Overview:
In this webinar we will build a pay structure using hypothetical pay survey data.
Why you should Attend:
Learn how to build a pay structure with regression analysis. Pay lines are created using this technique that form the basis for the establishment of pay range midpoints in the pay structure. These pay lines can be manipulated in consideration of the importance of various job classifications in your organization. Using this method will enable you to explain what you have done, and what it means to your employees.
Areas Covered in the Session:
In the process you will learn:
- What data to use from pay surveys you possess
- The importance of benchmark jobs and their associated data
- How much survey data to use for building the pay structure
- What a `weighted average` is and why it is important
- What to use as a measure of the pay range midpoint: `weighted averages` or `medians`?
- The difference between `regression analysis` and `correlation analysis?`
- How wide pay ranges should be in percentage terms
- The ideal range spread for `midpoint to midpoint spreads`
- What the `seed data` should be for the development of pay ranges
- How to test and integrate your employee pay rates into the new pay structure
- How often to update your pay structures
Speaker
David J. Wudyka, MBA, is the Managing Principal of Westminster Associates of Wrentham, MA (www.westminsterassociates.net). He has over thirty years experience as a Human Resource Consultant with a specialty in Compensation Consulting. David has taught extensively in colleges and universities such as UMass Boston, Bryant University, and the U.S. Coast Guard Academy. David is especially interested in how the HR Dept. can strengthen its role as a Strategic Partner in businesses today. He is writing extensively about how to improve pay transparency and to reduce the gender pay gap in ways that make sense for businesses of all sizes.
Who Should Attend
- Human Resource Managers
- Compensation Analysts
- Compensation Managers
- Financial Managers of HR Departments
