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

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Presentation on theme: "Hires 5 Offers 10 Interviews 40 Invites 60 Applicants 240 Adapted from R.H. Hawk, The Recruitment Function (New York: American Management Association,"— Presentation transcript:

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 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

5 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)

6 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

7 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

8 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.

9 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

10 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

11 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

12 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 passsing 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

13 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


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