© 2010 Cengage Learning
1

© 2010 Cengage Learning
Optimal Employee Selection Systems
• Are Reliable
• Are Valid
– Based on a job analysis (content validity)
– Predict work-related behavior (criterion validity)
• Reduce the Chance of a Legal Challenge
– Face valid
– Don’t invade privacy
– Don’t intentionally discriminate
– Minimize adverse impact
• Are Cost Effective
– Cost to purchase/create
– Cost to administer
– Cost to score
2

© 2010 Cengage Learning
• The extent to which a score from a test is consistent and free from errors of measurement
•
Methods of Determining Reliability
– Test-retest (temporal stability)
– Alternate forms (form stability)
– Internal reliability (item stability)
– Scorer reliability
3

© 2010 Cengage Learning
• Measures temporal stability
• Administration
– Same applicants
– Same test
– Two testing periods
• Scores at time one are correlated with scores at time two
• Correlation should be above .70
4

© 2010 Cengage Learning
Problems
• Sources of measurement errors
– Characteristic or attribute being measured may change over time
– Reactivity
– Carry over effects
• Practical problems
– Time consuming
– Expensive
– Inappropriate for some types of tests
5

© 2010 Cengage Learning
Administration
• Two forms of the same test are developed, and to the highest degree possible, are equivalent in terms of content, response process, and statistical characteristics
• One form is administered to examinees, and at some later date, the same examinees take the second form
6

© 2010 Cengage Learning
Scoring
• Scores from the first form of test are correlated with scores from the second form
• If the scores are highly correlated, the test has form stability
7

© 2010 Cengage Learning
Disadvantages
• Difficult to develop
• Content sampling errors
• Time sampling errors
8

© 2010 Cengage Learning
• Defines measurement error strictly in terms of consistency or inconsistency in the content of the test.
• Used when it is impractical to administer two separate forms of a test.
• With this form of reliability the test is administered only once and measures item stability.
9

© 2010 Cengage Learning
• Split-Half method (most common)
– Test items are divided into two equal parts
– Scores for the two parts are correlated to get a measure of internal reliability.
• Spearman-Brown prophecy formula:
(2 x split half reliability) ÷ (1 + split-half reliability)
10

© 2010 Cengage Learning
(2 x split-half correlation)
(1 + split-half correlation)
If we have a split-half correlation of .60, the corrected reliability would be:
(2 x .60) ÷ (1 + .60) = 1.2 ÷ 1.6 = .75
11

© 2010 Cengage Learning
• Cronbach’s Coefficient Alpha
– Used with ratio or interval data.
• Kuder-Richardson Formula
– Used for test with dichotomous items (yes-no true-false)
12

© 2010 Cengage Learning
• Used when human judgment of performance is involved in the selection process
• Refers to the degree of agreement between 2 or more raters
13

© 2010 Cengage Learning
Rate the Waiter’s Performance
(Office Space – DVD segment 3)
14

© 2010 Cengage Learning
• The higher the reliability of a selection test the better. Reliability should be .70 or higher
• Reliability can be affected by many factors
•
If a selection test is not reliable, it is useless as a tool for selecting individuals
15

© 2010 Cengage Learning
16

© 2010 Cengage Learning
• Definition
The degree to which inferences from scores on tests or assessments are justified by the evidence
• Common Ways to Measure
– Content Validity
– Criterion Validity
– Construct Validity
17

© 2010 Cengage Learning
• The extent to which test items sample the content that they are supposed to measure
• In industry the appropriate content of a test of test battery is determined by a job analysis
18

© 2010 Cengage Learning
• Criterion validity refers to the extent to which a test score is related to some measure of job performance called a criterion
•
Established using one of the following research designs:
– Concurrent Validity
– Predictive Validity
– Validity Generalization
19

© 2010 Cengage Learning
• Uses current employees
• Range restriction can be a problem
20

© 2010 Cengage Learning
• Correlates test scores with future behavior
• Reduces the problem of range restriction
• May not be practical
21

© 2010 Cengage Learning
• Validity Generalization is the extent to which a test found valid for a job in one location is valid for the same job in a different location
• The key to establishing validity generalization is meta-analysis and job analysis
22

