Recommendations of a Technical Advisory Committee on Adverse Impact Analysis

Cohen, David J.; Dunleavy, Eric M.; Aamodt, Mike G.; Geier, John; Mueller, Lorin M.; Silberman, Mickey; Sinar, Evan F.

doi:10.1037/e518362013-208

Cited by 3 publications

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“…This highlights the importance of examining measures of effect size as well as statistical significance. A variety of practical significance tests have been recommended for assessing adverse impact (Cohen et al, 2010), but little is known about the data requirements and stability of these methods. In addition, many of these methods lack established guidelines for what constitutes a substantial difference, which will inevitably lead to varying applications and outcomes in litigation and enforcement settings.…”

Section: Discussionmentioning

confidence: 99%

“…The LMP test has been widely accepted in the medical and statistics fields, and it is starting to receive attention in the equal employment opportunity literature (Cohen, Aamodt, & Dunleavy, 2010) and in court cases ( Strong v. Blue Cross of California , 2010; Delgado-O'Neil v. City of Minneapolis , 2010). Still, it remains relatively unknown among human resources professionals and personnel psychologists.…”

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confidence: 99%

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Using Lancaster's mid-P correction to the Fisher's exact test for adverse impact analyses.

Biddle¹

2011

Journal of Applied Psychology

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Adverse impact is often assessed by evaluating whether the success rates for 2 groups on a selection procedure are significantly different. Although various statistical methods have been used to analyze adverse impact data, Fisher's exact test (FET) has been widely adopted, especially when sample sizes are small. In recent years, however, the statistical field has expressed concern regarding the default use of the FET and has proposed several alternative tests. This article reviews Lancaster's mid-P (LMP) test (Lancaster, 1961), an adjustment to the FET that tends to have increased power while maintaining a Type I error rate close to the nominal level. On the basis of Monte Carlo simulation results, the LMP test was found to outperform the FET across a wide range of conditions typical of adverse impact analyses. The LMP test was also found to provide better control over Type I errors than the large-sample Z-test when sample size was very small, but it tended to have slightly lower power than the Z-test under some conditions.

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Section: Discussionmentioning

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Using Lancaster's mid-P correction to the Fisher's exact test for adverse impact analyses.

Biddle¹

2011

Journal of Applied Psychology

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“…Both strategies make sense in certain contexts. Because courts now generally identify the majority comparison group as the group with highest favourable decision rate (D. Cohen et al., 2019), this would have notable effects on which groups are adversely affected. As will be seen, Whites, young employees and men had lower attrition scores in this study and were therefore treated as the majority groups (i.e.…”

Section: Methodsmentioning

confidence: 99%

Empirical attrition modelling and discrimination: Balancing validity and group differences

Speer

2021

Human Res Mgmt Journal

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Attrition models combine variables into statistical algorithms to understand and predict employee turnover. People analytics teams and external vendors use attrition models to offer insights and to develop organisational interventions. However, if attrition models or other data‐driven models inform employment decisions, model scores may then be subjected to civil rights laws and diversity concerns resulting from group differences in scores. This paper discusses adverse impact when building attrition models, outlining how researchers test for adverse impact in this context, strategies to reduce group differences and how attrition modelling and other human resources ‘big data’ predictions fit within larger validity frameworks. Procedures were applied to field data in an applied demonstration of an attrition model with disparate impact. Model revisions resulted in adverse impact reductions while simultaneously maintaining model validity. Collectively, this paper provides timely attention to important aspects of the people analytics, turnover and legal domains.

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“…Given the difficulty in revising the Uniform Guidelines , perhaps a better alternative might be to leave the Uniform Guidelines unchanged but instead persuade judges and EEO agencies to rely on more current and science‐based documents such as the Principles and the Standards . For specific issues such as adverse impact analyses or assessment center validity, judges and EEO agencies could also be encouraged to rely on committee “best practices” documents such as the Center for Corporate Equality's (CCE) Technical Advisory Committee Report on Best Practices in Adverse Impact Analyses (Cohen, Aamodt, & Dunleavy, 2010) or the International Task Force on Assessment Center Guidelines (2009)— Guidelines and Ethical Considerations for Assessment Center Operations .…”

Section: Option 3: Keep the Uniform Guidelines But Educate Eeo Decisimentioning

confidence: 99%

Guidelines, Principles, Standards, and the Courts: Why Can't They All Just Get Along?

Dunleavy

Aamodt

Morgan³

et al. 2011

Ind. organ. psychol.

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Recommendations of a Technical Advisory Committee on Adverse Impact Analysis

Cited by 3 publications

References 0 publications

Using Lancaster's mid-P correction to the Fisher's exact test for adverse impact analyses.

Using Lancaster's mid-P correction to the Fisher's exact test for adverse impact analyses.

Empirical attrition modelling and discrimination: Balancing validity and group differences

Guidelines, Principles, Standards, and the Courts: Why Can't They All Just Get Along?

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