ADA Perceived Disability Claims: A Decision-Tree Analysis

William R. Draper • Carolyn E. Hawley •

Brian T. McMahon • Christine A. Reid •

Lara A. Barbir

� Springer Science+Business Media New York 2013

Abstract Introduction The purpose of this study is to

examine the possible interactions of predictor variables

pertaining to perceived disability claims contained in a

large governmental database. Specifically, it is a retro-

spective analysis of US Equal Employment Opportunity

Commission (EEOC) data for the entire population of

workplace discrimination claims based on the ‘‘regarded as

disabled’’ prong of the Americans with Disabilities Act

(ADA) definition of disability. Methods The study utilized

records extracted from a ‘‘master database’’ of over two

million charges of workplace discrimination in the Inte-

grated Mission System of the EEOC. This database

includes all ADA-related discrimination allegations filed

from July 26, 1992 through December 31, 2008. Chi

squared automatic interaction detection (CHAID) was

employed to analyze interaction effects of relevant vari-

ables, such as issue (grievance) and industry type. The

research question addressed by CHAID is: What combi-

nation of factors are associated with merit outcomes for

people making ADA EEOC allegations who are ‘‘regarded

as’’ having disabilities? Results The CHAID analysis

shows how merit outcome is predicted by the interaction of

relevant variables. Issue was found to be the most promi-

nent variable in determining merit outcome, followed by

industry type, but the picture is made more complex by

qualifications regarding age and race data. Although dis-

charge was the most frequent grievance among charging

parties in the perceived disability group, its merit outcome

was significantly less than that for the leading factor of

hiring.

Keywords Americans with Disabilities Act �Discrimination in Employment Act � Disabled

persons � Employment discrimination � Disability

discrimination

Purpose

The Americans with Disabilities Act of 1990 (ADA) con-

tains a three-pronged definition of ‘‘disability:’’ that which

is current and documented, that which is historical (‘‘record

of disability’’), and that which is perceived (‘‘regarded as

disabled’’). In the context of workplace discrimination

(Title I of the ADA), the third prong refers to the per-

spective of employers, especially exaggerated views of an

impairment, which elevates it to disability status. Such

misperceptions can lead to unfair and unlawful personnel

practices in any of 43 areas such as hiring, termination,

reasonable accommodation, harassment, or the terms and

conditions of employment. These employer activities are

collectively known as ‘‘issues.’’

Most ‘‘regarded as’’ allegations do not involve the

simple presence or absence of an actual impairment. More

common is an actual but not-substantial impairment, the

seriousness of which may be exaggerated by the employer

to the extent that the worker’s ability to safely perform the

essential functions of the job is brought into question. This

is infrequently a calculated lack of animus, but more often

a subtle misperception rooted in commonly held stereo-

types regarding certain impairments [1]. These ‘‘innocent

mistakes’’ are well documented in literature pertaining to

stigma theory (the stigmatization of an individual or group

W. R. Draper � C. E. Hawley (&) � B. T. McMahon �C. A. Reid � L. A. Barbir

Department of Rehabilitation Counseling, Virginia

Commonwealth University, PO Box 980330,

Richmond, VA 23298, USA

e-mail: cehawley@vcu.edu

123

J Occup Rehabil

DOI 10.1007/s10926-013-9464-7

based on an attribute, such as a disability) [2–7] and causal

attribution theory (the assignment of judgments, justified or

not, to observed behavior, i.e. ‘‘once disabled always dis-

abled’’) [1, 8–18]. Such models may provide an under-

standing of personnel actions that result in disability-based

discrimination, but not an excuse. All personnel actions

related to the existence or consequence of disability are,

under ADA, unlawful.

A previous study of compared allegations of perceived

discrimination (REGAS N = 34,222, the target group)

with those of the first prong of the definition; i.e., current

and documented disability (DOCDIS N = 338,861, the

comparison group) [11]. Many variables were studied but

the fourth research question, which tested differences in the

all-important outcome variable of ‘‘merit resolution,’’ led

to findings that provided the impetus for the present study.

‘‘Merit’’ refers to the outcome of investigations by the

Equal Employment Opportunity Commission (EEOC)

regarding the validity of each allegation. This was the

finding [11, p. 8]

The fourth research question addresses how the cases

were resolved. Here again, there were differences:

merit resolutions for REGAS proportionately excee-

ded those for DOCDIS by a statistically significant

margin (26.2 vs. 22.5 %):X2 (13, N = 377,580 =

637.383); p \ .001; d = .08. The largest effect sizes

(d) for the variables studied were as follows: 0.23 for

issue, 0.18 for census region, 0.12 for race, and 0.11

for industry type (NAICS code).

