Differential Item Functioning And Adverse Impact

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Adverse Impact

This text is the best single repository for a comprehensive examination of the scientific research and practical issues associated with adverse impact. Adverse impact occurs when there is a significant difference in organizational outcomes to the disadvantage of one or more groups defined on the basis of demographic characteristics such as race, ethnicity, gender, age, religion, etc. This book shows, based on scientific research, how to design selection systems that minimize subgroup differences. The primary object of this volume in the SIOP series is to bring together renowned experts in this field to present their viewpoints and perspectives on what underlies adverse impact, where we are in terms of assessing it and what we may have learned (or not learned) about minimizing it.
Differential Item Functioning and Adverse Impact

This study serves as a comparative analysis of two measures for detecting differential item functioning (DIF) in item responses of 29,171 applicants on a 49 item selection test. The methods compared in this study were two of the more commonly used DIF detection procedures in the testing arena: the Mantel-Haenszel chi-square and the logistic regression procedure. The study focused on the overall effect each method had on adverse impact when used for the removal of items from a test. The study found that the presence of adverse impact findings were decreased by the removal of items displaying DIF, and that the effect on adverse impact differed by method of DIF detection. The study does not however, provide enough evidence to support the use of one DIF detection method over the other in applied settings where considerations such as cost and test reliability are of concern.
Adverse Impact and Test Validation

Adverse impact analyses and test validation promote social justice and equity. Employers who unknowingly use invalid tests or recruitment procedures that have an adverse impact are reducing minority and/or female representation in their workforce, unfairly screening out qualified workers and (worst of all) just plain discriminating. Dan Biddle's Adverse Impact and Test Validation provides you with analyses that allow you to identify which of your selection procedures have adverse impact. The validation steps will help you decide whether to keep the selection procedure (because it's valid), change it, or stop using it altogether. This second edition contains new material on using multiple regression to evaluate pay practices and provides step-by-step instructions for using SPSS or Excel for evaluating your company's pay practices for possible inequities. New content on how to define "Internet applicants" and set up defensible Basic Qualifications (BQs) for online recruiting will help employers ensure compliance with EEO regulations and screen in qualified applicants. Specific guidelines for developing and validating written job knowledge tests, such as those used for police and fire promotional testing, have also been included in this new edition. The downloadable resources include tools (which may be used on a trial evaluation basis) describing several of the functions described in the book, including Adverse Impact Toolkit®, Test Validation and Analysis Program® (TVAP®), Guidelines Oriented Job Analysis® (GOJA®) Manual, and Content Validity Checklists. This highly pragmatic guide goes beyond the concepts, theories and ideas behind adverse impact and test validation. It not only explains what to do but crucially, also shows you how to do it. The second edition has been expanded to include two brand new chapters with a new Appendix and comes with new editions of the accompanying software. As a means of protecting your organization from litigation, damage to employee relations and to your corporate reputation, Adverse Impact and Test Validation is a 'must-have' purchase for human resource professionals, testing and recruitment specialists.