Developing a Hiring System

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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 b1x1 b2x2 b3x3
• 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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Multiple Hurdle Model
Finalist Decision
Background
Interview
Test 1
Test 2
Pass
Pass
Pass
Pass
Fail
Fail
Fail
Fail
Reject
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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 87 86 81 81
80 79 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 youre 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

• 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

• Eliminate applicants with unacceptable
  qualifications
• Then hire candidates with as many desirable
  ratings as possible
• 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

• Eliminate applicants with unacceptable
  qualifications
• Convert ratings to a common scale
• Obtained score/maximum possible score
• Weight by importance of attribute and measure to
  develop composite score

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Numerical Decision Approach
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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