This significantly higher merit rate was a high impact

finding. First, it provided evidence for the assertion that

this ‘‘alternative prong’’ is an important aspect of work-

place discrimination. Not only did ‘‘regarded as’’ alle-

gations total in the tens of thousands, but they involved a

higher proportion of actual discrimination (vs. perceived

discrimination) than the first prong. In the ‘‘run-up’’ to

the ADA Amendments Act of 2008, employers openly

challenged the veracity of alternative prongs as unnec-

essary and confusing. However, Draper, Reid, and

McMahon [11] demonstrated that at least the third

alternative prong ‘‘has legs,’’ and the alternative prongs

were preserved and affirmed in the Amendments. Thus,

the authors set forth in this study to develop a still clearer

understanding of what drives merit relative to ‘‘regarded

as’’ allegations.

What follows is a decision tree analysis using the

Chi square automatic interaction detector (CHAID) to

determine which variables influenced the mutually

exclusive outcomes of merit (an EEOC conclusion that

discrimination has occurred) vs. non-merit (an EEOC

conclusion that evidence is insufficient to reach a con-

clusion that discrimination has occurred). In simple

terms, this study attempts to identify the ‘‘drivers’’ of

EEOC resolutions.

Methods

The present study is a retrospective analysis of EEOC data

found in its Integrated Mission System (IMS). This anal-

ysis does not involve sampling. The IMS includes the

entire population of reported allegations involving the

‘‘regarded as disabled’’ prong of the ADA definition of

disability. With nearly 3 million allegations, the IMS

includes all ADA-related discrimination allegations filed

from July 26, 1992 through December 31, 2008. The unit

of study is the individual allegation. Confidentiality was

protected through purging identifying information from the

data. Only allegations related to ADA title I employment

provisions were included, and allegations were excluded if

filed on the basis of other employment statutes, or if

investigated by entities other than the EEOC. Only closed

allegations were extracted so that the outcome of each

investigation was known.

Exactly 34,222 allegations were filed under people

whose disability status was defined by the third prong:

perceived disabilities. For each allegation, the following

data were available:

• Age of charging party (CP): (B29; 30–39; 40–49;

50–59; C60)

• Gender of CP

• Race/Ethnicity of CP (Black, Hispanic, White, Asian,

Mixed Ethnicity, other)

• Issue (nature of the discrimination alleged; e.g., hiring,

termination, harassment, etc.)

• Size of company (i.e., number employed: 15–100;

100–200; 201–500; 500?)

• Employer industry (BLS/NAICS categories: transporta-

tion/warehousing; health care/social assistance, etc.)

• Outcome of EEOC investigation (merit or non-merit

resolution/closure)

The research question addressed was: what combination

of factors (variables) are associated with merit outcomes of

allegations of disability-based workplace discrimination

under ADA filed by charging parties on the basis of per-

ceived (‘‘regarded as’’) disability status?

Data Analysis

A decision-tree analysis is a graphic, tree-like classification

model that investigates multi-level interactions. In this

study, it was used to identify and rank order those predictor

variables which influence the dichotomous dependent

variable of merit vs. non-merit outcome. CHAID is a

J Occup Rehabil

123

non-parametric exploratory decision-tree technique useful

for extracting meaningful patterns of information from

large databases; it also prioritizes groups of homogeneous

allegations, or end groups, on the basis of their contribution

to the dichotomous outcome variable of merit resolution

[19]. The end groups are shown on the classification tree as

‘‘nodes.’’ With this method, combinations of variables

serving as predictors of merit outcome for the cases are

tested individually [20]. Apart from its graphical depiction

of variable interactions, this technique has the added

advantage of not being limited by the distributional

assumptions required by traditional methods. The require-

ment is that the dependent variable must be dichotomous.

The software used is SPSS Answer Tree 3.1 (SPSS, Inc.,

2002). This technique has been widely used in the National

EEOC ADA Research Project to further our understanding

of high prevalence ADA discrimination issues such as

hiring [19], harassment [21], and discharge [22].

Specifically, CHAID first establishes an independent

variable, which serves as an optimal predictor, one

according to which the data are subdivided [23]. Then, Chi

square significance levels are used to determine maximal

explanatory value in terms of variance of the dependent

variable. Each subgroup is re-analyzed independently, and

the process continues until there are no longer any signif-

icant Chi square values available [24]. The resulting clas-

sification tree provides a graphic, hierarchical display of

variable interactions. The CHAID decision tree analysis is

performed in order to examine the interaction of multiple

significant variables, thus yielding information of more

complexity than a standard Chi square analysis.

