Presentation on theme: "Developing a Hiring System OK, Enough Assessing: Who Do We Hire??!!"— Presentation transcript:
Developing a Hiring System OK, Enough Assessing: Who Do We Hire??!!
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
List of Critical Attributes
Performance Attributes Matrix
Who Do You Hire??
Common Decision-Making Errors Switching to non-performance factors Succumbing to the “Tyranny of the Best” Reverting to “intuition” or “gut feel”
Information Overload!! Leads to: – Reverting to gut instincts – Mental Gymnastics
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
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”)
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?
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
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?
Test 1Test 2 Interview Background Finalist Decision Reject Multiple Hurdle Model Fail Pass
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
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
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
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)
Applicant Total Scores
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
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
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
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
Sample Decision Table
Using the Decision Table 1: More Positions than Applicants
Using the Decision Table 2: More Applicants than Positions
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
Numerical Decision Approach
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