1. Hires 5 Offers 10 Interviews 40 Invites 60 Applicants 240 Adapted from R.H. Hawk, The Recruitment Function (New York: American Management Association, 1967). Yield Pyramid

2. Recruitment Sources (Techniques) In-House (e.g., hiring or promotion from within the organization) Newspapers, Trade/Professionals Publications Outside Companies (Headhunters, Employment/Temp Agencies, Executive Search Firms Job or College Fairs Internet-based Private sites (e.g., Monster) Professional Organizations Company web site

3. Some Factors in Considering Recruiting Sources Cost Time Requirements Number and Quality of Applicants Type of Job (e.g., manual labor, managerial) Type of Applicant (knowledge, skills, demographic and minority representation)

4. Recruiter Characteristics --- Demographics (e.g., gender, race, age) Functional job area (similarity to job being recruited) Personality (e.g., warmth, enthusiasm, supportive, personable) Administration of Recruitment --- Promptness of follow-up contact (short timeframe is best) Amount of information requested of applicant (more information = less # of applicants) Other Recruitment Issues

5. Money and time spent on recruiting by HR = 16% Evaluation of recruitment effectiveness (often limited to criteria such as meeting deadlines) Extent of recruiter training (most not trained; less than half of organizations used formal, standardized training programs) Organizational Emphasis on Recruitment

6. Realistic Job Preview Effects RJP VACCINATION OF EXPECTATIONS SELF SELECTION INTERNAL FOCUS OF CONTROL COMMITMENT TO CHOICE OF ORGANIZATION PERCEPTION OF HONESTY AND CARING ROLE CLARITY COPING MECHANISMS DEVELOP FOR NEW JOBS INVOLUNTARY TURNOVER JOB PERFORMANCE NEEDS ARE MATCHED TO ORGANIZATIONAL CLIMATE JOB SATISFACTION VOLUNTARY TURNOVER TENURE IN THE ORGANIZATION

7. Selection Ratio (SR) = n N Job openings Applicants Test Validity [Criterion-related]: The extent to which test scores correlate with job performance scores [Range is from 0 to 1.0] Test Utility Key Points

8. Proportion of “Successes” Expected Through the Use of Test of Given Validity and Given Selection Ratio, for Base Rate.60. (From Taylor & Russell, 1939, p. 576) Selection Ratio Validity.05.10.20.30.40.50.60.70.80.90.95.00.60.60.60.60.60.60.60.60.60.60.60.05.64.63.63.62.62.62.61.61.61.60.60.10.68.67.65.64.64.63.63.62.61.61.60.15.71.70.68.67.66.65.64.63.62.61.60.20.75.73.71.69.67.66.65.64.63.62.61.25.78.76.73.71.69.68.66.65.63.62.61.30.82.79.76.73.71.69.68.66.64.62.61.35.85.82.78.75.73.71.69.67.65.63.62.40.88.85.81.78.75.73.70.68.66.63.62.45.90.87.83.80.77.74.72.69.66.64.62.50.93.90.86.82.79.76.73.70.67.64.62.55.95.92.88.84.81.78.75.71.68.64.62.60.96.94.90.87.83.80.76.73.69.65.63.65.98.96.92.89.85.82.78.74.70.65.63.70.99.97.94.91.87.84.80.75.71.66.63.75.99.99.96.93.90.86.81.77.71.66.63.80 1.00.99.98.95.92.88.83.78.72.66.63.85 1.00 1.00.99.97.95.91.86.80.73.66.63.90 1.00 1.00 1.00.99.97.94.88.82.74.67.63.95 1.00 1.00 1.00 1.00.99.97.92.84.75.67.63 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00.86.75.67.63 Note: A full set of tables can be found I Taylor and Russell (1939) and in McCormick and Ilgen (1980, Appendix B). (SR)