Results

Claimant and industry demographics are provided in

Tables 1 and 2. Figures 1 and 2 depict the CHAID decision

tree, which graphically depicts the influences of the various

independent variables on the dependent variable of merit

outcome (closure).

Conclusively, the most significant variable predictive of

merit resolution found was issue (v2 = 58.08, df = 8,

p = 0.000). Issue is the nature of the allegation filed with

the EEOC. The two issues that were most predictive of

merit outcome were prohibited medical inquiry (Node 8,

62.5 %, N = 512) and testing (Node 7, 50.0 %, N = 82).

However, these issues involved a small number of allega-

tions and were not influenced by other variables.

Issue is, in turn, driven by the variable of industry type.

Since the purpose of the study was to examine the inter-

action effects of the predictor variables on the dependent

variable of merit outcome, Fig. 1 depicts which clusters of

variables (as CHAID nodes of information) emerge as

Table 1 Charging party and employer demographics

Variables Frequency Percentage

Gender

Male 20,637 53.8

Female 17,723 46.2

Ethnicity

African American 6,582 20.8

Hispanic 2,265 7.2

Other 2,945 9.3

Caucasian 19,878 62.8

Age

B29 3,381 9.4

30–39 8,769 24.4

40–49 12,462 34.7

50–59 8,413 23.4

60C 2,939 8.2

Industry

Agriculture 264 1.0

Mining and construction 1,136 4.2

Manufacturing 6,050 22.3

Transportation and utilities 3,090 11.4

Wholesale and retail trade 3,672 13.5

Financial, insurance, real estate 1,248 4.6

Services 8,963 33.0

Public administration 2,753 10.1

Employer size

15–100 12,710 34.5

101–200 4,368 11.9

201–500 4,326 11.8

500? 15,407 41.9

Issues (grievance)

Discharge 14,282 36.9

Reasonable accommodation 3,161 8.2

Harassment 2,507 6.5

Terms/conditions 3,684 9.5

Hiring 3,601 9.3

Discipline 1,174 3.0

Promotion 851 2.2

Wages 887 2.3

Suspension 8,572 22.1

*The other category includes Native Americans, Asians, and mixed

race individuals

Table 2 Merit closure status

Frequency Percentage

Non merit 28,567 73.8

Merit 10,152 26.2

Total 38,719 100.0

J Occup Rehabil

123

significant predictors and with which other variables they

interact.

Qualification standards (Node 6). Allegations of this

issue had a merit resolution rate of 38.8 % (2.9 % of total

allegations for perceived disability). Industry type served

as the second most significant predictor of merit closure,

yielding two industry clusters. Within the issue of qualifi-

cation standards, mining, manufacturing and construction

(Node 25) had a greater predictive value for merit outcome

than did the group comprising services, transportation/

utilities, public administration and finance (Node 24) (46.9

vs. 30.3 %) (v2 = 32.7, df = 1, p = 0.00). There was no

further branching of the CHAID tree from either of these

nodes; that is, no further interactive information of any

significance was found by the program. However, three

additional issues contributed heavily to the higher merit

rate for ‘‘regarded as’’ and they yielded interactive infor-

mation about additional variables.

Hiring and reinstatement (Node 3). These allegations

(11.6 % of total) had a merit resolution rate of 34.3 %.

Industry type, in turn, drove this high issue merit rate, led

by manufacturing/agriculture (39.8 % in turn driven by CP

race), transportation/utilities/public administration/finance

(31.3 % in turn driven by CP age), and other services

(26.8 %).

Demotion and job assignment (Node 4). These allega-

tions (4.3 % of total) had a merit resolution rate of 29.8 %.

Industry type again drove this high issue merit rate, led by

other services/transportation/utilities/public administration

(25 % in turn driven by CP race) and finance/insurance/real

estate/construction (34.8 % in turn driven by issues).

Terms and conditions/early retirement (Node 1). These

allegations (26.9 % of total) had a merit resolution rate of

26.6 %. Industry type again drove this high Issue merit

rate, led by transportation/utilities/mining/construction

(29.3 %), wholesale/retail/manufacturing (26.7 %); other

services/finance/real estate (24.4 % in turn driven by gen-

der), and public administration/agriculture (20.3 % driven

in turn by race).

