case1901: Sex Role Sterotypes and Personnel Decisions

In Sleuth3: Data Sets from Ramsey and Schafer's "Statistical Sleuth (3rd Ed)"

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)

Example output

'data.frame':	2 obs. of  3 variables:
 $ Gender     : Factor w/ 2 levels "Female","Male": 2 1
 $ Promoted   : int  21 14
 $ NotPromoted: int  3 10
       Promoted NotPromoted
Male         21           3
Female       14          10

	Fisher's Exact Test for Count Data

data:  myTable
p-value = 0.0245
alternative hypothesis: true odds ratio is greater than 1
95 percent confidence interval:
 1.230224      Inf
sample estimates:
odds ratio 
   4.83119 


	Fisher's Exact Test for Count Data

data:  myTable
p-value = 0.04899
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
  1.00557 32.20580
sample estimates:
odds ratio 
   4.83119 


	2-sample test for equality of proportions with continuity correction

data:  myTable
X-squared = 3.7978, df = 1, p-value = 0.05132
alternative hypothesis: two.sided
95 percent confidence interval:
 0.01249145 0.57084188
sample estimates:
   prop 1    prop 2 
0.8750000 0.5833333 

       Promoted NotPromoted
Male         21           3
Female       14          10

	1-sample proportions test with continuity correction

data:  21 out of (21 + 3), null probability 0.5
X-squared = 12.042, df = 1, p-value = 0.0005202
alternative hypothesis: true p is not equal to 0.5
95 percent confidence interval:
 0.6653891 0.9671473
sample estimates:
    p 
0.875 


	1-sample proportions test with continuity correction

data:  14 out of (14 + 10), null probability 0.5
X-squared = 0.375, df = 1, p-value = 0.5403
alternative hypothesis: true p is not equal to 0.5
95 percent confidence interval:
 0.3694058 0.7720124
sample estimates:
        p 
0.5833333 

Loading required package: Hmisc
Loading required package: lattice
Loading required package: survival
Loading required package: Formula
Loading required package: ggplot2

Attaching package: ‘Hmisc’

The following objects are masked from ‘package:base’:

    format.pval, units

Sleuth3 documentation built on May 2, 2019, 6:41 a.m.