Adverse Impact
Some Stuff You Should Know
Human Resources
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What Exactly is Adverse Impact?
A substantially different rate of selection in hiring,
promotion or other employment decision which works
to the disadvantage of members of a race, sex, or
ethnic group (Uniform Guidelines Q&A #10).
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Where Does it Exist?
 Written tests typically have the highest degree of
adverse impact.
 Highest level of impact tends to be against African
Americans and Hispanics (Sackett, 2001).
 Physical ability tests typically have adverse impact
against women, especially when they measure upper
body strength.
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Why Does it Exist?
(Guion, 1998)
1.
2.
3.
4.
5.
Chance
Measurement problems inherent to the test (e.g.,
poor reliability)
The nature of test use (e.g., ranking vs. pass/fail)
Differences in distribution sizes (e.g., 100 males
and only 10 females)
True population differences in distributions of the
trait being measured
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What Does it Describe?
 Adverse impact simply describes differences
between groups on a testing process.
 It is not a legal term that implies guilt or a
psychometric term that implies unfairness or test
bias.
 Many employment tests result in adverse impact.
 Adverse impact is not normally due to forms of bias
inherent to the test (Sackett, 2001).
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Two Types of
Adverse Impact Analyses
 Selection Rate Comparison and Availability
Comparison.
 Selection Rate Comparison – evaluates the
selection rates between two groups on a selection
procedure (or other employment decision such as
layoffs).
 Involves two groups: a focal group (e.g., females or
minorities) and a reference group (e.g., males or
Caucasian).
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Selection Rate Comparison
Four variables are entered into this type of adverse
impact analysis:
Number of focal group members selected
2. Number who were not selected
3. Number of reference group members selected
4. Number who were not selected
1.
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Selection Rate Comparison Example
Took Test
Passed Test
%
Males
75
60
.80
Females
50
30
.60
A passing rate for females which is less than 64% (or 80% of the
passing rate for males) is evidence of adverse impact.
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Availability Comparison
 Availability Comparison – evaluates one group’s
representation in a position to their availability for that
position (e.g., 13% of the incumbents in the
Accountant classification are Hispanic; 15% of the
qualified Accountant Applicants are Hispanic.
 Useful for showing the extent to which one group
may be underutilized (e.g., 2% in the above
example).
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Availability Comparison
Three variables are entered into this type of
Adverse Impact analysis:
Total number of incumbents in a position
2. Number of focal group members in the position
3. Percentage of focal group members who are
available for the position
1.
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Two Types of
Adverse Impact Analyses
 The Selection Rate Comparison is the only type of
adverse impact analysis that can be used alone to
demonstrate adverse impact.
 The Availability Comparison only shows a prima facie
reason to investigate further into an employer’s
practices to see why a “gap” may exist.
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Connecticut v. Teal (1982)
 The employer must determine whether adverse
impact exists at each step of a multiple-step selection
process.
 Evidence of adverse impact at any step of the
selection process constitutes adverse impact.
 Employers cannot hide adverse impact within a
selection process by demonstrating none at the
bottom line.
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Adverse Impact Example
Males
Females
Took
Test
Passed
Test
%
Took
Test
Passed
Test
%
Written Test
200
130
.65
125
60
.48*
Performance Test
130
95
.73
60
32
.53*
Oral Board
95
76
.80
32
28
.88
Eligibility List
76
28
* Denotes adverse impact
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80% or 4/5th Rule of Thumb
. . . A “rule of thumb” as a practical means for
determining adverse impact for use in enforcement
proceedings . . . It is not a legal definition of
discrimination, rather it is a practical device to keep
the attention of enforcement agencies on serious
discrepancies in hire or promotion rates or other
employment decisions (Overview, Section ii).
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80% or 4/5th Rule of Thumb
. . . A selection rate for any race, sex, or ethnic group
which is less than four-fifths (4/5) (or eighty percent)
of the rate for the group with the highest rate will
generally be regarded by the Federal enforcement
agencies as evidence of adverse impact, while a
greater than four-fifths rate will generally not be
regarded by Federal enforcement agencies as
evidence of adverse impact. Small differences in
selection rate may nevertheless constitute adverse
impact, where they are significant in both statistical
and practical terms . . . (Section 4D).
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80% Rule vs. Statistical Significance
 Courts have scrutinized the 80% rule in Title VII
litigation because the rule is greatly impacted by
small numbers and does not consider the statistical
significance of the passing rate disparity between the
two groups.
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80% Rule vs. Statistical Significance
For example:
“Rather than using the 80 percent rule as a touchstone,
we look more generally to whether the statistical
disparity is ‘substantial’ or ‘significant’ in a given case”
(Bouman v. Block, 1991).
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Statistical Significance
 If the result of a statistical test is “statistically
significant,” it is unlikely to have occurred by chance.
 Obtaining a finding of statistical significance in a
research study signifies a point at which the
researcher is capable of stating that a legitimate
trend, and not a chance relationship, actually exists
(with a reasonable level of certainty).