It is noted that although they did not elevate the merit

rate for ‘‘regarded as’’ allegations, discharge and suspen-

sion (40.2 % of all ‘‘regarded as’’ allegations and a merit

rate of 22.7 %) and harassment and discipline (12.2 % of

all ‘‘regarded as’’ allegations and a merit rate of 20.9 %)

provided a firm floor of support close to but not signifi-

cantly lower than the merit rate of 22.5 % for the first

Node 1Category % n#0 73.4 7649#1 26.6 2779

Total 26.9 10428

Node 2 Category % n#0 77.3 12015 #1 22.7 3536

Total 40.2 15551

Node 3 Category % n#0 65.7 2951#1 34.3 1540

Total 11.6 4491

Node 0 Category % n# 0 73.8 28567# 1 26.2 10152

Total (100) 38719

Node 10 Category % n#0 75.6 1995#1 24.4 643

Total 6.8 2638

Node 11Category % n#0 70.7 3010#1 29.3 1249

Total 11.0 4259

Node 12Cat. % n#0 79.7 709#1 20.3 181

Total 2.3 890

Node 16Category % n#0 73.7 3587#1 26.3 1282

Total 12.6 4869

Node 15Category % n#0 77.8 3575#1 22.2 1022

Total 11.9 4597

Node 14Category % n#0 79.8 4853#1 20.2 1232

Total 15.7 6085

Node 17Category % n#0 73.2 719#1 26.8 263

Total 2.5 982

Node 18Category % n#0 68.7 959#1 31.3 437

Total 3.6 1396

Node 26Category % n#0 73.6 1185#1 26.4 426

Total 4.2 1611

Node 27Category % n#0 78.9 810#1 21.1 217

Total 2.7 1027

Node 28Category % n#0 77.6 547#1 22.4 158

Total 1.8 705

Node 30Category % n#0 82.2 1654#1 17.8 359

Total 5.2 2013

Node 31Category % n#0 78.6 3199#1 21.4 873

Total 10.5 4072

Node 33Category % n#0 78.9 2933#1 21.1 783

Total 9.6 3716

Node 19Category % n#0 60.2 1273#1 39.8 840

Total 5.5 2113

Node 13Category % n#0 73.3 1935#1 26.7 706

Total 6.8 2641

Node 29Category % n#0 87.6 162#1 12.4 23

Total 0.5 185

Node 32Category % n#0 72.9 642#1 27.1 239

Total 2.3 881

Node 34Category % n#0 65.7 592

#1 34.3 309Total 2.3 901

Node 35Category % n#0 74.1 367#1 25.9 128

Total 1.3 495

Node 37Category % n#0 57.4 746#1 42.6 553

Total 3.4 1299

Node 36Category % n#0 64.7 527#1 35.3 287

Total 2.1 814

Terms&C/EarlyRetireIncv

AsAmer;White;Hisp

Other;mssg

Mfg; AgricTrans/Ut;PubAdm;FinanServices<missing>Wh/Ret;Mfg;Mng;CnstSvcs;Trans/Util;PubAdm&cWholesa;Ret;MfgPubAdmin;AgricTrans/Util;Mining;Constr; ,<missing>

Svcs;Finan;RealEst

Hiring/ReinstatementDischarge/Suspension

Issue categoryAdj. P-value=0.000 Chi-sq.=58.088,

df=8

MERIT CLS

AsAmer;Other;Hisp

White;<mssg>

<=29;60+

40s;50s;30s

<=29;50s;60+;30s

Male 40s;<missg>

AsAmer;Hisp;<missg>

White;Other

Industry categoryp=0.00, ChiSq=40.7,df=3

Industry categoryp=0.00,ChiSq=57.9,df=2

Industry categoryp=0.00,ChiSq=58.0,df=2

CPSEXp=0.006;ChSq=9.6;df=1

4RACEcatp=0.04;ChSq=9.0;df=1

4RACEcatp=0.015;ChSq=10.8;df=1

AGErangep=0.003;ChSq=15.1;df=1

AGErange p=0.036;ChSq=10.5;df=11

4RACEcatp=0.012;ChSq=11.1;df=1

Fem;<mssg

Fig. 1 Split view of left branch

J Occup Rehabil

123

prong. Only union representation (9.7 % merit rate,

N = 110) provided any ‘‘drag’’ on the ‘‘regarded as’’ merit

rate, but its tiny cell size rendered the effect

inconsequential.