© 2010 Cengage Learning
Typical Corrected Validity Coefficients for
Selection Techniques
Method
Structured Interview
Cognitive ability
Job knowledge
Work samples
Assessment centers
Biodata
Integrity tests
Situational judgment
Validity
.57
.51
.48
.48
.38
.34
.34
.34
Method
College grades
References
Experience
Validity
.32
.29
.27
Conscientiousness
Unstructured interviews
Interest inventories
Handwriting analysis
.24
.20
.10
.09
Projective personality tests .00
23

© 2010 Cengage Learning
• The extent to which a test actually measures the construct that it purports to measure
• Is concerned with inferences about test scores
• Determined by correlating scores on a test with scores from other test
24

© 2010 Cengage Learning
• The extent to which a test appears to be job related
• Reduces the chance of legal challenge
• Increasing face validity
25

© 2010 Cengage Learning
Locating Test Information
Exercise 6.1
26

© 2010 Cengage Learning
27

© 2010 Cengage Learning
The degree to which a selection device improves the quality of a personnel system, above and beyond what would have occurred had the instrument not been used.
28

© 2010 Cengage Learning
• You have many job openings
• You have many more applicants than openings
• You have a valid test
• The job in question has a high salary
• The job is not easily performed or easily trained
29

© 2010 Cengage Learning
Taylor-Russell Tables
Proportion of Correct Decisions
The Brogden-Cronbach-Gleser Model
30

© 2010 Cengage Learning
Utility Analysis
Taylor-Russell Tables
• Estimates the percentage of future employees that will be successful
• Three components
– Validity
– Base rate (successful employees ÷ total employees)
– Selection ratio (hired ÷ applicants)
31

© 2010 Cengage Learning
• Suppose we have
– a test validity of .40
– a selection ratio of .30
– a base rate of .50
• Using the Taylor-Russell
Tables what percentage of future employees would be successful?
32

© 2010 Cengage Learning
50
%
.
r.
.05 .10 .20 .30 .40 .50 .60 .70 .80 .90 .95
00 .50
.50
.
50 .50
.
50 .
50 .50
.
50 .
50 .50
.
50
.10
.58
.57
.56
.55
.54
.53
.53
.52
.51
.51
.50
.59
.60
.61
.62
.53
.54
.56
.57
.62
.65
.67
.70
.54
.56
.58
.60
.66
.70
.73
.78
.55
.58
.61
.63
.70
.75
.80
.86
.56
.60
.63
.67
.75
.80
.85
.92
.58
.62
.66
.70
.79
.85
.90
.97
.59
.64
.69
.74
.84
.90
.95
.99
.61
.67
.73
.76
.90
.95
.99
1.0
.64
.71
.78
.84
.94
.98
1.0
1.0
.67
.74
.82
.88
.60
.70
.80
.90
.20
.30
.40
.50
.54
.55
.55
.52
.52
.53
.54
.52
.53
.53
.53
.51
.51
.52
.52

© 2010 Cengage Learning
• Proportion of Correct Decisions With Test
(Correct rejections + correct acceptances) ÷ Total employees
Quadrant II Quadrant IV Quadrants I+II+III+IV
• Baseline of Correct Decisions
Successful employees ÷ Total employees
Quadrants I + II Quadrants I+II+III+IV
34

i o r e n t i r
C
© 2010 Cengage Learning
10
I
9
6
5
4
3
8
7
IV
2
1
1 x x x
2 x x x x x x x x x x x x x x x x x x
II x
III x x x x x x x
3 4 5 6
Test Score (x)
7 8 x
9 10
35

© 2010 Cengage Learning
• Proportion of Correct Decisions With Test
( 10 + 11 ) ÷
Quadrant II Quadrant IV
(5 + 10 + 4 + 11)
Quadrants I+II+III+IV
= 21 ÷ 30 = .70
• Baseline of Correct Decisions
5 + 10 ÷
Quadrants I + II
5 + 10 + 4 + 11
Quadrants I+II+III+IV
= 15 ÷ 30 = .50
36

© 2010 Cengage Learning
Computing the Proportion of Correct Decisions
Exercise 6.3
37

© 2010 Cengage Learning
38

© 2010 Cengage Learning
9
3
2
1
8
7
6 x
5 I
4 x x x x x x x x x x x x x x
II x x x x
IV x III
1 2 3 4 5 6 7 8 9
Test Scores
39

© 2010 Cengage Learning
• Proportion of Correct Decisions With Test
( 8 + 6 ) ÷
Quadrant II Quadrant IV
(4 + 8 + 6 + 2)
Quadrants I+II+III+IV
= 14 ÷ 20 = .70
• Baseline of Correct Decisions
4 + 8 ÷
Quadrants I + II
4 + 8 + 6 + 2
Quadrants I+II+III+IV
= 12 ÷ 20 = .60
40

