Transcript Test Validity “… the development of a valid test requires multiple procedures, which are employed at different stages of test construction …

Test Validity
“… the development of a valid test requires multiple procedures,
which are employed at different stages of test construction … The
validation process begins with the formulation of detailed trait or
construct definitions … Test items are then prepared to fit the
construct definitions. Empirical item analyses follow … Other
appropriate internal analyses may then be carried out … The final
stage includes validation and cross-validation of various scores and
interpretive combinations of scores through statistical analyses
against external, real-life criteria.” (Anastasi, 1986, p.3)
Almost any information gathered in the process of developing or
using a test is relevant to its validity … If we think of test validity in
terms of understanding what a particular test measures, it should be
apparent that virtually any empirical data obtained with the test
represent a potential source of validity information.” (Anastasi, 1986,
p.3)
Test Validation Process
Define Objectives
State Inferences
Decide on
Methods to Test
Inferences
Collect Evidence
Types of Validity
Content Validity
[the extent to which test items represent a domain]
a) Subject Matter Expert Opinions (e.g., CVR statistic)
b) Internal consistency reliability
c) Correlation with other similar tests
Content relevance
Domain specification
Content coverage
Domain representativeness
Steps in a Content Validation Effort
1) Perform a job analysis
•
Description of job tasks
•
Rating of job tasks on various criteria
•
Specification of KSAs
•
Rating of KSAs on various criteria
•
Link/connect tasks to KSAs
From SIOP Principles: “The characterization of the work domain should be based on accurate
and thorough information about the work including analysis of work behaviors and
activities, responsibilities of the job incumbents, and/or the KSAOs prerequisite to effective
to effective performance on the job. The researcher should indicate what important work
behaviors , activities, and worker KSAOs are included in the domain, describe how the
content of the domain is linked to the selection procedure, and explain why
certain parts of the domain were or were not included in the selection procedure.” (p. 22)
2) Selection of SMEs
From SIOP Principles: “ The success of the content-based study is closely related to the
qualifications of the subject matter experts (SMEs) … The experts should have thorough
knowledge of the work behaviors and activities, responsibilities of job incumbents, and the
KSAOs prerequisite to effective to effective performance on the job” (p. 22)
3) Writing (or selecting) and evaluation of selection measure content
(test items)
TASK -- KSA MATRIX
To what extent is each KSA needed when performing each job task?
5 = Extremely necessary, the job task cannot be performed without the KSA
4 = Very necessary, the KSA is very helpful when performing the job task
3 = Moderately necessary, the KSA is moderately helpful when performing the job task
2 = Slightly necessary, the KSA is slightly helpful when performing the job task
1 = Not necessary, the KSA is not used when performing the job task
KSA
Job
Tasks
1
2
3
4
5
6
7
8
9
10
11
12
13
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
item #
KSA
B
KSA
B
C
item #
1
41
2
42
3
43
4
44
5
45
6
46
7
47
8
48
9
49
10
50
11
51
12
52
KSA
B
KSA
B
C
Content Validity Issues
• Are the job activities and requirements stable across time?
• Does successful performance on the test require the same
KSAs as successful performance on the job?
• Is the type (or mode) of testing procedure the same as
that required on the job?
• Do some KSAs not required on the job exist on the test?
• Not useful when abstract constructs are being measured (a
small inferential leap is required between the test content
and job requirements)
From Anastasi (1986): “When tests are designed for use within special contexts, the relevant constructs are
usually derived from content analysis of particular behavior domains” (p. 7).
From SIOP Principles: “ When selection procedure content is linked to job content, content-oriented strategies
are useful. When selection procedure content is less clearly linked to job content, other sources of validity
evidence take precedence” (p. 23).
Types of Validity (cont.)
Criterion-related Validity
Predictive
[Correlation between test
scores of applicants and their
performance scores when
some time interval has passed
after they are hired]
• Range restriction issue on
performance scores
• Time, cost, & pragmatic
concerns
Concurrent
[Correlation between test scores
and performance scores of
current employees]
• Motivation level
• Guessing, Faking
• Job experience factor
• Range restriction issue on
performance scores
Criterion-related Validity Issues
A) Job Stability
B) Reliable and relevant measure of job performance
From SIOP Principles: “A relevant, reliable, and uncontaminated criterion(s) must be
obtained or developed. Of these characteristics, the most important is
relevance. A relevant criterion is one that reflects the relative standing of
employees with respect to important work behavior(s) or outcome measure(s).
If such a criterion measure does not exist or cannot be developed, use of a
criterion-related validation strategy is not feasible (p. 14).
