Validity refers to
the relationship between performance on an assessment and performance on the
job. Validity is the most important issue to consider when deciding whether to
use a particular assessment tool because an assessment that does not provide
useful information about how an individual will perform on the job is of no
value to the organization.
There are different types of validity evidence.
Which type is most appropriate will depend on how the assessment method is used in making an employment decision. For example, if a
work sample test is designed to mimic the actual tasks performed on the job,
then a content validity approach may be needed to
establish the content of the test matches in a convincing way the content
of the job, as identified by a job analysis.
If a personality test is intended to forecast the job success of applicant's for
a customer service position, then evidence of predictive
validity may be needed to show scores on the personality test are
related to subsequent performance on the job.
The most commonly used measure of
predictive validity is a correlation (or validity) coefficient. Correlation
coefficients range in absolute value from 0 to 1.00. A correlation of 1.00 (or
-1.00) indicates two measures (e.g., test scores and job performance
ratings) are perfectly related. In such a case, you could perfectly predict
the actual job performance of each applicant based on a single assessment
score. A correlation of 0 indicates two measures are unrelated. In
practice, validity coefficients for a single assessment rarely exceed .50. A
validity coefficient of .30 or higher is generally considered useful for most circumstances
When multiple selection tools are used, you can consider the
combined validity of the tools. To the extent the assessment tools
measure different job-related factors (e.g., reasoning ability and honesty)
each tool will provide unique information about the applicant's ability to
perform the job. Used together, the tools can more accurately predict the
applicant's job performance than either tool used alone. The amount of
predictive validity one tool adds relative to another is often referred to
as the incremental validity of the tool. The
incremental validity of an assessment is important to know because even if an
assessment has low validity by itself, it has the potential to add
significantly to the prediction of job performance when joined with another
Just as assessment tools differ with respect to reliability,
they also differ with respect to validity. The following table provides the estimated
validities of various assessment methods for predicting job performance
(represented by the validity coefficient), as well as the incremental validity
gained from combining each with a test of general
cognitive ability. Cognitive ability tests are used as the baseline
because they are among the least expensive measures to administer and the most
valid for the greatest variety of jobs. The second column is the correlation
of the combined tools with job performance, or how well they collectively
relate to job performance. The last column shows the percent increase in validity from combining the tool with a measure of general
cognitive ability. For example, cognitive ability tests have an estimated
validity of .51 and work sample tests have an estimated validity of .54. When
combined, the two methods have an estimated validity of .63, an increase of 24%
above and beyond what a cognitive ability test used alone could provide.
Table 1: Validity of Various Assessment Tools Alone and in Combination
||Validity of method used alone
| % increase in validity from combining tool with cognitive ability
|Tests of general cognitive ability
|Work sample tests
|Job knowledge tests
| Accomplishment record*
|Years of job experience
|Training & experience point method
|Years of education
Note: Table adapted from Schmidt & Hunter (1998).
Copyright © 1998 by the American Psychological Association. Adapted with permission.
*Referred to as the training & experience behavioral consistency method in Schmidt & Hunter (1998).