Applied Psychology in Human Resource Management seventh edition Cascio & Aguinis Power Point Slides developed by Ms. Elizabeth Freeman University of South.

Applied Psychology in Applied Psychology in Human Resource Human Resource

Management Management seventh editionseventh edition

Cascio & AguinisCascio & Aguinis

Power Point Slides developed by Ms. Elizabeth Freeman

University of South Carolina Upstate

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall1

Chapter 8 Chapter 8

Fairness Fairness in in

Employment Employment DecisionsDecisions

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 2

To this point, HRM decisions depend upon

LawsSystem utility (cost & benefit)

ProcessesTests –

ReliabilityValidity


What does fairness mean?

Treating all people

alike, justly, equitably

Having no adverse impact on any group of individuals


How do you determine fairness?

By analyzing the differential validity and predictive bias among groups


Must keep in mind that HRM decisions are based on individual differences measures.

Therefore, HRM decisions will have some discriminatory effects.


Fairness in employment decisions means then that HRM decisions make justifiable and wise discriminatory decisions.


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)


Resources for guiding HRM fairness

Computer program to explore decision

making scenarios

www.cudenver.edu/~haguinis/mmr


Legal precedence guiding HRM fairness

Ninth Circuit Court of AppealsOfficers for Justice v. Civil Service Commission of the City and County of San Francisco, 1992

Seventh Circuit Court of AppealsChicago Firefighters Local 2 v. City of Chicago, 2001


Fairness challenges

number subjects per group unbiased criterioncomprehension of differences

differential validity differential prediction

value systemssocietal costs


Fairness research focuses

1. Efficacy of selection decisions analysis of differential validity within subgroups

2. Accuracy of performance

predictions analysis of mean job performances and differential validity


Basic Fairness Procedure

Critical Definitions

1. 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




2. Differential Validity when significant difference exists

between two subgroups’ validity coefficients

when correlations in one or both groups are significant




3. 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



1. Divide data by group & subgroup,

2. Determine predictor & criterion correlation

3. Analyze fairness implications



1. Divide data by group & subgroup, Example

Managerial Jobs by AgeRaceEthnicityGender



2. Determine predictor & criterion correlation

For all managerial jobs usingPredictor = Test ScoreCriterion = Performance Rating

Plot the relationship by gender



3. Analyze fairness implications for a. Positive validity b. Zero validityc. Positive validity but adverse

impactd. Positive validity combined groups,

invalid for separate groups e. Equal validity, unequal predictor

meansf. Equal validity, unequal criterion

meansg. Equal predictor means, valid for

nonminority only h. Unequal criterion means and

validity only for nonminority




a. Positive validity

Predictor – criterion relationship is the same for both subgroups and elliptical in shape

Conclude fairness, validity, and legality supported



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




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




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




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




f. Equal validity, unequal criterion means

Predictor – criterion relationship is similarfor both subgroups, elliptical in shape, but criterion means differ

Conclude fairness questionable, validity questionable, but no adverse impact




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




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


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.


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


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



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



Interesting evidence for MMR research

Differences over predict job performance

Cognitive DifferencesPhysical Ability differencesPersonality differences

For HRM, decisions would tend to hire more minorities rather than fewer



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



Problem to consider

small sample sizes for minority groups

increase chance that procedure deemed unfair when procedure is fair

decrease statistical power



To avoid low MMR statistical power, carefully plan a validation study to include technical feasibility & credible results


To reduce adverse impact

1. Improve minority recruiting strategy2. Use cognitive abilities in combination with noncognitive predictors 3. Use specific cognitive abilities measures4. Use differential weighting for the various

criterion facets5. Use alternate modes of presenting test

stimuli6. Enhance face validity7. Implement test-score banding to select

among applicants Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 36

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


Test-score banding

Sliding-band method – considers range of test scores as equivalent given imperfect reliabilities for tests

maximizes both utility and social objectives


Test-score banding

Criterion-referenced banding methodconsiders range of test scores

(predictors) and range of performance scores (criteria)

also maximizes utility and social objectives


Criterion-referenced banding strengths

Use of validity evidence Bandwidths are widerInclusion relevant criterion dataUse of reliability information


Criterion-referenced banding weaknesses

1. Possible legal issues2. 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


Social and Interpersonal Context of Employment Testing

Fairness requires professionalism, courtesy, compassion, & respect Perceived unfairness may lead to

negative organization impressionlitigation challenges


Social and Interpersonal Context of Employment Testing

Fairness perceptions include (1) distributive justice - outcomes

(2) procedural justice – processes to reach decisions


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


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


Applied Psychology in Human Resource Management seventh edition Cascio & Aguinis Power Point Slides developed by Ms. Elizabeth Freeman University of South.

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