PERSONNEL DECISIONS
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Personnel Psychology – subfield of I/O psychology
focusing on the management of human resources
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Recruitment 3
Selection
Placement
Psychometrics 3
Individual differences 3
Criterion development
Performance appraisal
PSYC 353 11C personnel decisions 11/01/11 [Arthur]
ƒ Training and
development
ƒ Team selection and
training
ƒ Legal issues - Equal
Emplo ment
Employment
Opportunity, Title
VII, CRA 1991
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Applicant/employee
A
li
t/
l
fl
flow th
through
h and
d iinteractions
t
ti
with various PERSONNEL systems
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• what to do with predictor or test scores now
that we have them?
• how do we decide who to select or hire?
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Overview of selection process
• Several stakeholders in selection
p
process
– I/O psychologists
– HR professionals
– applicants
– legal system (potentially)
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Overview of selection process
• I/O Psychologists
– determine KSAOs needed
• job analysis
– develop assessment tools
• validation studies
• recommend
d cutoff
t ff scores
– aware of . . .
• legal context
• applicant reactions
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Overview of selection process
• HR Professionals
– application of assessment tools
a personnel
p o
selection
o decisions
d
o
– make
• social context
– organizational image/culture
– nepotism
• may deviate from recommendations and best
practices
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Overview of selection process
• Applicants
– concerned with fairness and outcomes
• face validity
• procedural justice
• Legal system
– enforces Title VII, CRA, ADA/ADAA, ADEA
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Selection decisions
Job analysis
y
Development of
predictors
Validate [& set
cutoff]
Examine results
Assess utility
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Development of predictors
• Determine the KSAOs to be assessed
and method of assessment
• Conduct validation study
– content-related
content related validation study
• focus on extent to which test samples or
represents the domain of interest
– criterion-related validation study
• focus on empirical
p
relationship
p between
predictor and criterion
– requires incumbent predictor and criterion data
– construct-related
t t l t d validation
lid ti study
t d
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Multiple regression
• one predictor
– Y = a + bX
ƒ Y = predicted criterion score
ƒ a = intercept; a constant
ƒ b = slope; weight associated with the predictor score
based on the correlation with the criterion
– example
a = 50; b = .50
Y = 50 + .50 (X)
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Multiple regression
Y = job performance
X1 = programming skills
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Multiple regression
Y = job performance
X1 = cognitive ability
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X2 = agreeableness
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Multiple regression
• Y = a + b1X1 + b2X2 + . . . bnXn
• use multiple predictors in test battery to
account for more variance
• p
predictors that correlate with criterion but
NOT with each other
• incremental validityy
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Multiple regression
Example of Hypothetical Predictor/Criterion Correlation Matrix
Measure
Mean
SD
1.
2.
3.
4.
5.
6.
1. Verbal Ability
43.57
9.84
.85
2. Math Ability
22.36
4.98
.22*
.89
3. GMA
65.93
11.97
.91*
.60*
5.36
0.79
.31*
.33*
.39*
.91
37.20
6.88
-.02
.06
.01
.02
.77
8.27
1.20
.08
.02
.07
.17*
.09*
.87
17.48
3.16
.12*
.16*
.17*
.08
.22*
-.01
4 91
4.91
0 98
0.98
.10
10*
.14
14*
.14
14*
.25
25*
.21
21*
4. Conscientiousness
5. Extraversion
6. Interpersonal skills
7. Leadership
8 Performance
8.
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7.
8.
.91
.15
15*
.86
.32
32*
.88
88
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Cross-validation
• Because of changes due to time, people, situations,
and criteria, it is essential to make an independent
check on the validity of measures used for predictive
purposes.
ƒ empirical
p
– derivation sample
– cross-validation sample
ƒ statistical estimation
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Cutoff scores
• Type of cutoff score depends on . . .
– validation study type
• content- versus criterion-related validation
study
– frame-of-reference
• norm-referenced
norm referenced
– cutoff score is dependent on sample
• criterion-referenced
– cutoff score is independent of sample
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Cutoff scores
Classification of Major Cutoff Score Methods
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Cutoff scores
• norm-referenced
norm referenced
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mean
standard deviation
percentile
base-rate
• criterion-referenced
ƒ regression [ X = (Y – a) ÷ b ]
ƒ 70%
ƒ Angoff procedure
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Cutoff scores
• Base rate: proportion of
employees who are
successfullyy p
performing
g
the job
• Regression: X = (Y – a) ÷ b
• Angoff:
g
average
g item probability
p
y that a minimallyy competent
p
person would answer the item correctly
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Cutoff scores
• banding
ƒ ag
group
p of scores are considered equivalent
q
and not
differentiated between when making a selection decision
ƒ based on the premise that reliability of test scores is NOT
p
perfect
ƒ calculated from the standard error of measurement (which is
based on the reliability of the test scores)
ƒ can be fixed or sliding
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Cutoff scores
• once cutoff scores have been set, then
what?
ƒ rank and select top down
ƒ random
ƒ any others?
th ?
