Uniform Guidelines on Employee Selection Procedures

By Teva J. Scheer. 2007

475 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products (2016, methodology): “Then using a rule such as the 80% (or four-fifths) rule advocated in the study of disparate impact [29], we can constraint the relative risk of harm for the protected versus unprotected group to a maximum value such as 5/4: ∫ Xp ∫”
Fair Inference on Outcomes (2017, background): “This is a guideline (not a legal test) advocated by the Equal Employment Opportunity Commission (EEOC.”
Certifying and Removing Disparate Impact (2014, background): “Copyrights for components of this work owned by others than the author(s) must be honored.”
Subgroup Differences in Situational Judgment Test Performance: A Meta-Analysis (2008, unknown): “We did not include studies using the How Supervise? test (File, 1943) in the analyses because the items were substantially different than those found in other SJTs (see McDaniel et al., 2007). We also excluded studies where the SJT response instructions could not be coded because of lack of informat”
DOING COMPETENCIES WELL: BEST PRACTICES IN COMPETENCY MODELING (2011, background): “The ability to train, assess, select, promote, and reward employees in alignment to a desired future state can help speed organizations through transition (Cummings & Worley, 2008).”