Data

The data was provided by The original data was in SPSS format, so we need to use the foreign package to import it into R. The original paper can be found here. Remember, this was a randomized controlled experiment to understand the impact of gender. i.e. applications were randomized to have either male or female names.

library(foreign)
hiring <- read.spss("Moss-Racusin_etal_replicate.sav", to.data.frame=TRUE)

The data looks like

##   Subject cond gender likeable hireable   competence competence2 salary mentoring
## 1       4 male   male 2.666667 3.000000  0.277414539    4.000000  30000  4.333333
## 2       5 male   male 3.500000 3.000000  0.843618853    4.000000  45000  3.666667
## 3       7 male   male 3.333333 3.333333  0.057517929    3.666667  30000  4.666667
## 4      13 male   male 2.000000 3.333333 -0.342407302    3.333333  25000  5.666667
## 5      14 male   male 3.000000 3.333333  0.462666850    4.000000  35000  4.333333
## 6      15 male   male 4.333333 4.000000  0.004375458    3.000000  40000  5.666667

where

We have the following split for the gender of the applicants

## 
##   male female 
##     63     64

but also the following \(2\times 2\) split in who evaluates who:

##         gender
## cond     male female
##   male     48     15
##   female   45     19

i.e. 48 male candidates were evaluated by men while 15 male candidates where evaluated by women.

Comparisons in Hirerability

Overall

This is the overall comparison (i.e. not taking into account the gender of the evaluator)

## Response variable: numerical, Explanatory variable: categorical
## Difference between two means
## Summary statistics:
## n_male = 63, mean_male = 3.7804, sd_male = 1.2385
## n_female = 64, mean_female = 2.9245, sd_female = 1.0423

## Observed difference between means (male-female) = 0.8559
## 
## Standard error = 0.2033 
## 95 % Confidence interval = ( 0.4575 , 1.2544 )

Overall, it seems that on average men had higher “hireability” scores.

Split by Gender of Evaluator

Let’s split the data set by the gender of the evalator using the filter command

hiring.male <- filter(hiring, gender=="male")
hiring.female <- filter(hiring, gender=="female")

We show 95% confidence intervals of \(\mu_{M}-\mu_{F}\) for the male evaluators:

## Response variable: numerical, Explanatory variable: categorical
## Difference between two means
## Summary statistics:
## n_male = 48, mean_male = 3.7361, sd_male = 1.2395
## n_female = 45, mean_female = 2.9593, sd_female = 1.1232

## Observed difference between means (male-female) = 0.7769
## 
## Standard error = 0.245 
## 95 % Confidence interval = ( 0.2966 , 1.2571 )

We show 95% confidence intervals of \(\mu_{M}-\mu_{F}\) for the female evaluators:

## Response variable: numerical, Explanatory variable: categorical
## Difference between two means
## Summary statistics:
## n_male = 15, mean_male = 3.9222, sd_male = 1.2675
## n_female = 19, mean_female = 2.8421, sd_female = 0.8416

## Observed difference between means (male-female) = 1.0801
## 
## Standard error = 0.38 
## 95 % Confidence interval = ( 0.2651 , 1.8951 )

For both female and male evaluators, seems that on average men had higher “hireability” scores.