© 2010 Cengage Learning
• Gives an estimate of utility by estimating the amount of money an organization would save if it used the test to select employees.
Savings =(n) (t) (r) (SDy) (m) - cost of testing
• n= Number of employees hired per year
• t= average tenure
• r= test validity
• SDy=standard deviation of performance in dollars
• m=mean standardized predictor score of selected applicants
41

© 2010 Cengage Learning
Selection ratio
The ratio between the number of openings to the number of applicants
Validity coefficient
Base rate of current performance
The percentage of employees currently on the job who are considered successful.
SD y
The difference in performance (measured in dollars) between a good and average worker (workers one standard deviation apart)
42

© 2010 Cengage Learning
m
• For example, we administer a test of mental ability to a group of 100 applicants and hire the 10 with the highest scores. The average score of the 10 hired applicants was 34.6, the average test score of the other 90 applicants was 28.4, and the standard deviation of all test scores was 8.3. The desired figure would be:
• (34.6 - 28.4) ÷ 8.3 = 6.2 ÷ 8.3 = ?
43

© 2010 Cengage Learning
m
• You administer a test of mental ability to a group of 150 applicants, and hire 35 with the highest scores. The average score of the 35 hired applicants was 35.7, the average test score of the other 115 applicants was 24.6, and the standard deviation of all test scores was 11.2. The desired figure would be:
– (35.7 - 24.6) ÷ 11.2 = ?
44

© 2010 Cengage Learning
.70
.60
.50
.40
.30
SR
1.00
.90
.80
.20
.10
.05
.50
.64
.80
.97
1.17
m
.00
.20
.35
1.40
1.76
2.08
45

© 2010 Cengage Learning
– Suppose:
• we hire 10 auditors per year
• the average person in this position stays 2 years
• the validity coefficient is .40
• the average annual salary for the position is $30,000
• we have 50 applicants for ten openings.
– Our utility would be:
(10 x 2 x .40 x $12,000 x 1.40) – (50 x 10) =
$133,900
46

© 2010 Cengage Learning
Exercise 6.2: Utility
47

© 2010 Cengage Learning
48

© 2010 Cengage Learning
1. Selection Ratio
Base rate
Validity
% of future successful employees
200 ÷ 400 = .50
800 ÷ 1000 = .80
.30
87%
49

© 2010 Cengage Learning Selection Ratio
80
%
.
r.
.05 .10 .20 .30 .40 .50 .60 .70 .80 .90 .95
00 .80
.80
.
80 .80
.
80 .
80 .80
.
80 .
80 .80
.
80
.10
.85
.85
.84
.83
.83
.82
.82
.81
.81
.81
.80
.87
.89
.91
.94
.82
.83
.85
.86
.90
.92
.94
.97
.83
.84
.86
.88
.92
.94
.96
.99
.84
.86
.88
.90
.94
.96
.98
1.0
.84
.87
.89
.91
.95
.97
.99
1.0
.85
.88
.90
.93
.96
.98
1.0
1.0
.86
.89
.92
.94
.98
.99
1.0
1.0
.87
.90
.93
.96
.99
1.0
1.0
1.0
.89
.92
.95
.97
.99
1.0
1.0
1.0
.90
.94
.96
.98
.60
.70
.80
.90
.20
.30
.40
.50
.84
.85
.87
.81
.82
.83
.84
.83
.83
.84
.84
.81
.81
.82
.82

© 2010 Cengage Learning
– Components:
• We will hire 200 people
• The average person in this position stays 4 years
• The validity coefficient is .25
• The average annual salary for the position is
$50,000
• We have 400 applicants for 200 openings.
– Our utility would be:
(200 x 4 x .25 x $20,000 x .80) – (400 x 8) =
$3,200,000 - $3,200 = $3,196,800
51

© 2010 Cengage Learning
– Components:
• We will hire 200 people
• The average person in this position stays 4 years
• The validity coefficient is .30
• The average annual salary for the position is
$50,000
• We have 400 applicants for 200 openings.
– Our utility would be:
(200 x 4 x .30 x $20,000 x .80) – (400 x 4) =
$3,840,000 - $1,600 = $3,838,400
52

