1 / 28

Selection and Staffing III

Selection and Staffing III. Learning objectives. Fairness versus bias Know the legal environment of selection Distinguish between types of differences Mean difference Differential validity Differential prediction/regression Differential item/test functioning. Fairness and Bias.

paulcasey
Download Presentation

Selection and Staffing III

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Selection and Staffing III

  2. Learning objectives • Fairness versus bias • Know the legal environment of selection • Distinguish between types of differences • Mean difference • Differential validity • Differential prediction/regression • Differential item/test functioning

  3. Fairness and Bias • Some predictors show mean differences between racial/ethnic groups • Example: cognitive ability • Roughly, whites have a mean IQ of 100, Asians about an SD higher, African Americans about an SD lower (SD equals about 15) • Even if this wasn’t the case, we should consider issues of fairness and bias

  4. Fairness and Bias • Fairness is a subjective appraisal that reflects societal values about whether a selection decision is just • Bias is a concept that has matured with time, but is now concerned with whether a predictor misrepresents a person’s likelihood of satisfactory future performance • Interrelated issues, but very different

  5. First a quick distinction • Adverse (or disparate) treatment • Intentional discrimination • Employer knowingly and willfully treats minority applicants in a different manner than majority applicants • Adverse impact • Unintentional discrimination • Employment practice incidentally treats minority applicants differently than majority applicants

  6. Fairness • Since fairness is a subjective judgment, what constitutes a fair selection process is largely defined by the law and by professional guidelines • Several laws have clarified issues • Griggs vs. Duke Power Co. • Civil Rights Act of 1965, Title VII • Civil Rights Act of 1991

  7. Griggs vs. Duke Power • Duke Power required high school diploma • At the time, about 12% of African Americans had diplomas, about 34% of Caucasians did • Supreme Court determined the process for analyzing adverse impact • Is there adverse impact? • If so, the burden shifts to the employer; must show selection system is job-related • Duke couldn’t, Court found in favor of Griggs

  8. Civil Rights Act of 1964 • Required that selection processes be job related • Bona Fide Occupational Qualifications • Allows for gender, religion, race-based decisions if there is a genuine business necessity • Ex: Catholic Church allowed to accept only males as priests • Ex: A script calls for a Latino actor for a part • Created the EEOC

  9. Civil Rights Act of 1991 • Several topics covered • Most important for this class: Made Race- or Gender-Norming illegal • No separate cutoffs for different groups • No separate norms • No bonus points for race/gender/ethnic membership • Considerable impact on how organizations can pursue diversity objectives

  10. How to define adverse impact • How different does it take to be different? • If 50% of men pass the test, and 10% of women do, is there adverse impact? • If 50% of men pass, and 45% of women do, is there adverse impact?

  11. The 4/5ths Rule • Rule of thumb: If the passing rate for the minority group is 4/5ths (80%) of the passing rate for the majority group, adverse impact is considered to have occurred • Compute: minority pass rate/majority pass rate • If the result is less than 80%, adverse impact • So, from our second example • (45%)/(50%) = .90, or 90%, so no adverse impact

  12. Bias • Three issues • What are we measuring? • Does the predictor make unbiased predictions of future performance? • Jargonized • What’s the construct validity of the predictor? • Does the test demonstrate intercept or slope differences between groups

  13. Assessing Bias • Mean difference • On average, there is a difference in scores between minority and majority group members • Differential validity • The relationship between the selection test and job performance is different for minority and majority group members • Differential Prediction/ Regression • A particular score on the test is associated with a different level of job performance for minority and majority group members

  14. Over- vs. under-prediction • Over-prediction occurs when the regression implies a higher level of job performance than really happens • Under-prediction occurs when the regression implies a lower level of performance than really happens • Rarely find examples of differential regression • When it happens, we usually find over-prediction (Hartigan & Wigdor, 1989)

  15. DIF/DTF • Differential item/test functioning • Assessed using item response theory • Item or test works differently for minority vs. majority group members • If a minority person has a lower chance of getting the item right than a majority person of the same ability level, the item displays DIF • If the test has a bunch of biased items, then it will display DTF

  16. DIF

  17. DIF

More Related