9. Mean Standard Criterion Score of Accepted Cases in Relation to Test Validity and Selection Ratio (From Brown & Ghiselli, 1953, p. 342) Validity Coefficient Selection Ratio.00.05.10.15.20.25.30.35.40.45.50.55.60.65.70.75.80.85.90.95 1.00.05.00.10.21.31.42.52.62.73.83.94 1.04 1.14 1.25 1.35 1.46 1.56 1.66 1.77 1.87 1.98 2.08.10.00.09.18.26.35.44.53.62.70.79.88.97 1.05 1.14 1.23 1.32 1.41 1.49 1.58 1.67 1.76.15.00.08.15.23.31.39.46.54.62.70.77.85.93 1.01 1.08 1.16 1.24 1.32 1.39 1.47 1.55.20.00.07.14.21.28.35.42.49.56.63.70.77.84.91.98 1.05 1.12 1.19 1.26 1.33 1.40.25.00.06.13.19.25.32.38.44.51.57.63.70.76.82.89.95 1.01 1.08 1.14 1.20 1.27.30.00.06.12.17.23.29.35.40.46.52.58.64.69.75.81.87.92.98 1.04 1.10 1.16.35.00.05.11.16.21.26.32.37.42.48.53.58.63.69.74.79.84.90.95 1.00 1.06.40.00.05.10.15.19.24.29.34.39.44.48.53.58.63.68.73.77.82.87.92.97.45.00.04.09.13.18.22.26.31.35.40.44.48.53.57.62.66.70.75.79.84.88.50.00.04.08.12.16.20.24.28.32.36.40.44.48.52.56.60.64.68.72.76.80.50.00.04.07.11.14.18.22.25.29.32.36.40.43.47.50.54.58.61.65.68.72.60.00.03.06.10.13.16.19.23.26.29.32.35.39.42.45.48.52.55.58.61.64.65.00.03.06.09.11.14.17.20.23.26.28.31.34.37.40.43.46.48.51.54.57.70.00.02.05.07.10.12.15.17.20.22.25.27.30.32.35.37.40.42.45.47.50.75.00.02.04.06.08.11.13.15.17.19.21.23.25.27.30.32.33.36.38.40.42.80.00.02.04.05.07.09.11.12.14.16.18.19.21.22.25.26.28.30.32.33.35.85.00.01.03.04.05.07.08.10.11.12.14.15.16.18.19.20.22.23.25.26.27.90.00.01.02.03.04.05.06.07.08.09.10.11.12.13.14.15.16.17.18.19.20.95.00.01.01.02.02.03.03.04.04.05.05.06.07.07.08.08.09.09.10.10.11 Selection Ratio Example

10. 1) The 1 st day on the job is crucial! It is important to manage it well and make it a positive time. Employees remember it for years, particularly if it is an unpleasant experience 2) Impressions formed during the first 60-90 days are difficult to alter. So, it is important to make this time a positive experience for newly-hired employees 3) Ensure that new employees see how their job fits within the framework of the overall organization. (As such, the organization needs to communicate information about it’s goals and objectives) 4) Avoid ‘information overload.” It’s best to provide the new employee with information in reasonable amounts and in a meaningful sequence 5) Ensure that the new employee’s immediate supervisor is ultimately responsible for the orientation program 6) Social and family adjustment concerns should be addressed in the orientation program Basic Orientation Principles

11. Simple Regression Equation Multiple Regression y = a + bx Test Score Slope y-intercept Predicted Score  y = a + b x + b x + b x ….. Predicted Score  y-intercept 1 1 2 2 3 3 Weights Regression Basic Process: All applicants take every test. Scores are weighted and combined to yield a predicted score for each applicant. Applicants scoring above a set cutoff score are considered for hire Key Points: Regression is a compensatory approach. That is, a high score on one test can compensate for a low score on another. Best for tests to not relate to each other, but relate highly to the criterion.

12. How Four Job Applicants with Different Predictor Scores Can Have the Same Predicted Criterion Score Using Multiple Regression Analysis Applicant Score on X Score on X Predicted Criterion Score 1 2 A 25 0 100 B 0 50 100 C 20 10 100 D 15 20 100 Note: Based on the equation Y = 4X + 2X. 1 2  Compensatory Example

13. Predictor 1 Criterion Predictor 2 R = r + r For example, if r =.60 and r =.50, then R = (.60) + (.50) =.36 +.25 =.61 22 2 c.12 1c 2c 1c 2c c.12 r r 1c 2c Independent Predictors

14. R = 2 c.12 r r - 2r r r 2 1c 2c 12 1c 2c For example, if the two predictors intercorrelate.30, given the validity coefficients from the previous example And r =.30, we will have 12 R = =.47 2 c.12 1 - r 2 12 (.60) + (.50) - 2(.30)(.60)(.50) 2 1 – (.30) 2 rr r 1c 2c 12 Interrelated Predictors Predictor 1 Predictor 2 Criterion

15. WAB 100 0 Pass Fail Cutoff score Paper & Pencil Math Test 100 0 Pass Fail Cutoff score Paper & Pencil Aptitude Test 100 0 Pass Fail Cutoff score Basic Process: All applicants take every test. Applicant must achieve a passing score on every test to be considered for hire. Key Point: A multiple cut-off approach can lead to different decisions regarding who to hire versus using a regression approach. Multiple Cutoff Approach xx x

16. Interview 100 0 Pass Fail Cutoff score Paper & Pencil Knowledge Test 100 0 Pass Fail Cutoff score Work Sample Test 100 0 Pass Fail Cutoff score Multiple Hurdle Approach Basic Process : All applicants take the 1 st test. Pass/fail decisions are made on the 1 st and subsequent tests and only those who pass can continue on to the next test [a sequential process]. Key Point : Useful when a lengthy, costly, and complex training process is required for the position. Eliminated from the selection process xxxxx xxx xxxxx xx

17. Basic concept: Small differences in test scores might reasonably be due to measurement error. Therefore, you do not want small differences in test scores to trump all other consideration in ranking individuals in hiring.” (p. 82). Banding

18. Banding (cont.) SED Banding Types FixedSliding........ Both use the top score to establish the top of the band All those from the band are selected before those from the lower band.......... Bands slide down after each person is removed from the top (bands re-established) 98 94 92 88 98 94 92 88 Example of a band of 6 points