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Selection decision outcomes
Selection Decisions
Decisions, Outcomes
Outcomes, and Some
Factors That Influence Them
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Selection decisions
• classification outcomes
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A = true positives
B = true negatives
g
C = false negatives
D = false positives
• costs associated with classification
errors?
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Selection decisions
• classification decisions are influenced
y
by:
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selection ratio (# openings ÷ # applicants)
cutoff score (predictor)
base rate (criterion “cutoff”)
test validity
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Selection decisions – predictor cutoff
Selection Decisions
Decisions, Outcomes
Outcomes, and Some
Factors That Influence Them
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Selection decisions – predictor cutoff
P1
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Selection decisions – predictor cutoff
P2
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Selection decisions – base rate
C1
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Selection decisions – base rate
C2
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Selection decisions – test validity
Job performanc
ce
10
75
7.5
25
2.5
0
60
65
70
75
Cognitive ability
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Selection decisions – test validity
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Selection decisions – test validity
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Selection decisions – test validity
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Selection decisions – test validity
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Selection decisions
Strategies for Making Selection Decisions
• multiple regression approach
• multiple cutoff approach
• multiple
lti l hurdle
h dl approach
h
• characteristics?
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Utility Analysis
• value of personnel decisions to the
organization
• determined by:
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validity
lidit off the
th predictor
di t
cost to administer the predictor
# of people who do the job
# of people hired each year
typical tenure in the job
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Utility analysis
∆U = (NS × T × SDy × rxy × Zx) – (NA × C)
• ∆U = increase in average dollar-valued payoff resulting
from use of selection procedure over random
selection
• T = expected tenure of selected group
• rxy = correlation between selection procedure scores and
criterion scores (validity of test)
• SDy = standard deviation of dollar-valued job performance
• Zx = average
g standard p
predictor score of selected g
group
p
• NS = number of applicants selected
• NA = number of applicants tested
• C = costt off testing
t ti one applicant
li
t
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Utility analysis
• High Performance Work Systems
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selective staffing
comprehensive training
developmental performance appraisal
equitable reward systems
performance based pay
performance-based
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Legal issues
Legal Issues
1964 Civil Rights
g
Act (Title
(
VII)) and CRA 1991
• cannot make employment-related decisions on basis of
protected class status/variable
• protected classes ¨ race,
race sex
sex, religion
religion, color,
color and national
origin
• “employment-related decisions” covers all personnel
decisions such as selection
selection, training,
training promotion,
promotion retention,
retention
and performance appraisal
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Legal issues
• CRA 1991 explicitly prohibits test score adjustments
• Equal Employment Opportunity Commission (EEOC)
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Legal issues
Theories of discrimination
• disparate
p
treatment
• disparate impact (adverse impact)
ƒ 4/5th or 80% rule
ƒ significance tests
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Legal issues — disparate impact
• 80% rule
S
Sex
Male
Female
Apply
100
200
Hire
20
180
20%
90%
Selection rate
• adverse
d
iimpactt = 20 ÷ 90 = 22%
• < 80% so have adverse impact
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Legal issues — disparate impact
Legal defenses to adverse impact
• jjob-relatedness ((i.e.,, validity)
y)
• bona fide occupational qualification (BFOQ)
• business necessity
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Legal issues
Major Court Cases
• Griggs v.
v Duke Power Company (1971)
– burden of proof on defendant not plaintiff
• Albemarle Paper Company v. Moody (1975)
– job analysis
• Bakke v. University of California (1978)
– “reverse” discrimination
• Watson vv. Fort Worth Bank & Trust (1988)
– what is a “test” ¨ interviews
– cost of alternative predictors can be a consideration
• Wards Cove Packing Company vv. Antonio (1989)
– modified burden of proof guidelines; served as impetus for
CRA 1991
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Legal issues
1967 Age Discrimination in Employment
Act (ADEA)
• employees 40 yrs of age and older
• prevent older employees from being singled
out during cutbacks
• 1990 Older Workers Benefit Protection Act
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Legal issues
1990 Americans
A
i
with
ith Disabilities
Di biliti Act
A t (ADA;
(ADA
ADAAA)
• 43 million Americans have a disability
• disability ¨ physical or mental impairment
that limits one or more “major life activity”
• prohibits discriminating against “qualified
individuals” (can perform “essential”
functions)
• provide reasonable accommodations
• employers with 15 or more employees
• up to $300,000 in compensatory & punitive
damages
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Legal issues
Affirmative Action
• NOT a p
psychometric
y
issue
• social policy aimed at reducing the effects of prior
discrimination
• although EEOC guidelines stipulate that only govt
employers and govt contractors must have written
affirmative action policies, many private sector
employers do so as well
ƒ believe it makes good business sense
• 3 Goals
ƒ correct past inequities
ƒ compensate past inequities [court ordered and supervised]
ƒ promote diversity
• NOT quotas
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Legal issues
Types or levels of affirmative action
•
•
•
•
•
recruitment
removing discriminatory obstacles
soft preferential treatment
h d preferential
hard
f
i l treatment (illegal)
(ill
l)
diversity [targeted recruitment]
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