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Published byJosef Jessup Modified over 9 years ago
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Developing a Hiring System OK, Enough Assessing: Who Do We Hire??!!
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Summary of Performance-Based Hiring Understand performance expectations List attributes that predict performance Match attributes with selection tools Choose/develop each tool effectively Make performance-based decisions
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List of Critical Attributes
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Performance Attributes Matrix
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Who Do You Hire??
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Common Decision-Making Errors Switching to non-performance factors Succumbing to the “Tyranny of the Best” Reverting to “intuition” or “gut feel”
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Information Overload!! Leads to: – Reverting to gut instincts – Mental Gymnastics
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Combining Information to Make Good Decisions “Mechanical” methods are superior to “Judgment” approaches – Multiple Regression – Multiple Cutoff – Multiple Hurdle – Profile Matching – High-Impact Hiring approach
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Multiple Regression Approach Predicted Job perf = a + b 1 x 1 + b 2 x 2 + b 3 x 3 – x = predictors; b = optimal weight Issues: – Compensatory: assumes high scores on one predictor compensate for low scores on another – Assumes linear relationship between predictor scores and job performance (i.e., “more is better”)
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Multiple Cutoff Approach Sets minimum scores on each predictor Issues – Assumes non-linear relationship between predictors and job performance – Assumes predictors are non-compensatory – How do you set the cutoff scores?
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How Do You Set Cut Scores? Expert Judgment Average scores of current employees – Good employees for profile matching – Minimally satisfactory for cutoff models Empirical: linear regression
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Multiple Cutoff Approach Sets minimum scores on each predictor Issues – Assumes non-linear relationship between predictors and job performance – Assumes predictors are non-compensatory – How do you set the cutoff scores? – If applicant fails first cutoff, why continue?
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Test 1Test 2 Interview Background Finalist Decision Reject Multiple Hurdle Model Fail Pass
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Multiple Hurdle Model Multiple Cutoff, but with sequential use of predictors – If applicant passes first hurdle, moves on to the next May reduce costs, but also increases time
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Profile Matching Approach Emphasizes “ideal” level of KSA – e.g., too little attention to detail may produce sloppy work; too much may represent compulsiveness Issues – Non-compensatory – Small errors in profile can add up to big mistake in overall score Little evidence that it works better
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How Do You Compare Finalists? Multiple Regression approach –Y (predicted performance) score based on formula Cutoff/Hurdle approach – Eliminate those with scores below cutoffs – Then use regression (or other formula) approach Profile Matching – Smallest difference score is best – ∑ (Ideal-Applicant) across all attributes In any case, each finalist has an overall score
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Making Finalist Decisions Top-Down Strategy – Maximizes efficiency, but also likely to create adverse impact if CA tests are used Banding Strategy – Creates “bands” of scores that are statistically equivalent (based on reliability) – Then hire from within bands either randomly or based on other factors (inc. diversity)
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Applicant Total Scores 94 93 89 88 87 86 81 80 79 78 72 70 69 67
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Limitations of Traditional Approach “Big Business” Model – Large samples that allow use of statistical analysis – Resources to use experts for cutoff scores, etc. – Assumption that you’re hiring lots of people from even larger applicant pools
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A More Practical Approach Rate each attribute on each tool – Desirable – Acceptable – Unacceptable Develop a composite rating for each attribute – Combining scores from multiple assessors – Combining scores across different tools – A “judgmental synthesis” of data Use composite ratings to make final decisions
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Improving Ratings 1. Use intuitive rating system Unacceptable – Did not demonstrate levels of attribute that would predict acceptable performance Acceptable – Demonstrated levels that would predict acceptable performance Desirable – Demonstrated levels that would predict exceptional performance
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Categorical Decision Approach 1. Eliminate applicants with unacceptable qualifications 2. Then hire candidates with as many desirable ratings as possible 3. Finally, hire as needed from applicants with “acceptable” ratings – Optional: “weight” attributes by importance
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Sample Decision Table
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Using the Decision Table 1: More Positions than Applicants
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Using the Decision Table 2: More Applicants than Positions
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Numerical Decision Approach 1. Eliminate applicants with unacceptable qualifications 2. Convert ratings to a common scale – Obtained score/maximum possible score 3. Weight by importance of attribute and measure to develop composite score
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Numerical Decision Approach
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Summary: Decision-Making Focus on critical requirements Focus on performance attribute ratings – Not overall evaluations of applicant or tool Eliminate candidates with unacceptable composite ratings on any critical attribute Then choose those who are most qualified: – Make offers first to candidates with highest numbers of desirable ratings
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