C) Use of a representative sample of people and jobs
D) Large sample (on predictor and criterion)
From SIOP Principles: “A competent criterion-related validity study should be
based on a sample that is reasonably representative of the work and
candidate pool … A number of factors related to statistical power can influence
the feasibility of a criterion-related study. Among these factors are the degree
(and type) of range restriction in the predictor or the criterion, reliability of the
criterion, and statistical power (p. 14)
Legal Issues and Criterion-related Validity
• Court focus on the content of measures as opposed to
criterion validity evidence (relationship between test cores
and job performance)
• Emphasis on the legal history of tests
• Criterion-validity emphasis versus concurrent validity
designs
• Statistical significant relationships are not always
acceptable (consideration of other factors such as test utility)
Factors Affecting the Validity Coefficient
[correlation between a test and job performance]
• Reliability of both the criterion (job performance) and the predictor (test)
• Restriction of range (on both the test and job performance measure)
• Contamination of the criterion (e.g., measure of job performance is
affected by other variables rather than one’s ability or knowledge)
Standard error of estimate
(validity coefficient):
y’ = y
y = standard deviation of y
2
1 - r xy
(criterion)
2
r xy = correlation between x
and y squared
Correction for Attenuation
T
x y=
xy
0
Observed validity coefficient
 yy
Criterion reliability
Validity coefficient

 =
 of unrestricted sample
S1
S1
2
1- +
 of restricted sample
2
S 12
2
2
 =
1-
S1
2
2
(1 -  )
S1
S1
Range of Restriction
(Predictor)
Range Restriction
(Criterion)
Test Utility Key Points
Selection Ratio
(SR) =
# Job openings
n
N
# Applicants
Test Validity [Criterion-related]: The extent to which test
scores correlate with job performance scores [Range is from 0
to 1.0]
Proportion of “Successes” Expected Through the Use of Test of Given Validity
and Given Selection Ratio, for Base Rate .60.
(From Taylor & Russell, 1939, p. 576)
Selection Ratio
Validity
.05
.10
.20
.30
.40
.50
.60
.70
.80
.90
.95
.00
.05
.10
.15
.20
.60
.64
.68
.71
.75
.60
.63
.67
.70
.73
.60
.63
.65
.68
.71
.60
.62
.64
.67
.69
.60
.62
.64
.66
.67
.60
.62
.63
.65
.66
.60
.61
.63
.64
.65
.60
.61
.62
.63
.64
.60
.61
.61
.62
.63
.60
.60
.61
.61
.62
.60
.60
.60
.60
.61
.25
.30
.35
.40
.45
.78
.82
.85
.88
.90
.76
.79
.82
.85
.87
.73
.76
.78
.81
.83
.71
.73
.75
.78
.80
.69
.71
.73
.75
.77
.68
.69
.71
.73
.74
.66
.68
.69
.70
.72
.65
.66
.67
.68
.69
.63
.64
.65
.66
.66
.62
.62
.63
.63
.64
.61
.61
.62
.62
.62
.50
.55
.60
.65
.70
.93
.95
.96
.98
.99
.90
.92
.94
.96
.97
.86
.88
.90
.92
.94
.82
.84
.87
.89
.91
.79
.81
.83
.85
.87
.76
.78
.80
.82
.84
.73
.75
.76
.78
.80
.70
.71
.73
.74
.75
.67
.68
.69
.70
.71
.64
.64
.65
.65
.66
.62
.62
.63
.63
.63
.75
.80
.85
.90
.95
1.00
.99
1.00
1.00
1.00
1.00
1.00
.99
.99
1.00
1.00
1.00
1.00
.96
.98
.99
1.00
1.00
1.00
.93
.95
.97
.99
1.00
1.00
.90
.92
.95
.97
.99
1.00
.86
.88
.91
.94
.97
1.00
.81
.83
.86
.88
.92
1.00
.77
.78
.80
.82
.84
.86
.71
.72
.73
.74
.75
.75
.66
.66
.66
.67
.67
.67
.63
.63
.63
.63
.63
.63
Note: A full set of tables can be found I Taylor and Russell (1939) and in McCormick and Ilgen (1980, Appendix B).
Selection Ratio Example (cont.)
