Difference between revisions of "Salary Regression Analysis"

From YMwiki
Jump to navigationJump to search
 
(19 intermediate revisions by 3 users not shown)
Line 1: Line 1:
−
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 is a Multiple Linear Regression analysis method of testing for possible compensation disparity within an organization. This analysis can take into account multiple factors that influence salaries (e.g. seniority, performance, skills etc.) and determine if there is a significant correlation between race or gender and compensation. 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 tests if race or gender status significantly influences compensation after accounting for other independent variables included in the analysis, such as: number of years employed with the company, number of years in the current job, and 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, SSEGs, or by all employees. We normally choose to complete the analysis by job or SSEG, because these most closely follow the Equal Pay Act and Title VII 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.
+
The Salary Regression Analysis report lists the job (or salary grade, department, job group, etc.) being tested, the total number of employees in the job and the average salary of the employees in the job. Note: 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 "Avg Salary" column shows the average salaries for each of the groups that are analyzed. The "Significant" and "Std Dev" columns tell us whether the difference in average pay by race or gender is statistically significant. A standard deviation of -2.0 or less is considered statistically significant and "Yes" will be reported in the "Significant?" column.
  
−
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.
+
The Salary Regression Analysis uses group size to determine if the analysis is statistically valid. The analysis is deemed statistically insignificant and "N/A" is reported in the "Significant?" column if the total group size is less than 30 or if any subgroup is less than 5. For example, if there were 31 males and 4 females in the group, the total group size of 35 is large enough, but the Female subgroup is too small to render valid results.
  
−
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.
+
An explanatory variable that is multicollinear with another variable has been excluded from the analysis if indicated in the footnotes. Variables are considered multicollinear if they contain the same or close to the same values. For example, a company may have Original Hire Date and Most Recent Hire Date available as explanatory variables. If these two variables contain substantially the same dates in the particular group being analyzed, one of the variables should be excluded or the analysis is less reliable.
  
−
Elements for running a successful Salary Regression Analysis include:
+
Only those variables that significantly correlate to salaries are included in the analysis if that option is checked.  A variable is excluded from the analysis if it has a standard deviation that is not greater than 2.0 or less than -2.0. Unchecked, this option will force all variables to be included in the analysis. All variables that are included or excluded from the analysis are listed in the footnote section of the report.
−
* 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
 
  
−
[[File:Salary Regression Analysis Report.png]]
+
[[File:Regression Analysis 10-23-14.png]]
  
 
==See Also==  
 
==See Also==  
−
[[Salary Regression Options]]
+
[[Publish Your Reports]]<br>
 +
[[Options]], Calculations tab<br>
 +
[[Report Options - Salary Regression]]<br>
 +
[[Understanding Your Reports]]<br>
  
 
© Copyright Yocom & McKee, Inc.
 
© Copyright Yocom & McKee, Inc.

Latest revision as of 17:24, 30 October 2014

The Salary Regression Analysis is a Multiple Linear Regression analysis method of testing for possible compensation disparity within an organization. This analysis can take into account multiple factors that influence salaries (e.g. seniority, performance, skills etc.) and determine if there is a significant correlation between race or gender and compensation. 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 tests if race or gender status significantly influences compensation after accounting for other independent variables included in the analysis, such as: number of years employed with the company, number of years in the current job, and 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, SSEGs, or by all employees. We normally choose to complete the analysis by job or SSEG, because these most closely follow the Equal Pay Act and Title VII guidelines.

The Salary Regression Analysis report lists the job (or salary grade, department, job group, etc.) being tested, the total number of employees in the job and the average salary of the employees in the job. Note: Employees who report to offsite managers are not included in the Salary Regression Analysis report.

The "Avg Salary" column shows the average salaries for each of the groups that are analyzed. The "Significant" and "Std Dev" columns tell us whether the difference in average pay by race or gender is statistically significant. A standard deviation of -2.0 or less is considered statistically significant and "Yes" will be reported in the "Significant?" column.

The Salary Regression Analysis uses group size to determine if the analysis is statistically valid. The analysis is deemed statistically insignificant and "N/A" is reported in the "Significant?" column if the total group size is less than 30 or if any subgroup is less than 5. For example, if there were 31 males and 4 females in the group, the total group size of 35 is large enough, but the Female subgroup is too small to render valid results.

An explanatory variable that is multicollinear with another variable has been excluded from the analysis if indicated in the footnotes. Variables are considered multicollinear if they contain the same or close to the same values. For example, a company may have Original Hire Date and Most Recent Hire Date available as explanatory variables. If these two variables contain substantially the same dates in the particular group being analyzed, one of the variables should be excluded or the analysis is less reliable.

Only those variables that significantly correlate to salaries are included in the analysis if that option is checked. A variable is excluded from the analysis if it has a standard deviation that is not greater than 2.0 or less than -2.0. Unchecked, this option will force all variables to be included in the analysis. All variables that are included or excluded from the analysis are listed in the footnote section of the report.

Regression Analysis 10-23-14.png

See Also

Publish Your Reports
Options, Calculations tab
Report Options - Salary Regression
Understanding Your Reports

© Copyright Yocom & McKee, Inc.

Retrieved from "https://yocom-mckee.com/wiki2/index.php?title=Salary_Regression_Analysis&oldid=2217"