1. Part 5 Staffing Activities: Employment
Chapter 11: Decision Making
Chapter 12: Final Match
McGraw-Hill/Irwin
© 2006 The McGraw-Hill Companies, Inc., All Rights Reserved.

2. Decision Making CHAPTER ELEVEN Screen graphics created by:
Jana F. Kuzmicki, PhD
Troy State University-Florida and Western Region

3. Staffing Organizations Model
Vision and Mission
Goals and Objectives
Organization Strategy
HR and Staffing Strategy
Staffing Policies and Programs
Support Activities
Core Staffing Activities
Legal compliance
Recruitment: External, internal
Planning
Selection: Measurement, external, internal
Job analysis
Employment: Decision making, final match
Staffing System and Retention Management

4. Chapter 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

5. Choice of Assessment Method
Validity coefficient
Correlation with other predictors
Adverse impact
Utility

6. Validity Coefficient Practical significance Statistical 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

7. Correlation 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

8. Adverse Impact Role of predictor Issues
Discriminates between people in terms of the likelihood of their job success
When it discriminates by screening out a disproportionate number of minorities and women,
Adverse impact exists which may result in legal problems
Issues
What if one predictor has high validity and high adverse impact?
And another predictor has low validity and low adverse impact?

9. Utility 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.

10. Utility 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%

11. Utility 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

12. Limitations 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

13. Determining 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 model
Combined model - Exh. 11.5: Combined Model for Recruitment Manager

14. Relevant Factors: Selecting the 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?

15. Exh. 11.5: Combined Model for Recruitment Manager

16. Hiring 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 process
Cut score - Separates applicants who advance from those who are rejected
Consequences of cut scores
Exh. 11.6: Consequences of Cut Scores

17. Exh. 11.6: Consequences of Cut Scores

18. Hiring Standards and Cut Scores (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 Setting Cutoff Scores

19. Exh. 11.7: Use of Cut Scores in Selection Decisions

20. Methods 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

21. Decision Makers Role of human resource professionals Role of managers
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

22. Legal Issues Legal issue of importance in decision making
Cut scores or hiring standards
Uniform Guidelines on Employee Selection Procedures (UGESP)
If no adverse impact, guidelines are silent on cut scores
If adverse impact occurs, guidelines become applicable
Choices among finalists

23. Ethical Issues Issue 1 Issue 2
Do 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?