1. Applied Psychology in Human Resource Management seventh edition Cascio & Aguinis
Power Point Slides developed by
Ms. Elizabeth Freeman
University of South Carolina Upstate
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2. Chapter 8 Fairness in Employment Decisions
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3. To this point, HRM decisions depend upon. Laws
To this point, HRM decisions depend upon Laws System utility (cost & benefit) Processes Tests – Reliability Validity
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4. What does fairness mean. Treating all people. alike, justly, equitably
What does fairness mean? Treating all people alike, justly, equitably Having no adverse impact on any group of individuals
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5. How do you determine fairness. By analyzing the
How do you determine fairness? By analyzing the differential validity and predictive bias among groups
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6. Must keep in mind that HRM decisions are based on individual differences measures. Therefore, HRM decisions will have some discriminatory effects.
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7. Fairness in employment decisions means then that HRM decisions make justifiable and wise discriminatory decisions.
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8. Resources for guiding HRM fairness Uniform Guidelines on Employee Selection Procedures (1978) Standards for Educational and Psychological Testing (1999) Principles for the Validation and Use of Personnel Selection Procedures (2003)
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9. Resources for guiding HRM fairness Computer program to explore decision making scenarios
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10. Legal precedence guiding
Legal precedence guiding HRM fairness Ninth Circuit Court of Appeals Officers for Justice v. Civil Service Commission of the City and County of San Francisco, Seventh Circuit Court of Appeals Chicago Firefighters Local 2 v. City of Chicago, 2001
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11. Fairness challenges. number subjects per group. unbiased criterion
Fairness challenges number subjects per group unbiased criterion comprehension of differences differential validity differential prediction value systems societal costs
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12. Fairness research focuses 1. Efficacy of selection decisions
Fairness research focuses Efficacy of selection decisions analysis of differential validity within subgroups 2. Accuracy of performance predictions analysis of mean job performances and differential validity
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13. Basic Fairness Procedure Critical Definitions. 1. Adverse impact
Basic Fairness Procedure Critical Definitions Adverse impact when HRM selections for members of subgroups are less than 4/5 or 80% of group with highest selection rate may exist fairly, may exist unfairly
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14. Basic Fairness Procedure Critical Definitions Differential Validity when significant difference exists between two subgroups’ validity coefficients when correlations in one or both groups are significant
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15. Basic Fairness Procedure Critical Definitions Single Group Validity when no significant difference exists between two subgroups’ validity coefficients when significant difference does exist for one group’s predictor – criterion relationship
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16. Basic Fairness Procedure 1. Divide data by group & subgroup,. 2
Basic Fairness Procedure Divide data by group & subgroup, Determine predictor & criterion correlation Analyze fairness implications
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17. Basic Fairness Procedure 1. Divide data by group & subgroup, Example
Basic Fairness Procedure 1. Divide data by group & subgroup, Example Managerial Jobs by Age Race Ethnicity Gender
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18. Basic Fairness Procedure. 2
Basic Fairness Procedure Determine predictor & criterion correlation For all managerial jobs using Predictor = Test Score Criterion = Performance Rating Plot the relationship by gender
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19. Basic Fairness Procedure 3. Analyze fairness implications for. a
Basic Fairness Procedure 3. Analyze fairness implications for a. Positive validity b. Zero validity c. Positive validity but adverse impact d. Positive validity combined groups, invalid for separate groups e. Equal validity, unequal predictor means f. Equal validity, unequal criterion means g. Equal predictor means, valid for nonminority only h. Unequal criterion means and validity only for nonminority
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20. Basic Fairness Procedure 3. Analyze fairness implications a
Basic Fairness Procedure 3. Analyze fairness implications a. Positive validity Predictor – criterion relationship is the same for both subgroups and elliptical in shape Conclude fairness, validity, and legality supported
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21. Basic Fairness Procedure 3. Analyze fairness implications b
Basic Fairness Procedure 3. Analyze fairness implications b. Zero validity Predictor – criterion relationship is the same for both subgroups but circular in shape Conclude that no differential validity, no point to consider predictor
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22. Basic Fairness Procedure 3. Analyze fairness implications. c
Basic Fairness Procedure 3. Analyze fairness implications c. Positive validity but adverse impact Predictor – criterion relationship shows differences per subgroups and elliptical in shape Conclude valid and legal adverse impact but only if criterion necessity proven
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23. Basic Fairness Procedure 3. Analyze fairness implications. d
Basic Fairness Procedure 3. Analyze fairness implications d. Positive validity combined groups, invalid for separate groups Predictor – criterion relationship is high for entire group but low or zero for either subgroup and elliptical in shape Conclude unfair, invalid, illegal, and discriminatory
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24. Basic Fairness Procedure 3. Analyze fairness implications. e
Basic Fairness Procedure 3. Analyze fairness implications e. Equal validity, unequal predictor means Predictor – criterion relationship is similar for both subgroups, elliptical in shape, but predictor means differ Conclude with successful performance as foundation the use of different cut scores for decisions is fair, valid, and legal most but not all of the time
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25. Basic Fairness Procedure 3. Analyze fairness implications. f
Basic Fairness Procedure 3. Analyze fairness implications f. Equal validity, unequal criterion means Predictor – criterion relationship is similar for both subgroups, elliptical in shape, but criterion means differ Conclude fairness questionable, validity questionable, but no adverse impact
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26. Basic Fairness Procedure 3. Analyze fairness implications g
Basic Fairness Procedure 3. Analyze fairness implications g. Equal predictor means, valid for nonminority only Predictor – criterion relationship differs for both subgroups, shapes differ, but valid for nonminority only Conclude fairness questionable, validity limited, no adverse impact, but definite social implications
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27. Basic Fairness Procedure 3. Analyze fairness implications h
Basic Fairness Procedure 3. Analyze fairness implications h. Unequal criterion means, unequal validity, only for nonminority group Predictor – criterion relationship differs for both subgroups, shapes differ, but valid for nonminority only Conclude fairness questionable, validity limited, some adverse impact minorities, definite social implications
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28. Basic Fairness Summary Perfect fairness may not be possible when HRM decisions applied to heterogeneous groups Implementing different HRM decision systems may be empirically more fair but may be perceived with suspicion and lose any credibility.
