Adverse Impact Analysis Understanding Data Statistics And Risk 1st Edition Scott B Morris by Scott B. Morris, Eric M. Dunleavy 9781315301426, 9781138855854, 9781138855847, 9781315301433, 1315301423, 1138855855, 1138855847, 1315301431 instant download after payment.

Compliance with federal equal employment opportunity regulations, including civil rights laws and affirmative action requirements, requires collection and analysis of data on disparities in employment outcomes, often referred to as adverse impact. While most human resources (HR) practitioners are familiar with basic adverse impact analysis, the courts and regulatory agencies are increasingly relying on more sophisticated methods to assess disparities. Employment data are often complicated, and can include a broad array of employment actions (e.g., selection, pay, promotion, termination), as well as data that span multiple protected groups, settings, and points in time. In the era of "big data," the HR analyst often has access to larger and more complex data sets relevant to employment disparities. Consequently, an informed HR practitioner needs a richer understanding of the issues and methods for conducting disparity analyses.

This book brings together the diverse literature on disparity analysis, spanning work from statistics, industrial/organizational psychology, human resource management, labor economics, and law, to provide a comprehensive and integrated summary of current best practices in the field. Throughout, the description of methods is grounded in the legal context and current trends in employment litigation and the practices of federal regulatory agencies.

The book provides guidance on all phases of disparity analysis, including:

• How to structure diverse and complex employment data for disparity analysis
• How to conduct both basic and advanced statistical analyses on employment outcomes related to employee selection, promotion, compensation, termination, and other employment outcomes
• How to interpret results in terms of both practical and statistical significance
• Common practical challenges and pitfalls in disparity analysis and strategies to deal with these issues