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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?