Selection Decisions MANA 5341 Dr. George Benson

Selection Decisions First, how to deal with multiple predictors? Second, how to make a final decision?

Dealing With Multiple Predictors “Mechanical” techniques superior to judgment Combine predictors (Compensatory)  Sum total – Unit weighting  Weighting predictors – Rational weighting  Ranking  Profile matching Judge each independently  Multiple Hurdles / Multiple Cutoff Hybrid selection systems

Compensatory Methods Unit weighting P1 + P2 + P3 + P4 = Score Rational weighting (.10) P1 + (.30) P2 + (.40) P3 + (.20) P4 = Score Ranking RankP1 + RankP2 +RankP3 + RankP4 = Score Profile Matching D 2 = Σ (P(ideal) – P(applicant)) 2

Combined Selection Model Selection Stage Selection Test Decision Model Applicants  Candidates Application BlankMinimum Qualification Hurdle Candidates  Finalists Four Ability Tests Work Sample Rational Weighting Hurdle Finalists  Offers Structured Interview Unit Weighting Rank Order Offers  Hires Drug Screen Final Interview Hurdle

Final Selection Top Down Selection (Rank) vs. Cutoff scores  Is the predictor linearly related to performance?  How reliable are the tests? 1.Top-down method – Rank order 2.Minimum cutoffs – Passing Scores

Setting Cutoff Scores Based on the percentage of applicants you need to hire (yield ratio). “Thorndike’s predicted yield”  You need 5 warehouse clerks and expect 50 to apply. 5 / 50 =.10 (10%) means 90% of applicants rejected  Cutoff Score set at 90th percentile  Z score 1 = 84 th percentile Based on a minimum proficiency score  Based on validation study linked to job analysis  Incorporates SEM (validity and reliability)

Selection Outcomes PREDICTION PERFORMANCE No PassPass Regression Line Cut Score 90% Percentile

Selection Outcomes PREDICTION High Performer Low Performer True Positive True Negative Type 2 Error False Positive Type 1 Error False Negative PERFORMANCE No HireHire

Selection Outcomes: Normal Case PREDICTION High Performer Low Performer PERFORMANCE UnqualifiedQualified Regression Line Cut Score

Selection Outcomes: High Risk PREDICTION Acceptable Unacceptable PERFORMANCE UnqualifiedQualified Regression Line Cut Score

Banding Function of test reliability Standard Error of Measure  Band of + or – 2 SEM  95% Confidence interval If the top score on a test is 95 and SEM is 2, then scores between 95 and 91 should be banded together.

Final Decision  Random Selection  Ranking  Grouping Role of Discretion or “Gut Feeling”

Hiring Employment-at-will VS. Employment Contract

Hiring Employment-at-will  Employer not obligated to retain for specific time  Termination for any legitimate reason without liability  Disclaimers in applications and offer letter Employment Contract  Employment for a definite period of time  Entitlements for compensation / benefits

Offer Letter Legal document  Should be carefully worded  Creates obligations (intended or unintended)  Letter from HR or legal Attraction tool  Final incentive for prospective hire  Should make new employee feel welcome  Letter from manager or company president

Job Offer Content Start date  Usually allows notice for previous employer Base Compensation and Benefits  Benefits –blanket statement to refer to HR documents  Compensation - payroll schedule not annual rate  FLSA designation Conditions of employment  Reference checks, drug screen, I-9, medical exam etc. Other conditions  Confidentiality / Non-compete / Arbitration Working hours Acceptance date

Job Offer Content Integration clause  Letter contains all terms and conditions  Letter supercedes all other documents or conversations Employment-at-will disclaimer  Employment is not for a specific duration  Employment terminable by either party at any time  Include the “magic words” but don’t go overboard

Responses Yes  Follow immediately with positive communication  Instructions for start day (when, where, etc) No  Try to find out why  Working conditions vs. $$$ Maybe / More time....  Find out when  Answer questions

Those Not Selected Maintain positive relations with fair treatment  Inform promptly  Thank them for applying and acknowledge the effort  Tell them they were a finalist with good skills to offer Most companies do not provide details of decision Keep applications on file Notify by mail or by phone for higher jobs

Independent Contractors “You have been retained as an independent contractor for the XYZ project to be completed by ABC date.” “As a contractor you are not entitled to any benefits company will not withhold any taxes, FICA or other deductions.”

Independent Contractors “You will be paid $$$ according to this schedule contingent upon....” “As a contractor you are responsible for all safety equipment and procedures employee liabilities including....”

Managing Retention Cost of turnover and replacement Types of Turnover  Involuntary turnover  Voluntary turnover out of company control  Voluntary turnover that company can control Turnover and performance  Increases costs  Decreases productivity Positive vs. Negative Turnover

Analyzing Turnover Diagnosing retention problems  Active: Employee surveys  Active: Exit interviews  Passive: Research and HRIS data mining How much is too much turnover?  Depends on the organization  Depends on the position

Federal Human Capital Survey 34.6% of federal employees said they were considering leaving their jobs. Half of these will retire, half plan to quit. 68 % of workers are satisfied with their jobs 64 % are satisfied with their pay 65 % are happy with their retirement benefits 91 % believe they do important work Less than 50% are satisfied with recognition for doing a good job 30 % saying awards programs provide real motivation. 27 % said steps have been taken to deal with poor performers 36 % said their leaders generate high levels of motivation among workers.

HRIS Data Mining

Why Do People Leave? Desirability of Leaving  Job satisfaction  “Shocks”  Switching costs  Career progression General Ease of Leaving  Labor market conditions  Human capital (education and training)  Job offers