case1901 {Sleuth3} | R Documentation |
Sex Role Sterotypes and Personnel Decisions
Description
Researchers gave 48 male bank supervisors attending a management institute hypothetical personnel files and asked them whether they would promote the applicant based on the file. The personnel files were identical except that 24 of them listed a male and 24 listed a female applicant. The assignment of managers to receive either a male or female applicant file was carried out at random.
Usage
case1901
Format
A data frame with 2 observations on the following 3 variables.
- Gender
a factor with levels
"Female"
and"Male"
- Promoted
the number of managers who promoted the applicant
- NotPromoted
the number of managers who did not promote the applicant
Source
Ramsey, F.L. and Schafer, D.W. (2013). The Statistical Sleuth: A Course in Methods of Data Analysis (3rd ed), Cengage Learning.
References
Rosen, B. and Jerdee, J (1974). Influence of Sex Role Steroetypes on Personnel Decisions, Journal of Applied Psychology 59: 9–14.
Examples
str(case1901)
attach(case1901)
## INFERENCE
myTable <- cbind(Promoted,NotPromoted)
row.names(myTable) <- Gender
myTable
fisher.test(myTable, alternative="greater")
# Alternative: that odds of Promotion in first row (Males) are greater.
fisher.test(myTable) # Use 2-sided to get confidence interval for odds ratio
prop.test(myTable) # Compare two binomial proportions
## GRAPHICAL DISPLAY FOR PRESENTATION
myTable
# Promoted NotPromoted
#Male 21 3
#Female 14 10
prop.test(21,(21+3)) # Est = .875; CI = .665 to .967
prop.test(14,(14+10))# Est = .583; CI = .369 to .772
pHat <- c(0.875,0.583)
lower95 <- c(0.665, 0.369)
upper95 <- c(0.967, 0.772)
if(require(Hmisc)) { # Use Hmisc library
myObj<- Cbind(pHat,lower95,upper95) # Cbind: a form of cbind needed for Dotplot
Dotplot(Gender ~ myObj,
xlab="Probability of Promotion Based on Applicant File (and 95% Confidence Intervals)",
ylab="Gender Listed in Applicant File", ylim=c(.5,2.5), cex=2)
}
detach(case1901)