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Statistical Significance
 Statistical significance tests result in a p-value (p for
probability).
 These p-values can range from 0 to +1.0.
 A p-value of 0.01 means that the odds of the event
occurring by chance are only 1% or 1 out of 100.
 A p-value of 0.05 means that the odds of the event
occurring by chance are only 5% or 5 out of 100.
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Statistical Power
 The use of statistical tests to determine whether
statistical significance exists in a data-set is highly
contingent on whether the analysis has sufficiently
high levels of statistical power to find it.
 Statistical Power – the ability to reveal a statistically
significant finding if there is one to be found.
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Statistical Power
 A powerful adverse impact analysis is one that has a
high likelihood to uncovering adverse impact if it
really exists.
 When applying the statistical power concept to
adverse impact analysis, there are 3 factors that
impact the statistical power of the analysis.
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3 Factors That Impact
Statistical Power
1.
Effect size. For selection rate comparisons, this
pertains to the size of the “gap” between the
selection rates of the 2 groups (e.g., male passing
rate of 80% vs. female passing rate of 60%).
2.
Sample size. The number of focal and reference
group members (plays a key role in the analysis).
3.
Type of statistical test used. Some statistical
tests are more powerful than others. Another issue
is whether a one-tail or two-tail test of significance is
used.
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Sample Size
 A large sample size is the most effective way to
increase the power of an adverse impact analysis.
 There is no absolute bottom threshold regarding the
minimum sample size needed for statistical
investigations.
 In Bradley v. Pizzaco of Nebraska, Inc. (1991), the
court said: “There is no minimum sample size
prescribed either in federal law or statistical theory.”
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Sample Size
 OFCCP (1993) indicated that the minimum number
for adverse impact analysis is 30 with at least 5
expected for selection (i.e., hired, promoted).
 The Uniform Guidelines Q & A #20 (1979) states that
a sample size of 20 is too small for adverse impact
analysis.
 What have the courts said?
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Sample Size
 A sample size of 6 was found to be too small in Gault v.
Zellerbach (1998).
 A sample size of 7 or 11 was insufficient to demonstrate a
pretext in Martin v. United States Playing Card Co. (1998).
 A sample size of 13 was too small in Tinker v. Sears, Roebuck,
& Co. (1997).
 A sample of 8 was insufficient in Anderson v. Premier Industrial
Corp. (1995).
 An 8-person sample was too small to support a discrimination
case in Osborne v. Brandeis Machinery & Supply Corp. (1994).
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Sample Size
Uniform Guidelines:
Where the user’s evidence concerning the impact of a
selection procedure indicates adverse impact but is
based upon numbers which are too small to be
reliable, evidence concerning the impact of the
procedure over a longer period of time and/or
evidence concerning the impact which the selection
procedure had when used in the same manner in
similar circumstances elsewhere may be considered
in determining adverse impact (Section 4D).
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Statistical Tests
 There are 2 categories of statistical significance tests
that can be used – exact and estimated.
 Exact tests are the most powerful statistical tests to
use for adverse impact calculations.
 Exact tests provide the precise probability value of
the analysis.
 The exact statistical significance test to use for
selection rate comparisons is the two-tail Fisher
Exact Probability Test for 2 X 2 contingency tables.
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Fisher Exact Test
Males
Females
Pass
60
35
Fail
15
20
The 2 X 2 contingency table will always have two groups and two
categories.
The two-tail Fisher Exact Test is the statistical procedure most
frequently used in litigation for establishing statistically significant levels
of adverse impact.
Any probability value that is less than .05 is statistically significant and
indicates a difference in passing rates between two groups that is not
likely to be occurring by chance.
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One-Tail vs. Two-Tail Test
 A one-tail statistical test used for adverse impact analysis
investigates the possibility of discrimination occurring in just one
direction (e.g., against women when making a men vs. women
comparison).
 A two-tail test makes the assumption that discrimination could
have occurred in either direction.
 The courts have been almost totally consistent in their
requirement of using a two-tail test for significance because it
mirrors the philosophy of Title VII, which is to evaluate a
disparity against any group, not just one group.
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One-Tail vs. Two-Tail Test
Some court decisions endorsing the two-tail method:
 Chang v. University of Rhode Island (1985)
 Palmer v. Shultz (1987)
 Mozee v. American Commercial Marine Service Co. (1991)
 Csicseri v. Bowsher (1994)
 Hoops v. Elk Run Coal Company Inc. (2000)
 Moore v. Summers (2000)
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Practical Significance
Rule of One:
 The Rule of One is often used to determine the
practical significance of any detected adverse impact.
 The Rule of One states that if adverse impact is
negated by having one more candidate pass the
exam component (or be selected), the adverse
impact has no practical significance.
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So, What Does This Suggest?
 For small sample sizes, use the 80% or 4/5th rule of
thumb.
 Apply the “Rule of One” to determine practical
significance.
 For larger sample sizes (≥ 30) you can use the two-
tail Fisher Exact probability Test.
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