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Chapter 11: Decision Making Chapter 12: Final Match
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Chapter 11: Decision Making Chapter 12: Final Match

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

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

  3. Organization Vision and Mission Goals and Objectives Organization Strategy HR and Staffing Strategy Staffing Organizations Model 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. 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 Chapter Outline

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

  6. Validity 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

  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 • 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

  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 mannerand 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: Selectingthe 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 Modelfor 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 SettingCutoff Scores

  19. Exh. 11.7: Use of CutScores 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 • 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 proceduresand 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 EmployeeSelection 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 • 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?