Decisions by mudoc123

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									Developing a Hiring System

  OK, Enough Assessing:
   Who Do We Hire??!!
        Who Do You Hire??
                     Reference                 Knowledge   Personality
Name     Interview    Checks     Work Sample      Test     Inventory

Lee      Excellent      OK          Good         90%       Hire

Maria    Excellent    Glowing     Very Good      85%       Hire

Alan     Good          ???        Excellent      90%       Caution

Juan     Marginal       OK          Good         81%       Hire

Frank    Excellent    Glowing       Poor         70%       Hire

Tamika Good             OK          Good         75%       Hire
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 + 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”)
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?
Multiple Hurdle Model
                                                       Finalist
                                                       Decision




            Pass             Pass                   Pass           Pass
  Test 1           Test 2            Interview             Background

     Fail             Fail                   Fail           Fail




                                    Reject
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
                       Profile Match Exam ple


4.5


 4


3.5


 3


2.5
                                                              Ideal

 2


1.5


  1


0.5


 0
      Detail   Experience            C. Service   Sales Apt
                      Profile Match Exam ple


6




5




4


                                                             Ideal
                                                             John
3
                                                             Sam
                                                             Sue


2




1




0
    Detail   Experience             C. Service   Sales Apt
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
          94
          93
          89
          88
          87
          87
          86
          81
          81
          80
          79
          79
          78
          72
          70
          69
          67
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
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
         Customer     Attention to   Conscient-      Computer        Work
Name      Service        Detail       iousness         Skills      Knowledge

Lee      Acceptable    Desirable      Desirable      Acceptable     Acceptable

Maria    Desirable     Desirable      Acceptable     Acceptable     Desirable

Alan     Desirable    Acceptable     Unacceptable    Acceptable     Acceptable

Juan     Acceptable   Acceptable      Acceptable     Acceptable     Acceptable

Frank    Desirable     Desirable      Desirable     Unacceptable   Unacceptable

Tamika Acceptable      Desirable      Acceptable     Acceptable     Acceptable
        Using the Decision Table 1:
        More Positions than Applicants

        Customer     Attention to    Conscient-     Computer       Work        Hiring
Name     Service        Detail        iousness        Skills     Knowledge     Action

Lee     Acceptable    Desirable       Desirable     Acceptable    Acceptable    Hire

Maria   Desirable     Desirable      Acceptable     Acceptable    Desirable     Hire

Alan    Desirable    Acceptable     Unacceptable    Acceptable    Acceptable   Not Hire

Juan    Acceptable   Acceptable      Acceptable     Acceptable    Acceptable    Hire

Frank   Desirable     Desirable       Desirable    Unacceptable Unacceptable Not Hire

Tamika Acceptable     Desirable      Acceptable     Acceptable    Acceptable    Hire
        Using the Decision Table 2:
        More Applicants than Positions
        Customer     Attention to    Conscient-     Computer       Work        Hiring
Name     Service        Detail        iousness        Skills     Knowledge     Action

Lee     Acceptable    Desirable       Desirable     Acceptable    Acceptable    Hire 2

Maria   Desirable     Desirable      Acceptable     Acceptable    Desirable     Hire 1

Alan    Desirable    Acceptable     Unacceptable    Acceptable    Acceptable   Not Hire

Juan    Acceptable   Acceptable      Acceptable     Acceptable    Acceptable    Hire 4

Frank   Desirable     Desirable       Desirable    Unacceptable Unacceptable Not Hire

Tamika Acceptable     Desirable      Acceptable     Acceptable    Acceptable    Hire 3
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
                                 Importance
  Attention to Detail            0.25
                    Interview            0.2
              Personality Test           0.3
                  References             0.2
                 Work Sample             0.3

  Ability to Work with Others 0.25
                     Interview           0.2
              Personality Test           0.3
                   References            0.2
                 Work Sample             0.3

  Work Specific Knowledge    0.4
                   Interview             0.2
            Knowledge Test               0.6
           Application Form              0.2

  Computer Skill            0.1
           Application Form              0.2
               Work Sample               0.8
           Numerical Decision Approach
                                 Importance    Susan   Stan    Sally   Sam
Attention to Detail              0.25           90.0   74.0    67.3    84.3
               Interview (1-5)           0.2     4/5     3/5     3/5   1
     Personality Test (%ile)             0.3    0.80   0.60    0.40    0.90
            References (1-3)             0.2    1      1         2/3     2/3
         Work Sample (1-5)               0.3    1        4/5   1         4/5

Ability to Work with Others 0.25                64.3   97.6    88.5    79.5
               Interview (1-5)           0.2     3/5   1         4/5     4/5
      Personality Test (%ile)            0.3    0.5    0.92    0.75    0.85
            References (1-3)             0.2     2/3   1       1       1
           Work Sample (1-5)             0.3     4/5   1       1         3/5

Work Specific Knowledge 0.4                     70.9   74.5    90.0    91.8
              Interview (1-5)            0.2     3/5    3/5      4/5     4/5
     Knowledge Test (%ile)               0.6    0.76   0.82     0.9    0.93
     Application Form (1-3)              0.2     2/3    2/3    1       1

Computer Sk ill            0.1                 100     84      100     84
    Application Form (1-3)               0.2    1      1        1      1
       Work Sample (1-5)                 0.8    1        4/5    1        4/5

                      TOTAL                     77.0   81.1     85.0   86.1
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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