© 2010 Cengage Learning
Test
New Test: Reilly Statistical Logic Test
Old Test: Robson Math
Savings
Utility
$3,838,400
$3,196,800
$641,600
53

© 2010 Cengage Learning
.70
.60
.50
.40
.30
SR
1.00
.90
.80
.20
.10
.05
.50
.64
.80
.97
1.17
m
.00
.20
.35
1.40
1.76
2.08
54

© 2010 Cengage Learning
Typical Corrected Validity Coefficients for
Selection Techniques
Method
Structured Interview
Cognitive ability
Job knowledge
Work samples
Assessment centers
Biodata
Integrity tests
Situational judgment
Validity
.57
.51
.48
.39
.38
.34
.34
.34
Method
College grades
References
Experience
Validity
.32
.29
.27
Conscientiousness
Unstructured interviews
Interest inventories
Handwriting analysis
.24
.20
.10
.02
Projective personality tests .00
55

© 2010 Cengage Learning
1.
Selection Ratio
Base rate
Validity
% of future successful employees
.50
.80
.57
.91 (round r down)
.94 (round r up)
56

© 2010 Cengage Learning Selection Ratio
80
%
.
r.
.05 .10 .20 .30 .40 .50 .60 .70 .80 .90 .95
00 .80
.80
.
80 .80
.
80 .
80 .80
.
80 .
80 .80
.
80
.10
.85
.85
.84
.83
.83
.82
.82
.81
.81
.81
.80
.87
.89
.91
.94
.82
.83
.85
.86
.90
.92
.94
.97
.83
.84
.86
.88
.92
.94
.96
.99
.84
.86
.88
.90
.94
.96
.98
1.0
.84
.87
.89
.91
.95
.97
.99
1.0
.85
.88
.90
.93
.96
.98
1.0
1.0
.86
.89
.92
.94
.98
.99
1.0
1.0
.87
.90
.93
.96
.99
1.0
1.0
1.0
.89
.92
.95
.97
.99
1.0
1.0
1.0
.90
.94
.96
.98
.60
.70
.80
.90
.20
.30
.40
.50
.84
.85
.87
.81
.82
.83
.84
.83
.83
.84
.84
.81
.81
.82
.82

© 2010 Cengage Learning
2. Answer: Unstructured Interview
– Components:
• We will hire 200 people
• The average person in this position stays 4 years
• The validity coefficient is .20
• The average annual salary for the position is
$50,000
• We have 400 applicants for 200 openings.
– Our utility would be:
(200 x 4 x .20 x $20,000 x .80) – (400 x 15) =
$2,560,000 - $6,000 = $2,554,000
58

© 2010 Cengage Learning
2. Answer: Unstructured Interview
– Components:
• We will hire 200 people
• The average person in this position stays 4 years
• The validity coefficient is .57
• The average annual salary for the position is
$50,000
• We have 400 applicants for 200 openings.
– Our utility would be:
(200 x 4 x .57 x $20,000 x .80) – (400 x 15) =
$7,296,000 - $6,000 = $7,290,000
59

© 2010 Cengage Learning
Test
New Test: Structured Interview
Old Test: Unstructured Interview
Savings
Utility
$7,290,000
$2,554,000
$4,736,000
60

© 2010 Cengage Learning
61

© 2010 Cengage Learning
• Test Bias
– Technical aspects of the test
– A test is biased if there are group differences in test scores (e.g., race, gender) that are unrelated to the construct being measured (e.g., integrity)
• Test Fairness
– Includes bias as well a political and social issues
– A test is fair if people of equal probability of success on a job have an equal chance of being hired
62

© 2010 Cengage Learning
Adverse Impact
Occurs when the selection rate for one group is less than 80% of the rate for the highest scoring group
Number of applicants
Number hired
Selection ratio
Male
50
20
.40
Female
30
10
.33
.33/.40 = .83 > .80 (no adverse impact)
63

© 2010 Cengage Learning
Number of applicants
Number hired
Selection ratio
Male
40
20
.50
Female
20
4
.20
.20/.50 = .40 < .80 (adverse impact)
64

© 2010 Cengage Learning
1. Compute Standard Deviation female applicants
x total applicants male applicants
x total hired total applicants
2.
Multiply standard deviation by 2
3.
Compute expected number of females to be hired
(female applicants/total applicants) x total hired
4.
Compute confidence interval (expected ± 2 SD)
5.
Determine if number of females hired falls within the confidence interval
65