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29. Basic Fairness Summary Additional Differential Validity Issues Very few well-controlled studies Samples sizes existing research too small Predictors not always relevant to criterion Lack of unbiased, relevant, reliable criteria Limited number of cross-validated studies
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30. Assessing Differential Prediction & Moderator Variables To completely study and understand fairness, differential predictions for subgroups must be considered Differential predictions focus on the slope of the differential validity coefficients. Slopes are best understood by considering the regression line (line of best fit) between the predictor and criterion variances
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31. Assessing Differential Prediction & Moderator Variables Regression line accuracy can be improved by considering the sub-groupings as additional variables or moderators Considering multiple moderators brings in the concept of Moderated Multiple Regressions (MMR) or R²
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32. Assessing Differential Prediction & Moderator Variables Interesting evidence for MMR research Differences over predict job performance Cognitive Differences Physical Ability differences Personality differences For HRM, decisions would tend to hire more minorities rather than fewer
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33. Assessing Differential Prediction & Moderator Variables Cognitive Differences Minorities tended to do less well on job than test scores predicted for Dutch, African-American, Hispanics Physical Ability Differences Gender differences existed but varied by occupation considered Personality Differences Gender differences found by occupation
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34. Assessing Differential Prediction & Moderator Variables Problem to consider small sample sizes for minority groups increase chance that procedure deemed unfair when procedure is fair decrease statistical power
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35. Assessing Differential Prediction & Moderator Variables To avoid low MMR statistical power, carefully plan a validation study to include technical feasibility & credible results
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36. To reduce adverse impact 1. Improve minority recruiting strategy 2
To reduce adverse impact Improve minority recruiting strategy 2. Use cognitive abilities in combination with noncognitive predictors 3. Use specific cognitive abilities measures 4. Use differential weighting for the various criterion facets 5. Use alternate modes of presenting test stimuli 6. Enhance face validity 7. Implement test-score banding to select among applicants
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37. Test-score banding. considers distributive justice for
Test-score banding considers distributive justice for appropriateness of HRM testing decisions HRM tries to maximize profitability maximizing profits may lead to adverse impact values based HRM may lead to decreased profitability
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38. Test-score banding. Sliding-band method –
Test-score banding Sliding-band method – considers range of test scores as equivalent given imperfect reliabilities for tests maximizes both utility and social objectives
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39. Test-score banding Criterion-referenced banding method
Test-score banding Criterion-referenced banding method considers range of test scores (predictors) and range of performance scores (criteria) also maximizes utility and social objectives
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40. Criterion-referenced banding strengths. Use of validity evidence
Criterion-referenced banding strengths Use of validity evidence Bandwidths are wider Inclusion relevant criterion data Use of reliability information
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41. Criterion-referenced banding weaknesses 1. Possible legal issues 2
Criterion-referenced banding weaknesses Possible legal issues 2. Possible violation scientific values 3. Possible violation intellectual values 4. Emotions associated with Affirmative Action Programs 5. Conflict between goals of research and organizations 6. Measurement objections
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42. Social and Interpersonal Context of Employment Testing Fairness requires professionalism, courtesy, compassion, & respect Perceived unfairness may lead to negative organization impression litigation challenges
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43. Social and Interpersonal Context of Employment Testing Fairness perceptions include (1) distributive justice - outcomes (2) procedural justice – processes to reach decisions
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44. Public Policy While not always popular, tests and measurements serve public in several ways (1) diagnostic – to implement remedial programs (2) assessing candidate qualifications (3) protection from false credentials
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45. Public Policy Each generation must reconcile the meaning of equal employment opportunities Policies are not for or against tests and measurements, policies are about how tests & measurements are used
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