Download Free A Comparison Of Adverse Impact Analysis And Statistical Analysis For Evaluating Group Differences On Written And Oral Selection Procedures Book in PDF and EPUB Free Download. You can read online A Comparison Of Adverse Impact Analysis And Statistical Analysis For Evaluating Group Differences On Written And Oral Selection Procedures and write the review.

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
Written with human resources professionals, in-house counsel and employment lawyers in mind, readers are introduced to the statistical analysis of adverse impact. Various tools for examining disparate impact are presented in a non-technical manner. Concrete examples and simple calculations demonstrate how these statistical tools can be applied to questions of adverse impact in hiring, promotion, and termination decisions. Traditional areas of vulnerability to adverse impact are discussed, and some emerging areas with potential for adverse impact, such as the use of social media in recruiting and current employment status as a candidate screening tool, are presented. The underlying sources of vulnerability are explored and pending legislation is discussed. The importance of litigation avoidance is stressed, and suggestions for minimizing the risk of employment litigation with proactive statistical analysis are provided. The goal is to give human resources professionals and legal counsel a better understanding of the information their statistical consultants are providing. This leads to an improved ability to identify and correct problem areas that may exist within the organization, as well as to prevent problems from arising in the future.
"Human resources decision-makers are concerned when mean inter-group score differences on selection measures are observed. Moreover, they are not concerned with the magnitude of the differences per Se, but rather with whether those score differences will manifest themselves as adverse impact. An analytical approach was used to estimate for various combinations of selection ratio and minority applicant group representation. the maximum group score difference that would not violate the four-fifths rule. In addition. applicant pools of specific sizes with no mean inter-group score difference on the selection measure were considered to compute the conservative likelihood of encountering an adverse impact situation in a specific applicant sample. The results clearly suggest that the identification of adverse impact and its statistical substantiation will often occur in small applicant pools (i.e.-. 100), even when there is a small inter-group difference on the selection measure. For larger samples (i.e., 500), the results suggest that adverse impact will often he indicated when small mean inter-group selection measure differences are present. It is not clear to what degree the adverse impact found would be statistically substantiated. Research focusing on adverse impact and its statistical substantiation is needed for specific inter-group difference/applicant pool size combinations to create a clearer equivalence between intergroup differences and adverse impact."--DTIC.
Human resources decision-makers are concerned when mean inter-group score differences on selection measures are observed. Moreover, they are not concerned with the magnitude of the differences per Se, but rather with whether those score differences will manifest themselves as adverse impact. An analytical approach was used to estimate for various combinations of selection ratio and minority applicant group representation. the maximum group score difference that would not violate the "four-fifths" rule. In addition. applicant pools of specific sizes with no mean inter-group score difference on the selection measure were considered to compute the conservative likelihood of encountering an adverse impact situation in a specific applicant sample. The results clearly suggest that the identification of adverse impact and its statistical substantiation will often occur in small applicant pools (i.e.-. 100), even when there is a small inter-group difference on the selection measure. For larger samples (i.e., 500), the results suggest that adverse impact will often he indicated when small mean inter-group selection measure differences are present. It is not clear to what degree the adverse impact found would be statistically substantiated. Research focusing on adverse impact and its statistical substantiation is needed for specific inter-group difference/applicant pool size combinations to create a clearer equivalence between intergroup differences and adverse impact.
This book is the leader among the new generation of text books on quality that follow the systems approach to creating quality in products and services; the earlier generations focused solely on parts of the system such as statistical methods, process control, and management philosophy. It follows the premise that the body of knowledge and tools documented by quality professionals and researchers, when employed in designing, creating and delivering the product will lead to product quality, customer satisfaction and reduced waste. The tools employed at the different stages of the product creation cycle are covered in this book using real world examples along with their theoretical bases, strengths and weaknesses. This textbook can be used for training - from shop floor personnel to college majors in business and engineering to practicing professionals. Graduate students training as researchers in the quality field will also find useful material. The book has been used as the text for a Professional Series Massive Open Online Course offered by the Technical University of Munich on edX.org, through which tens of thousands of participants from all over the world have received training in quality methods. According to Professor Dr. Holly Ott, who chose the book for the course, the text is one of the main factors contributing to success of this MOOC. The Third Edition has been fully revised to be friendly for self-study, reflects changes in the standards referenced such as ISO 9000, and includes new examples of application of statistical tools in health care industry. Features: Reviews the history of quality movement in the U.S. and abroad Discusses Quality Cost analysis and quality’s impact on a company’s bottom line Explains finding customer needs and designing the product using House of Quality Covers selection of product parameters using DOE and reliability principles Includes control charts to control processes to make the product right-the-first-time Describes use of capability indices Cp and Cpk to meet customer needs Presents problem solving methodology and tools for continuous improvement Offers ISO 9000, Baldrige and Six Sigma as templates for creating a quality system
The study of and interest in adolescence in the field of psychology and related fields continues to grow, necessitating an expanded revision of this seminal work. This multidisciplinary handbook, edited by the premier scholars in the field, Richard Lerner and Laurence Steinberg, and with contributions from the leading researchers, reflects the latest empirical work and growth in the field.