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Chapter 11: Decision MakingChapter 11: Decision Making
Chapter 12: Final MatchChapter 12: Final Match
Part 5Part 5Staffing Activities:Staffing Activities:
EmploymentEmployment
McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc., All Rights Reserved.
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CHAPTER ELEVENCHAPTER ELEVEN
Decision MakingDecision Making
Screen graphics created by:Jana F. Kuzmicki, PhD
Troy State University-Florida and Western Region
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Organization StrategyOrganization Strategy HR and Staffing StrategyHR and Staffing Strategy
Staffing Policies and Programs
Staffing System and Retention Management
Support Activities
Legal compliance
Planning
Job analysis
Core Staffing Activities
Recruitment: External, internal
Selection:Measurement, external, internalEmployment:Decision making, final match
OrganizationVision and Mission
Goals and Objectives
Staffing Organizations ModelStaffing Organizations Model
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Chapter OutlineChapter Outline
Choice of Assessment Method Validity Coefficient Correlation with Other
Predictors Adverse Impact Utility
Determining Assessment Scores Single Predictor Multiple Predictors
Hiring Standards and Cut Scores Description of Process Consequences of Cut Scores Methods to Determine Cut
Scores Professional Guidelines
Methods of Final Choice Random Selection Ranking Grouping
Decision Makers HR Professionals Managers Employees
Legal Issues
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Choice of Assessment MethodChoice of Assessment Method
Validity coefficient
Correlation with other predictors
Adverse impact
Utility
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Validity CoefficientValidity Coefficient
Practical significance Extent to which predictor adds value to prediction of
job success Assessed by examining
Sign Magnitude
Validities above .15 are of moderate usefulness Validities above .30 are of high usefulness
Statistical significance Assessed by probability or p values Reasonable level of significance is p < .05
Face validity
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Correlation With Other PredictorsCorrelation With Other Predictors
To add value, a predictor must add to prediction of success above and beyond forecasting powers of current predictors
A predictor is more useful the
Smaller its correlation with other predictors and
Higher its correlation with the criterion
Predictors are likely to be highly correlated with one another when their content domain is similar
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Adverse ImpactAdverse Impact
Role of predictorDiscriminates between people in terms of the
likelihood of their job successWhen it discriminates by screening out a
disproportionate number of minorities and women, Adverse impact exists which may result in legal problems
IssuesWhat if one predictor has high validity and high
adverse impact?And another predictor has low validity and low
adverse impact?
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Utility AnalysisUtility Analysis Expected gains derived from using a predictor 1. Hiring success gain from using a new predictor (relative to
current predictor): Uses Taylor-Russell Tables Focuses on proportion of new hires who turn out to be
successful Requires information on:
Selection ratio: Number hired / number of applicants Base rate: proportion of employees who are successful Validity coefficient of current and “new” predictors
2. Economic gain from using a predictor (relative to random selection): Uses Economic Gain Formula
Focuses on the monetary impact of using a predictor Requires a wide range of information on current employees,
validity, number of applicants, cost of testing, etc.
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Utility Analysis: Taylor-Russell TablesUtility Analysis: Taylor-Russell Tables If base rate = .30, impact of validity and selection ratio
If base rate = .80, impact of validity and selection ratio
Selection Ratio
Validity .10 .70
.20 43% 33%
.60 77% 40%
Selection Ratio
Validity .10 .70
.20 89% 83%
.60 99% 90%
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Utility Analysis: Economic Gain FormulaUtility Analysis: Economic Gain Formula∆U = (T * N * rxy * SDy * Zs) – (N * Cy)
Where:
∆U = expected $ increase to org. versus random selection
T = tenure of selected group (how long new hires are expected to stay)
N = number of applicants selected
rxy = correlation between predictor and job performance value
SDy = standard deviation of job performance
Zs = average standard predictor score of selected group
N = number of applicants
Cy = cost per applicant
Apply the formula above. Assume the following estimates are reasonable:
T = 3; Ns=50; r = .35; 40% of pay = $15,000; Zs = .7; N = 200; C = $200
Discuss the issues involved in estimating gain in this example
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Limitations of Utility AnalysisLimitations of Utility Analysis
1. While most companies use multiple selection measures, utility models assume decision is Whether to use a single selection measure rather than Select applicants by chance alone
2. Important variables are missing from model EEO / AA concerns Applicant reactions
3. Utility formula based on simplistic assumptions Validity does not vary over time Non-performance criteria are irrelevant Applicants are selected in a top-down manner
and all job offers are accepted
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Determining Assessment ScoresDetermining Assessment Scores
Single predictor Multiple predictors - 3 approaches
Compensatory model - Exh. 11.3 Clinical prediction Unit weighting Rational weighting Multiple regression Choosing among weighting schemes - Exh. 11.4
Multiple hurdles modelCombined model - Exh. 11.5: Combined Model for
Recruitment Manager
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Relevant Factors: SelectingRelevant Factors: Selectingthe Best Weighting Schemethe Best Weighting Scheme
Do decision makers have considerable experience and insight into selection decisions?
Is managerial acceptance of the selection process important?
Is there reason to believe each predictor contributes relatively equally to job success?
Are there adequate resources to use involved weighting schemes?
Are conditions under which multiple regression is superior satisfied?
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Exh. 11.5: Combined ModelExh. 11.5: Combined Modelfor Recruitment Managerfor Recruitment Manager
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Hiring Standards and Cut ScoresHiring Standards and Cut Scores
Issue -- What is a passing score?Score may be a
Single score from a single predictor or
Total score from multiple predictors
Description of processCut score - Separates applicants who advance from
those who are rejected
Consequences of cut scoresExh. 11.6: Consequences of Cut Scores
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Exh. 11.6: Consequences of Cut ScoresExh. 11.6: Consequences of Cut Scores
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Hiring Standards and Cut ScoresHiring Standards and Cut Scores(continued)(continued)
Methods to determine cut scores
Exh. 11.7: Use of Cut Scores in Selection Decisions
Minimum competency
Top-down
Banding
Professional guidelines
Exh. 11.8: Professional Guidelines for SettingCutoff Scores
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Exh. 11.7: Use of CutExh. 11.7: Use of CutScores in Selection DecisionsScores in Selection Decisions
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Methods of Final ChoiceMethods of Final Choice
Random selection
Each finalist has equal chance of being selected
Ranking
Finalists are ordered from most to least desirable based on results of discretionary assessments
Grouping
Finalists are banded together into rank-ordered categories
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Decision MakersDecision Makers
Role of human resource professionals Determine process used to design and manage selection
system Contribute to outcomes based on initial assessment methods Provide input regarding who receives job offers
Role of managers Determine who is selected for employment Provide input regarding process issues
Role of employees Provide input regarding selection procedures
and who gets hired, especially in team approaches
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Legal IssuesLegal Issues
Legal issue of importance in decision makingCut scores or hiring standards
Uniform Guidelines on EmployeeSelection Procedures (UGESP) If no adverse impact, guidelines are silent on cut
scores
If adverse impact occurs, guidelines become applicable
Choices among finalists
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Ethical IssuesEthical Issues
Issue 1Do you think companies should use banding in
selection decisions? Defend your position.
Issue 2 Is clinical prediction the fairest way to combine
assessment information about job applicants, or are the other methods (unit weighting, rational weighting, multiple regression) more fair? Why?
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