© 2010 Cengage Learning
1. Compute Standard Deviation
10
50
x
40
50
x 20 = .20 x .80 x 20 = 3.2 = 1.79
2.
Multiply standard deviation by 2 = 1.79 * 2 = 3.58
3.
Compute expected number of females to be hired
(10/50) x 20 = .2 x 20 = 4
4.
Compute confidence interval (.42

4

7.58)
5.
Determine if number of females hired falls within the confidence interval
66

© 2010 Cengage Learning
• Single-Group Validity
– Test predicts for one group but not another
– Very rare
• Differential Validity
– Test predicts for both groups but better for one
– Also very rare
67

© 2010 Cengage Learning
68

© 2010 Cengage Learning
• Unadjusted Top-down Selection
• Passing Scores
• Banding
69

© 2010 Cengage Learning
Who will perform the best?
A “performance first” hiring formula
Applicant
Drew
Eric
Lenny
Omar
Mia
Morris
Sex
M
M
M
M
F
M
Test Score
99
98
91
90
88
87
70

© 2010 Cengage Learning
Advantages
• Higher quality of selected applicants
• Objective decision making
Disadvantages
• Less flexibility in decision making
• Adverse impact = less workforce diversity
• Ignores measurement error
• Assumes test score accounts for all the variance in performance (Zedeck, Cascio, Goldstein & Outtz, 1996).
71

© 2010 Cengage Learning
Who will perform at an acceptable level?
A passing score is a point in a distribution of scores that distinguishes acceptable from unacceptable performance
(Kane, 1994).
Uniform Guidelines (1978) Section 5H:
Passing scores should be reasonable and consistent with expectations of acceptable proficiency
72

© 2010 Cengage Learning
Applicant Sex Score
Omar
Eric
Mia
M
M
F
98
80
70 (passing score)
Morris
Tammy
Drew
M
F
M
69
58
40
73

© 2010 Cengage Learning
Advantages
• Increased flexibility in decision making
• Less adverse impact against protected groups
Disadvantages
• Lowered utility
• Can be difficult to set
74

© 2010 Cengage Learning
• Top-down (most inflexibility)
• Rules of “Three” or “Five”
• Traditional banding
• Expectancy bands
• SEM banding (standard error of measurement)
• Testing differences between scores for statistical significance.
• Pass/Fail bands (most flexibility)
75

© 2010 Cengage Learning
Applicant
Drew
Eric
Lenny
Omar
Mia
Morris
Sex
M
M
M
M
F
M
Test Score
99
98
91
90
88
87
76

© 2010 Cengage Learning
Applicant
Drew
Eric
Lenny
Omar
Jerry
Morris
Sex
M
M
M
M
F
M
Test Score
99
98
91
90
88
87
77

© 2010 Cengage Learning
• Based on expert judgment
• Administrative ease
• e.g. college grading system
• e.g. level of job qualifications
78

© 2010 Cengage Learning
Band
A
B
C
D
Test Score
522 – 574
483 – 521
419 – 482
0 – 418
Probability
85%
75%
66%
56%
79

© 2010 Cengage Learning
“Ranges of Indifference”
• A compromise between the top-down and passing scores approach.
• It takes into account that tests are not perfectly reliable (error).
80

© 2010 Cengage Learning
SEM Banding
• Compromise between top-down selection and passing scores
• Based on the concept of the standard error of measurement
• To compute you need the standard deviation and reliability of the test
Standard error = SD 1
 reliabilit y
• Band is established by multiplying 1.96 times the standard error
81

© 2010 Cengage Learning
Applicant
Armstrong
Glenn
Grissom
Aldren
Ride
Irwin
Carpenter
Gibson
McAuliffe
Carr
Teshkova
Jamison
Pogue
Resnick
Anders
Borman
Lovell
Slayton
Kubasov m m f m f m f m f m m m
Sex m m m m m f m
65
64
61
60
80
75
72
70
Score Band 1 Band 2 Band 3 Band 4
99 x hired hired hired
98
94 x x x x hired x hired hired x x 92
88
87
84 x x x x hired x x
58
57
55
53
82

© 2010 Cengage Learning
Applicant
Clancy
King
Koontz
Follot
Saunders
Crichton
Sanford
Dixon
Wolfe
Grisham
Clussler
Turow
Cornwell
Clark
Brown m m m m m m f f f m m m m
Sex m m
65
64
61
60
75
72
70
Score Band 1 Band 2 Band 3 Band 4 Band 5
97 x hired
95 x x hired
94
92
88
87
84
80 x x x x x x x x x x x hired x x x x hired x x x x
12 .
8 1

