Difference between revisions of "Salary Regression Analysis"
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The Salary Regression Analysis report estimates the pay for female and minority salaries based on a variety of factors, such as: number of years employed with the company, number of years in the current job, and/or performance ratings. Each organization may utilize up to six factors to help explain potential pay disparities. The analysis can be broken down by department, jobs, job groups, EEO categories, salary grades, or by all employees. We normally choose to complete the analysis by job, because this most closely follows Equal Pay Act guidelines. | The Salary Regression Analysis report estimates the pay for female and minority salaries based on a variety of factors, such as: number of years employed with the company, number of years in the current job, and/or performance ratings. Each organization may utilize up to six factors to help explain potential pay disparities. The analysis can be broken down by department, jobs, job groups, EEO categories, salary grades, or by all employees. We normally choose to complete the analysis by job, because this most closely follows Equal Pay Act guidelines. | ||
| − | The Salary Regression Analysis report lists the job (or salary grade, department, job group, etc.) being tested and the total number of employees in the job. The sample report shown at the bottom of this page is for | + | The Salary Regression Analysis report lists the job (or salary grade, department, job group, etc.) being tested and the total number of employees in the job. The sample report shown at the bottom of this page is for an Administrative Assistant job with 5 employees. Note: Part time employees and employees who report to offsite managers, are not included in the Salary Regression Analysis report. |
| − | Females are tested first, followed by minorities then individual races. In the following example, we will examine females, but the principles apply for the minorities and the other individual race and ethnic categories as well. There are three females in the example shown below. In each subcategory, the '''$ Difference''' column indicates the average dollar difference in pay between women or minorities versus men or non-minorities. The | + | Females are tested first, followed by minorities then individual races. In the following example, we will examine females, but the principles apply for the minorities and the other individual race and ethnic categories as well. There are three females in the example shown below. In each subcategory, the '''$ Difference''' column indicates the average dollar difference in pay between women or minorities versus men or non-minorities. The four females are paid $1,133 more than the average of the male in this job group. A negative number indicates less pay, a positive number, more. This number will differ from the average Salary Analysis or Salary Summary report when factors other than race and gender are included in the analysis. |
| − | The '''Significant''' column indicates whether this factor significantly affects salary. In this example, the difference in pay between males and females is significant. The column '''Independent Variables Used''' indicates the variables used in the analysis. In this example, gender and | + | The '''Significant''' column indicates whether this factor significantly affects salary. In this example, the difference in pay between males and females is not significant. The column '''Independent Variables Used''' indicates the variables used in the analysis. In this example, gender and race were used. Adding more variables, such as performance rating, experience, and date in job, may eliminate statistically significant pay disparities. The '''''Stepwise''''' option was used in this example which automatically includes only those variables that have a significant impact on salaries. Race and gender are always included. |
On the reports, '''N/A''' means not applicable. In some instances you may need to remove one or more of the variables used in the test because the number of employees is too small to represent a valid test. It is also possible that there may be no employees in that particular group to perform the test. | On the reports, '''N/A''' means not applicable. In some instances you may need to remove one or more of the variables used in the test because the number of employees is too small to represent a valid test. It is also possible that there may be no employees in that particular group to perform the test. | ||
Revision as of 17:18, 25 October 2011
The Salary Regression Analysis is a Multiple Linear Regression analysis method of testing for possible compensation disparity within an organization. Unlike the Salary Analysis reports, which simply compares the average or median pay of females to males, or minorities to whites, the multiple regression analysis takes into account other factors that influence salaries (e.g. seniority, performance, skills etc.) then determines if any difference in pay is statistically significant. It cannot be overemphasized that the salary regression analysis performed constitutes only the first step in what may be a necessary comprehensive study of your compensation practices. While providing useful insight to potential wage disparities, the results of this salary analysis should not be construed as conclusive evidence of either the existence of, or lack of an impermissible compensation practice.
The Salary Regression Analysis report estimates the pay for female and minority salaries based on a variety of factors, such as: number of years employed with the company, number of years in the current job, and/or performance ratings. Each organization may utilize up to six factors to help explain potential pay disparities. The analysis can be broken down by department, jobs, job groups, EEO categories, salary grades, or by all employees. We normally choose to complete the analysis by job, because this most closely follows Equal Pay Act guidelines.
The Salary Regression Analysis report lists the job (or salary grade, department, job group, etc.) being tested and the total number of employees in the job. The sample report shown at the bottom of this page is for an Administrative Assistant job with 5 employees. Note: Part time employees and employees who report to offsite managers, are not included in the Salary Regression Analysis report.
Females are tested first, followed by minorities then individual races. In the following example, we will examine females, but the principles apply for the minorities and the other individual race and ethnic categories as well. There are three females in the example shown below. In each subcategory, the $ Difference column indicates the average dollar difference in pay between women or minorities versus men or non-minorities. The four females are paid $1,133 more than the average of the male in this job group. A negative number indicates less pay, a positive number, more. This number will differ from the average Salary Analysis or Salary Summary report when factors other than race and gender are included in the analysis.
The Significant column indicates whether this factor significantly affects salary. In this example, the difference in pay between males and females is not significant. The column Independent Variables Used indicates the variables used in the analysis. In this example, gender and race were used. Adding more variables, such as performance rating, experience, and date in job, may eliminate statistically significant pay disparities. The Stepwise option was used in this example which automatically includes only those variables that have a significant impact on salaries. Race and gender are always included.
On the reports, N/A means not applicable. In some instances you may need to remove one or more of the variables used in the test because the number of employees is too small to represent a valid test. It is also possible that there may be no employees in that particular group to perform the test.
Elements for running a successful Salary Regression Analysis include:
- Actual Salary
- Part Time Status
- Hire Date
- Date in Current Job
- Date of Last Increase
- Training Completed Date
- Tenure
- Performance rating
- Education
- Prior Experience
- Shift
See Also
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