1. 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)

2. Test Validation Process
Define Objectives
State Inferences
Decide on Methods to Test Inferences
Collect Evidence

3. 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

4. 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)

5. 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 A B C D E F G H I J K L M N O P Q R
Job Tasks 1 2 3 4 5 6 7 8 9 10 11 12 13

6. ITEM RATING FORM
Job classification under review ____________________________________________
item #
KSA B C 1 41 2 42 3 43 4 44 5 45 6 46 7 47 8 48 9 49 10 50 11 51 12 52

7. 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).

8. 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

9. Criterion-related Validity Issues
Job Stability
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).
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)

10. 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)

11. 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)
y = standard deviation of y
(criterion)
r2xy = correlation between x
and y squared
Standard error of estimate
(validity coefficient):
y’ = y r2xy

12. Correction for Attenuation
Observed validity coefficient
T = 
 
x y xy yy
Criterion reliability
Validity coefficient
 of unrestricted sample S 1  S 2
 of restricted sample S 1
 = 1 2
 =
(1 -  )   S 2 2 S 1 1
1 -  +  2 S 1
Range of Restriction
(Predictor)
Range Restriction
(Criterion)

13. Test Utility Key Points
# Job openings
Selection Ratio (SR) = 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]

14. and Given Selection Ratio, for Base Rate .60.
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
Note: A full set of tables can be found I Taylor and Russell (1939) and in McCormick and Ilgen (1980, Appendix B).

15. 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

16. Ns rxy SDyZx – NT (C) Example of Brogden and Cronbach & Gleser Models
cost of assessing each applicant
number of applicants assessed
validity coefficient
# of applicants selected
average score on the selection procedure of those selected (standard score)
standard deviation of job performance in dollar terms

17. Construct Validity
Multitrait-Multimethod Matrix (Campbell & Fiske, 1959)

19. [extent to which a test assesses the construct it intends to measure]
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)

20. Equal validity, unequal criterion means
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.

21. Equal validity, unequal predictor means
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

22. Equal predictor means, but validity only for non minority groups
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

23. 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

24. Statistical Power and Hypothesis Testing
Reality
Significance exists
Type II error (“false positive”)
Correct decision
(reject null)
Correct decision
(accept null)
No significance exists
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)