Developing a Hiring System
OK, Enough Assessing:
Who Do We Hire??!!
Who Do You Hire??
Name
Interview
Reference
Checks
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
OK
Good
75%
Hire
Tamika Good
Work Sample
Knowledge
Test
Personality
Inventory
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
Test 1
Fail
Pass
Test 2
Pass
Interview
Fail
Fail
Reject
Pass
Pass
Background
Fail
Profile Matching Approach

Emphasizes “ideal” level of KSA
–

Issues
–
–

e.g., too little attention to detail may produce
sloppy work; too much may represent
compulsiveness
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
–
–
–

Develop a composite rating for each attribute
–
–
–

Desirable
Acceptable
Unacceptable
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
Eliminate applicants with unacceptable
qualifications
Then hire candidates with as many
desirable ratings as possible
Finally, hire as needed from applicants
with “acceptable” ratings
1.
2.
3.
–
Optional: “weight” attributes by importance
Sample Decision Table
Name
Customer
Service
Attention to
Detail
Conscientiousness
Computer
Skills
Work
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
Desirable
Acceptable
Acceptable
Acceptable
Tamika Acceptable
Using the Decision Table 1: More
Positions than Applicants
Name
Customer
Service
Attention to
Detail
Conscientiousness
Computer
Skills
Work
Knowledge
Hiring
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
Desirable
Acceptable
Tamika Acceptable
Unacceptable Unacceptable Not Hire
Acceptable
Acceptable
Hire
Using the Decision Table 2: More
Applicants than Positions
Name
Customer
Service
Attention to
Detail
Conscientiousness
Computer
Skills
Work
Knowledge
Hiring
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
Desirable
Acceptable
Tamika Acceptable
Unacceptable Unacceptable Not Hire
Acceptable
Acceptable
Hire 3
Numerical Decision Approach
Eliminate applicants with unacceptable
qualifications
Convert ratings to a common scale
1.
2.
–
3.
Obtained score/maximum possible score
Weight by importance of attribute and
measure to develop composite score
Numerical Decision Approach
Attention to Detail
Interview
Personality Test
References
Work Sample
Importance
0.25
0.2
0.3
0.2
0.3
Ability to Work with Others 0.25
Interview
Personality Test
References
Work Sample
0.2
0.3
0.2
0.3
Work Specific Knowledge
0.4
Interview
Knowledge Test
Application Form
0.2
0.6
0.2
Computer Skill
0.1
Application Form
Work Sample
0.2
0.8
Numerical Decision Approach
Attention to Detail
Interview (1-5)
Personality Test (%ile)
References (1-3)
Work Sample (1-5)
Importance
0.25
0.2
0.3
0.2
0.3
Ability to Work with Others 0.25
Interview (1-5)
Personality Test (%ile)
References (1-3)
Work Sample (1-5)
Work Specific Knowledge 0.4
Interview (1-5)
Knowledge Test (%ile)
Application Form (1-3)
Computer Sk ill
0.1
Application Form (1-3)
Work Sample (1-5)
TOTAL
Susan
90.0
4/5
0.80
1
1
Stan
74.0
3/5
0.60
1
4/5
Sally
67.3
3/5
0.40
2/3
1
Sam
84.3
1
0.90
2/3
4/5
0.2
0.3
0.2
0.3
64.3
3/5
0.5
2/3
4/5
97.6
1
0.92
1
1
88.5
4/5
0.75
1
1
79.5
4/5
0.85
1
3/5
0.2
0.6
0.2
70.9
3/5
0.76
2/3
74.5
3/5
0.82
2/3
90.0
4/5
0.9
1
91.8
4/5
0.93
1
0.2
0.8
100
1
1
77.0
84
1
4/5
81.1
100
1
1
85.0
84
1
4/5
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