Mean Standard Criterion Score of Accepted Cases in Relation to Test Validity and Selection Ratio
(From Brown & Ghiselli, 1953, p. 342)
Validity Coefficient
Selection
Ratio
.00 .05 .10 .15 .20 .25 .30 .35 .40 .45 .50 .55 .60 .65 .70 .75 .80 .85 .90 .95 1.00
.05
.10
.15
.20
.25
.30
.35
.40
.45
.50
.50
.60
.65
.70
.75
.80
.85
.90
.95
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.00
.10
.09
.08
.07
.06
.06
.05
.05
.04
.04
.04
.03
.03
.02
.02
.02
.01
.01
.01
.21
.18
.15
.14
.13
.12
.11
.10
.09
.08
.07
.06
.06
.05
.04
.04
.03
.02
.01
.31
.26
.23
.21
.19
.17
.16
.15
.13
.12
.11
.10
.09
.07
.06
.05
.04
.03
.02
.42
.35
.31
.28
.25
.23
.21
.19
.18
.16
.14
.13
.11
.10
.08
.07
.05
.04
.02
.52
.44
.39
.35
.32
.29
.26
.24
.22
.20
.18
.16
.14
.12
.11
.09
.07
.05
.03
.62
.53
.46
.42
.38
.35
.32
.29
.26
.24
.22
.19
.17
.15
.13
.11
.08
.06
.03
.73
.62
.54
.49
.44
.40
.37
.34
.31
.28
.25
.23
.20
.17
.15
.12
.10
.07
.04
.83
.70
.62
.56
.51
.46
.42
.39
.35
.32
.29
.26
.23
.20
.17
.14
.11
.08
.04
.94
.79
.70
.63
.57
.52
.48
.44
.40
.36
.32
.29
.26
.22
.19
.16
.12
.09
.05
1.04
.88
.77
.70
.63
.58
.53
.48
.44
.40
.36
.32
.28
.25
.21
.18
.14
.10
.05
1.14
.97
.85
.77
.70
.64
.58
.53
.48
.44
.40
.35
.31
.27
.23
.19
.15
.11
.06
1.25
1.05
.93
.84
.76
.69
.63
.58
.53
.48
.43
.39
.34
.30
.25
.21
.16
.12
.07
1.35
1.14
1.01
.91
.82
.75
.69
.63
.57
.52
.47
.42
.37
.32
.27
.22
.18
.13
.07
1.46
1.23
1.08
.98
.89
.81
.74
.68
.62
.56
.50
.45
.40
.35
.30
.25
.19
.14
.08
1.56
1.32
1.16
1.05
.95
.87
.79
.73
.66
.60
.54
.48
.43
.37
.32
.26
.20
.15
.08
1.66
1.41
1.24
1.12
1.01
.92
.84
.77
.70
.64
.58
.52
.46
.40
.33
.28
.22
.16
.09
1.77
1.49
1.32
1.19
1.08
.98
.90
.82
.75
.68
.61
.55
.48
.42
.36
.30
.23
.17
.09
1.87
1.58
1.39
1.26
1.14
1.04
.95
.87
.79
.72
.65
.58
.51
.45
.38
.32
.25
.18
.10
1.98
1.67
1.47
1.33
1.20
1.10
1.00
.92
.84
.76
.68
.61
.54
.47
.40
.33
.26
.19
.10
2.08
1.76
1.55
1.40
1.27
1.16
1.06
.97
.88
.80
.72
.64
.57
.50
.42
.35
.27
.20
.11
Example of Brogden and Cronbach & Gleser Models
Ns rxy SDyZx – NT (C)
validity
# of applicants coefficient
selected
cost of assessing
each applicant
number of applicants
assessed
average score on the
selection procedure of those
selected (standard score)
standard deviation of
job performance in
dollar terms
Construct Validity
Multitrait-Multimethod Matrix (Campbell & Fiske, 1959)
Types of Validity (cont.)
Construct Validity
[extent to which a test assesses the construct it intends to
measure]
• Correlation between scores measuring a construct (e.g., anxiety) with one
method (e.g., paper & pencil) with scores on the same construct using a
different method (e.g., interview) [Convergent validation]
• Correlation between scores measuring a construct (e.g., anxiety) using one
method (e.g., paper & pencil) with scores on a different construct (e.g.,
leadership) assessed with a different method (e.g., interview) [Discriminant
validation]
“Construct validation is indeed a never-ending process. However, that should
not preclude using the test operationally to help solve practical problems and
reach real-life decisions as soon as the available validity information has
reached an acceptable level for a particular application. This level varies with
the type of test and the way it will be used. Establishing this level requires
informed professional judgment within the appropriate specialty of professional
practice.” (Anastasi, p.4)
Satisfactory
Non minority
Performance
Criterion
Minority
Unsatisfactory
Reject
Accept
Predictor Score
Equal validity, unequal criterion means
- Equal test scores; Minorities performing less well on job (over predicting performance)
- Minorities hired same as non minorities but probability of success is small. Can
reinforce existing stereotypes.
Intercept Bias (Test)
Satisfactory
Minority
Performance
Criterion
Non minority
Unsatisfactory
Reject
Accept
Predictor Score
Equal validity, unequal predictor means
- Job performance is equal
- Test scores are greater for non-minorities
Satisfactory
Minority
Non minority
Performance
Criterion
Unsatisfactory
Accept
Reject
Predictor score
Equal predictor means, but validity only for non minority
groups
•
•
•
-
Equal test scores and criterion scores
No validity for minorities (only should be used for non minorities)
No adverse impact same numbers hired in each group
However, more non-minorities will succeed on jobs; can reinforced stereotypes
Situational specificity or
Generalizibility of test validity across samples?
Fluctuations in validity coefficients may often be due to:
• Small sample sizes (e.g., many have samples of 50 or less employees)
• Unreliable criterion measures
• Restriction of range in employee samples
Some evidence that certain tests (e.g., aptitude tests) may can be generalized
across a variety of occupations
Statistical Power and Hypothesis Testing
Reality
Significance
exists
No
significance
exists
Type II error
(“false positive”)
Correct decision
(reject null)
Correct decision
(accept null)
Type I error
(“false positive”)
No significant effect found
Significant effect found
Findings of study
“Every experiment may be said to exist only in order to give the facts a
chance of disproving the null hypothesis.” (Fisher, 1935, p.19)