Society for Industrial and Organizational Psychology > Research & Publications > TIP > TIP Back Issues > 2018 > October

SIOP Launches Advocacy Initiative on Veterans Transition

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In honor of Veterans Day, SIOP is proud to announce a new initiative that applies I-O evidence-based research to the transition of service members to the civilian workforce.

This effort is a collaboration between Lewis-Burke Associates LLC and the Government Relations Advocacy Team (GREAT) at SIOP. Lewis-Burke will spearhead the government relations outreach, connect with key policymakers, and seek opportunities to profile I-O findings to federal stakeholders interested in veterans’ transition. These efforts will be in close collaboration with an expert SIOP team led by Adam Kabins that includes Meredith Kleykamp, Julia Bayless, Peter Reiley, and Chris Stone. The team will coordinate SIOP efforts and I-O research and practice findings relevant to the transition of veterans to the workplace; they will provide timely feedback and expertise to Lewis-Burke on pressing federal issues as they arise.

Department of Labor Relies on SIOP Members for Adverse Impact Guidance

By Barbara Ruland

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The book, Adverse Impact Analysis: Understanding Data, Statistics, and Risk, edited by SIOP Fellows Scott Morris and Eric Dunleavy, has been cited as a resource on practical significance in EEO Analysis by the US Department of Labor’s Office of Federal Contract Compliance Programs (OFCCP).  The citation is specifically for the chapter written by Fred Oswald, Eric Dunleavy, and Amy Shaw.

As part of its FAQ on the topic, the OFCCP defines practical significance as “whether an observed disparity in employment opportunities or outcomes reflects meaningful harm to the disfavored group,” and acknowledges that practical significance, not just statistical significance, should be considered in compliance reviews. This distinction is important because, as the OFCCP notes, “a virtually unnoticeable disparity in, for instance, selection rates, may nevertheless be statistically significant due to the size of the data set.”

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