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.

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

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

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

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

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

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

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?

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.

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%

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

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

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

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?

Exh. 11.5: Combined Model for Recruitment Manager

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

Exh. 11.6: Consequences of Cut Scores

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

Exh. 11.7: Use of Cut Scores in Selection Decisions

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

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

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

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?