Unformatted text preview: s by regression (a compensatory approach) Multiple regression analysis Results in equation for combining test scores into a composite, weighted based on each predictor’s correlation with performance 27 Relationship Between Relationship Between Predictor Overlap & Criterion Prediction Figure 6.4
The Relationship between
Predictor Overlap and
Criterion Prediction 28 Combining Multiple Assessments
Combining Multiple Assessments Multiple Cutoff Approach
• Applicant must score above cutoff on each test – non
compensatory Cog. Ability Personality Interview 29 Large Staffing Projects
Large Staffing Projects Concessions must be made: Labor intensive Requires an actuarial strategy
Utility can be an issue (Cost of testing can be assessment procedures are not feasible expensive) Fairness is a critical issue
Standard, wellestablished, & feasible selection strategies are important 30 Small Staffing Projects
Small Staffing Projects Luxury of using wider range of assessment tools
Adverse impact is less of an issue
Fairness is still a key issue
Rational, jobrelated, & feasible selection strategies are important 31 Module 4: Legal Issues in
Module 4: Legal Issues in
Staffing Decisions Charges of employment discrimination Involve violations of Title VII of 1964 CRA, ADA, or ADEA IO psychologists often serve as expert witnesses in these lawsuits Consequences can be substantial Most often brought by individual claiming unfair termination 32 Discrimination
Discrimination The ability or power to see or make fine distinctions; discernment.
Discrimination is what we hope to do; but only on the right details 33 Intentional Discrimination or Intentional Discrimination or Adverse Treatment
Plaintiff attempts to show that employer treated plaintiff differently than majority applicants or employees AKA: Disparate Treatment 34 Hiring Discrimination
Hiring Discrimination Disparate Treatment:
• Evidence that a member of a protected group is treated differently from other job applicants Could discrimination (in the legal context) occur even if everyone is treated the same?
• Consider Griggs v. Duke Power (1971) 35 Unintentional Discrimination or Unintentional Discrimination or Adverse Impact (AI) Acknowledges employer may not have intended to discriminate against plaintiff but employer practice had AI on group to which plaintiff belongs
Everyone treated the same, but the outcome was substantially different based on group membership Griggs v. Duke Power (1971) 36 Determination of Adverse Determination
Impact Burden of proof on plaintiff to show: a) he/she belongs to a protected group, &
b) members of protected group were statistically disadvantaged compared to majority employees 37 “80%” or “4/5ths” rule
Guideline for assessing whether there is evidence of AI Plaintiffs must show that protected group received only 80% of desirable outcomes received by majority group in order to meet burden of demonstrating AI Results in AI ratio 38 “80%” or “4/5ths” Rule (cont'd)
Guideline set forth by EEOC
IR = Minority Selection Rate
Majority Selection Rate If IR ratio is less than .8, there is Adverse Impact
Small samples affect results Bur...
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This note was uploaded on 10/01/2013 for the course PSYC 371 taught by Professor Skinner during the Spring '12 term at Illinois Tech.
- Spring '12