Pay Equity Deep-Dive: Setting the Right Foundation
On Demand until September 01, 2024
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When conducting a pay equity analysis, the “star of the show” is often the multiple regression analysis that is used to identify and remediate pay disparities. While it may be tempting to get to this stage of the work as quickly as possible, there’s considerable value in spending time up front to set the right foundation for your analysis. During this webinar, we’ll walk you through five important steps to take before you move to the remediation phase:

  1. Compensation Philosophy Review
  2. Pay Analysis Group Formation & Testing
  3. Tainted Variable Analysis
  4. Wage Influencing Factor Selection & Consolidation
  5. Reliability Testing

Taking the time up front to set a strong foundation will ensure that the remediation actions you are taking are based on a rigorous and comprehensive process. In this webinar, we’ll help attendees:

  • Understand the significance of aligning your organization's compensation philosophy with pay equity objectives and how it impacts analysis outcomes.
  • Learn how to create meaningful pay analysis groups and test their effectiveness in uncovering pay disparities within your organization.
  • Explore techniques for identifying and addressing potential bias introduced by variables in your analysis, enhancing the fairness of your findings.
  • Gain insights into selecting and consolidating wage-influencing factors for a holistic analysis that captures all relevant elements.
  • Discover the importance of reliability testing to validate your statistical models, leading to trustworthy results.
  • Become better equipped to implement remediation actions based on a rigorous analytical process, promoting fair compensation practices.

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  • Prices are subject to change without notice. All sales are nonrefundable. Access to the live event is limited to the first 1000 participants, so be sure to log on up to 15 minutes prior to the event start time. The live event includes access to playback. Playback and the on-demand webinar are available two business days following the live event, and you can access them for 90 days. If you order the on-demand webinar after the live event, your 90-day access begins the day of order.
  • HRCI credits will only be granted for attending the live webinar event. On-demand webinars do not qualify for HRCI credit. You may receive a certificate of completion for watching an on-demand webinar. Please contact Customer Experience Team at 877-951-9191 (United States and Canada) or +1 480-951-9191 (Outside US) or email to request this certificate. You'll receive it in two to four weeks via email.
Gail Greenfield
Executive VP of Pay Equity and Total Rewards Strategy and Solutions at Trusaic

Gail Greenfield is an eminent pay equity expert who has recently joined Trusaic as Executive Vice President of Pay Equity and Total Rewards Strategy and Solutions.

With over 20 years of experience, she specializes in developing data-driven strategies to promote workplace diversity, equity, and inclusion. Gail's career includes roles at Oracle and Warner Brothers Discovery, and she has a rich history at Mercer, where she testified before Congress on diversity and inclusion.

Her insights on pay equity have been featured in major media outlets, and she is a respected speaker at industry events. Gail holds a PhD in economics from Claremont Graduate University and a bachelor's degree in business economics.

Mark Dwyer
Chief of Data Science at Trusaic

Mark is responsible for evolving Trusaic’s analytics regarding pay, diversity, and data consolidation processes as the company continues to scale and expand its robust data and analytic solutions. He brings over two decades of experience in statistical analysis to Trusaic, both as a faculty member at UCLA and within class-action litigation, where he provided oral and written testimony regarding statistical modelling.

Mark was formerly a Senior Economic Consultant for the Harris Economics Group, where he provided written and oral expert testimony regarding the interpretation of empirical analyses. He directed data cleaning and the construction of empirical models, among other responsibilities. Mark also worked as a Senior Economist for Econ One Research, Inc., and taught Econometrics as an Assistant Professor at UCLA.

He earned a Ph.D. in Economics from the University of Rochester, an M.A. in Finance from The Wharton School, and a B.A. in Physics from the University of Pennsylvania. He is a member of the American Economic Association and the Econometric Society.