Logistic Regression (LR) is a statistical method that is useful for evaluating why adverse impact may be occurring in a hiring or promotional process. While classic 2 X2 table analysis can identify whether the observed hiring or promotion rates between two groups are significantly different, LR can determine if job-relevant qualification factors (e.g., experience or education) of the individuals included in the analysis explain the difference in hiring or promotion (as opposed to gender or race being the reason).
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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).
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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?
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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
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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
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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
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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
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