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Using Logistic Regression to
Evaluate Adverse Impact



BCGi: Adverse Impact & Test
Validation Book Series
Logistic Regression Overview
    • Classic adverse impact analyses can only
      determine if the numerical difference in passing
      rates between two groups is significantly
      different.
    • Logistic Regression (LR) can identify if that
      numerical difference in passing rates is due to
      applicant differences in job-related criteria (e.g.,
      experience or education).


2                    www.BCGinstitute.org
Logistic Regression Example


                                       Relevant
                                      Experience
                  Hiring
      Gender     Decision(s)


                             Reference
           Education        Check Score



3              www.BCGinstitute.org
Logistic Regression Overview
• LR needs to be applied to job-related factors that were actually
  used or considered in the selection process
• LR should be mapped onto actual positions, hiring data, and hiring
  decisions, not theoretical ones
• LR is useful for weighing the practical importance of job-related
  factors in the hiring or promotion process
• LR can also be useful for determining “shortfall” calculations
   ― For example, how many women would have been hired “but

      for” the possible discrimination?
   ― What is the total shortfall for women, given what the model

      can explain?



4                     www.BCGinstitute.org
Why is this Topic Important to HR/EEO Professionals?
 • Why do I need to know about this topic?
   – LR can be a powerful tool to justify adverse impact in some
     cases
   – Critical concept on both sides of EEO issues
 • What are the key essentials I need to know about this topic?
   – What circumstances are best for LR analyses?
   – How can a LR dataset be set up?
   – How to understand the key essentials in an LR report?
 • What are the consequences surrounding these issues?
   – Adverse impact left unjustified (either through LR or validity,
     or both) constitutes a huge liability for both audits and court
     cases
   – Can be a more powerful tool that standard AI
   – LR can be useful for counting and re-counting shortfalls

                       www.BCGinstitute.org
Presentation Overview

• Overview & Background
• The Connection between Classic AI & LR:
     – Why Classic AI should be established before using LR
•   Three Steps for Building an LR Dataset
•   Nine Steps for Conducting an LR Analysis
•   Model Refinement & Examining Assumptions
•   Interpreting LR Analysis Results

                     www.BCGinstitute.org
Resources
• Adverse Impact and Test Validation: A Practitioner's
  Handbook by Daniel A. Biddle, Ph.D.
   – Purchase online at www.BCGinstitute.org

• Adverse Impact and Test Validation Book Series Webinars
   – Recordings available online for all BCGi Platinum Members
   – Webinar slides available online to all BCGi Standard & Platinum
     Members

• BCGi Membership
   – Free Standard Membership
   – Premium Platinum Membership



                        www.BCGinstitute.org
About Our Sponsor: Biddle Consulting Group (BCG)
 BCG is an HR firm dedicated to providing the highest products and services related to
 Equal Employment Opportunity (EEO), Affirmative Action and Employee Selection.
 • BCG’s Consulting Services
     ― Affirmative Action Plan Outsourcing
     ― Compensation Analysis
     ― EEO/AA Litigation Support (Plaintiff and Defendant)
     ― Job Analysis
     ― Test Development and Test Validation
 • BCG’s Software Products
     ― Adverse Impact Toolkit™
     ― AutoAAP® affirmative action plan development software
     ― AutoGOJA® job analysis software
     ― C4 call center testing software
     ― COMPARE™ compensation analysis software
     ― CritiCall® dispatcher/call-taker personnel selection software
     ― ENCOUNTER soft skills video situational judgment testing software
     ― OPAC® office skills testing software
     ― TVAP™ Test Validation & Analysis Program software
                                www.Biddle.com

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Logistic Regression (Overview)

  • 1. Using Logistic Regression to Evaluate Adverse Impact BCGi: Adverse Impact & Test Validation Book Series
  • 2. Logistic Regression Overview • Classic adverse impact analyses can only determine if the numerical difference in passing rates between two groups is significantly different. • Logistic Regression (LR) can identify if that numerical difference in passing rates is due to applicant differences in job-related criteria (e.g., experience or education). 2 www.BCGinstitute.org
  • 3. Logistic Regression Example Relevant Experience Hiring Gender Decision(s) Reference Education Check Score 3 www.BCGinstitute.org
  • 4. Logistic Regression Overview • LR needs to be applied to job-related factors that were actually used or considered in the selection process • LR should be mapped onto actual positions, hiring data, and hiring decisions, not theoretical ones • LR is useful for weighing the practical importance of job-related factors in the hiring or promotion process • LR can also be useful for determining “shortfall” calculations ― For example, how many women would have been hired “but for” the possible discrimination? ― What is the total shortfall for women, given what the model can explain? 4 www.BCGinstitute.org
  • 5. Why is this Topic Important to HR/EEO Professionals? • Why do I need to know about this topic? – LR can be a powerful tool to justify adverse impact in some cases – Critical concept on both sides of EEO issues • What are the key essentials I need to know about this topic? – What circumstances are best for LR analyses? – How can a LR dataset be set up? – How to understand the key essentials in an LR report? • What are the consequences surrounding these issues? – Adverse impact left unjustified (either through LR or validity, or both) constitutes a huge liability for both audits and court cases – Can be a more powerful tool that standard AI – LR can be useful for counting and re-counting shortfalls www.BCGinstitute.org
  • 6. Presentation Overview • Overview & Background • The Connection between Classic AI & LR: – Why Classic AI should be established before using LR • Three Steps for Building an LR Dataset • Nine Steps for Conducting an LR Analysis • Model Refinement & Examining Assumptions • Interpreting LR Analysis Results www.BCGinstitute.org
  • 7. Resources • Adverse Impact and Test Validation: A Practitioner's Handbook by Daniel A. Biddle, Ph.D. – Purchase online at www.BCGinstitute.org • Adverse Impact and Test Validation Book Series Webinars – Recordings available online for all BCGi Platinum Members – Webinar slides available online to all BCGi Standard & Platinum Members • BCGi Membership – Free Standard Membership – Premium Platinum Membership www.BCGinstitute.org
  • 8. About Our Sponsor: Biddle Consulting Group (BCG) BCG is an HR firm dedicated to providing the highest products and services related to Equal Employment Opportunity (EEO), Affirmative Action and Employee Selection. • BCG’s Consulting Services ― Affirmative Action Plan Outsourcing ― Compensation Analysis ― EEO/AA Litigation Support (Plaintiff and Defendant) ― Job Analysis ― Test Development and Test Validation • BCG’s Software Products ― Adverse Impact Toolkit™ ― AutoAAP® affirmative action plan development software ― AutoGOJA® job analysis software ― C4 call center testing software ― COMPARE™ compensation analysis software ― CritiCall® dispatcher/call-taker personnel selection software ― ENCOUNTER soft skills video situational judgment testing software ― OPAC® office skills testing software ― TVAP™ Test Validation & Analysis Program software www.Biddle.com