.
90
12 .
8 .
10
= 12.8 * .316
= 4.04
Band = 4.04 * 1.96 = 7.92 ~ 8
83

© 2010 Cengage Learning
Fixed
Sliding
Diversity-based
• Females and minorities are given preference when selecting from within a band .
84

© 2010 Cengage Learning
(just two bands)
Applicant Sex Score
Omar
Eric
Mia
M
M
F
98
80
70 (cutoff)
Morris M
Tammy F
Drew M
69
58
40
85

© 2010 Cengage Learning
• Helps reduce adverse impact, increase workforce diversity,and increase perceptions of fairness (Zedeck et al., 1996).
• Allows you to consider secondary criteria relevant to the job (Campion et al., 2001).
86

© 2010 Cengage Learning
(Campion et al., 2001)
• Lose valuable information
• Lower the quality of people selected
• Sliding bands may be difficult to apply in the private sector
• Banding without minority preference may not reduce adverse impact
87

© 2010 Cengage Learning
(Campion et. al, 2001)
• Narrow bands are preferred
• Consequences or errors in selection
• Criterion space covered by selection device
• Reliability of selection device
• Validity evidence
• Diversity issues
88

© 2010 Cengage Learning
(Campion et al., 2001).
Banding has generally been approved by the courts
• Bridgeport Guardians v. City of
Bridgeport, 1991
• Chicago Firefighters Union Local
No.2 v. City of Chicago, 1999
• Officers for Justice v. Civil Service
Commission, 1992
Minority Preference
89

© 2010 Cengage Learning
• The company should have established rules and procedures for making choices within a band
• Applicants should be informed about the use and logic behind banding in addition to company values and objectives (Campion et al., 2001).
90

© 2010 Cengage Learning
• Sample Test Information
– Reliability = .80
– Mean = 72.85
– Standard deviation = 9.1
• The Standard Error
SD 1
 reliabilit y
• Example 1
– We have four openings
– We would like to hire more females
9 .
1 1

.
80
• The Band
Band = Standard error * 1.96
Band = 4.07 * 1.96 = 7.98 ~ 8
9 .
1 .
20
• Example 2
– Reliability = .90
– Standard deviation = 12.8
=
9.1 * .447
= 4.07
91

© 2010 Cengage Learning
Using Banding to Reduce Adverse Impact
Exercise 6.4
92

© 2010 Cengage Learning
93

© 2010 Cengage Learning
1. Standard Error
2. Band
3. Hire using nonsliding band
McCoy
Robinette
4. Hire using sliding band
Carmichael
Ross
3.06
3.06 * 1.96 = 6 points
5. Hire using a passing score of 80
Carmichael
Ross
Crane
Carmichael
McCoy
Crane
McCoy
Crane
94

© 2010 Cengage Learning
Applicant
McCoy
Crane
Robinette
Schiff
Carmichael
Carver
Ross
Cutter
Kincaid
Cabot
Stone
Lewin
Shore
Branch
Sack f f m f m m f m m m f m m
Sex m m
85
83
80
78
87
86
86
89
89
88
Score Band 1 Band 2 Band 3 Band 4 Band 5
97 x x hire hired hired
95 x x x x hire
94
94
91 x x x x x hire x x x x hired hired x hired x x hire x hired
7 .
43 1

.
83
7 .
43 .
17
= 7.43 * .412
= 3.06
Band = 3.06 * 1.96 = 5.99 ~ 6
95

© 2010 Cengage Learning
Should the top scorers on a test always get the job?
96

© 2010 Cengage Learning
Applied Case Study:
Thomas A. Edison’s Employment Test
97

© 2010 Cengage Learning
Focus on Ethics
Diversity Efforts
98

© 2010 Cengage Learning
• To increase diversity, it is often legal to consider race or gender as a factor in selecting employees. Although legal, do you think it is ethical that race or gender be a factor in making an employment decision? How much of a role should it play?
• Is it ethical to hire a person with a lower test score because he or she seems to be a better personality fit for an organization?
• If an I/O psychologist is employed by a company that appears to be discriminating against Hispanics, is it ethical for her to stay with the company? What ethical obligations does she have?
99