Load Self Evaluation Survey Responses
Create a tribble to assign numeric values to Likert scale responses.
Rename predictor vars “gender” and “grad.” Gather questions and responses into a long format for plotting.
Calculate relative frequency of each answer and plot.
Create a tribble to group responses into “positive”, “neutral”, and “negative.”
Calculate relative frequency of each general sentiment and plot.
Use Fisher’s exact test to quantify gender difference between responses.
## # A tibble: 11 x 2
## Question p.value
## <chr> <dbl>
## 1 I feel that I am appropriately compensated for my work. 0.404
## 2 I feel comfortable asking for an increased rate of pay when I d~ 0.306
## 3 I have adequate access to facilities/equipment to develop into ~ 0.479
## 4 I generally have the skills to develop into a good teacher. 0.823
## 5 In general, people tend to believe that I am more competent tha~ 0.753
## 6 Sometimes I am afraid others will discover how much knowledge o~ 0.180
## 7 I feel highly confident that I will succeed in my future career. 0.581
## 8 At times, I feel I am in my current career position through som~ 0.0984
## 9 The major cause of success in my life is my high ability. 0.862
## 10 When I succeed, it is because I work much harder than others. 0.414
## 11 I am at least as smart as my peers. 0.0413
Loop through all questions.
Create dataframe of conditional probabilities by question.
Plot conditional probabilities.
Create tribble to assign numeric value to experience self-rating scale.
Create a df of “demographics” and rename pred vars something less annoying.
Break out each job posting into its own df and store as list.
Loop through all job postings to calculate experience level for each individual applicant, for each posting..
Plot self-rated expeience by gender for each job posting.
Calculate tukey tests between groups.
Plot outcome of Tukey tests.
Load Hiring Manager Survey
Create a tribble to assign numeric values to experience levels
Create a tribble to assign numeric values to Likert scale responses.
Use Fisher’s exact test to quantify gender differences in experience and education
## # A tibble: 3 x 2
## Question p.value
## <chr> <dbl>
## 1 $0-10 1
## 2 $11-20 0.204
## 3 $21-30 0.873
No difference in education.
## # A tibble: 3 x 2
## Question p.value
## <chr> <dbl>
## 1 $0-10 0.930
## 2 $11-20 0.609
## 3 $21-30 0.239
No difference in experience.
Pull and plot career progression data from https://www.payscale.com/data/gender-pay-gap
Pull and plot Ehrlinger and Dunning 2003 data
Create and plot confidence data from Institute of Leadership and Management: UK
Create and plot confidence data Marilyn Davidson (Manchester Business School)