##User Interface User may select dataset to be analyzed, which may be either of the following:
- Salary
- Total Compensation
- Bonus
- Stock
- Data X
Future versions will add the following:
- Promotion
- Hiring
- Layoff
The mean (average) for each of the above types of data will be compared between the different "factors" below. For instance, the average salary between different Genders (Men and Women). Factors may be one of the following:
- Gender (Factor 1)
- Ethnicity (Factor 2)
- Religion (Factor 3)
- Age (Factor 4)
User may also select criteria of the analyzed data. Selecting criteria will determine how the data is grouped. For instance, to compare salaries (data type) between men and women (data factor) one would typically group employees by their Title and Years of School or Years of Experience. Global organizations may also choose Country, so employees Criteria may be any of the following:
- Title
- Years of School
- Years of Experience
##Further breakdown by "location"? Perhaps add
- Country
- Organization (Business Unit, Division, and/or Department)
- Manager
##Data
- CurrentSalary *
- Total Compensation *
- In order to compare Salaries and Total Compensation they should be provided as Full-Time Equivalent (FTE) and in the same currency.
##Background Information
Equal Employment Opportunity Commission - https://www.eeoc.gov/policy/docs/qanda-compensation.html