
    main="Moderated  mediation (not centered")

summary(mod1)
summary(mod2)


}
\keyword{datasets}