Discussion

This study examined the effects of variables and their

interactions upon the EEOC investigation outcome variable

known as merit rate. One research question was posed:

which independent variables serve as predictors of merit

closure for allegations derived from charging parties

regarded as having a disability? The relevant independent

variables are: CPs age, race, gender; employer industry

classification and size; and issue (grievance). The CHAID

analysis showed that the most significant predictor of merit

closure (the dependent variable) was Issue, followed by

industry classification. Industry was driven in some

instances by age, race, gender or issue (see Wilkinson [25]

on the ‘‘re-emergence’’ of a CHAID variable in the deci-

sion tree). Each variable in the tree significantly affected

the one above it. That is to say, the level of merit closure is

heavily influenced by certain combinations of issues. And

the merit rate in those ‘‘issue combinations’’ was in turn

heavily influenced by certain combinations of industry

sectors. This is consistent with the stated purpose of the

study, namely, to consider, through the use of CHAID, the

interactions of the predictor variables that influenced the

decisions of the EEOC regarding investigation outcomes.

1. Regarding ADA title I allegations derived from

charging parties who were regarded as individuals

with disabilities, the highest level of merit was

achieved in 440 instances when the Issue contested

was qualification standards (38.8 %). This means that

discrimination occurred with respect to the factors or

criteria used in determined one’s fitness for employ-

ment, referral, promotion, admission to membership in

a labor organization, training, or assignment to a job or

class of jobs.

2. The second highest level of merit was achieved in

1,540 instances when the issue contested was hiring or

reinstatement (34.3 %), and was driven higher if the

industry sector was manufacturing agriculture (39.8 %)

Node 4Category % n#0 70.2 1175#1 29.8 499

Total 4.3 1674

Node 5Category % n#0 79.1 3747#1 20.9 989

Total 12.2 4736

Node 0 Category % n# 0 73.8 28567# 1 26.2 10152

Total (100) 38719

Node 20Category % n#0 75.0 637#1 25.0 212

Total 2.2 849

Node 23Category % n#0 76.2 1455#1 23.8 454

Total 4.9 19091703

Node 22Category % n#0 81.1 2292#1 18.9 535

Total 7.3 2827

Node 6Category % n#0 61.2 695#1 38.8 440

Total 2.9 1135

Node 7Category % n#0 50.0 41#1 50.0 41

Total 0.2 82

Node 38Category % n#0 80.7 272#1 19.3 65

Total 0.9 337

Node 39Category % n#0 71.3 365#1 28.7 147

Total 1.3 512

Node 40Category % n#0 69.8 303#1 30.2 131

Total 1.1 434

Node 42Category % n#0 77.8 1089#1 22.2 311

Total 3.6 1400

Node 43Category % n#0 84.3 1203#1 15.7 224

Total 3.7 1427

Node 45Category % n#0 81.0 570#1 19.0 134

Total 1.8 704

Node 8Category % n#0 37.5 192#1 62.5 320

Total 1.3 512

Node 21Category % n#0 65.2 538#1 34.8 287

Total 2.1 825

Node 41Category % n#0 60.1 235#1 39.9 156

Total 1.0 391

Node 44Category % n#0 73.4 885#1 26.6 320

Total 3.1 1205

Node 24Category % n#0 69.7 388#1 30.3 169

Total 1.4 557

Node 25Category % n#0 53.1 307#1 46.9 271

Total 1.5 578

Node 9Category % n#0 92.7 102#1 7.3 8

Total 0.3 110

Union RepresentnProhibt Med InqryTestingQualificatn Standrds

Svcs;Trans/Ut;PubAdm;Finan;Ins:REst;Whol;Ret

Mfg;Mining;Construction;

<missing>

Industry categoryp=0.00;ChSq=32.7;df=1

Demotion/Assignment Harassment/Discipline

Industry categoryp=0.003;ChSq=19.2;df=1

Industry categoryp=0.014;ChSq=16.2;df=1

Industry categoryAdj. P-value=0.000 Chi-sq.=58.088,

df=2

MERIT CLS

Trans/Ut;Finan;REstSvcs;Pad;Whol;RetlFin;Ins;REst;CnstSvcs;Trans/Ut;PAdm

AGErangep=0.006;ChSq=13.8;df=1

AGErangep=0.00;ChSq=19.5;df=1

ISSUEp=0.024;ChSq=8.5;df=1

1p=

4RACEcatp=0.029;ChSq=9.6;df=1

P

Demotion;Benefits/Pension

Job Classifn;Assignment

<=29;40-49;60+

50s;30s;<mssg>

<=29;50s;30s;<missg>

40-49;60+White;Hisp

AsAmer;Other;<msg>

Fig. 2 Split view of right branch

J Occup Rehabil

123

and, higher still, if the CP was white or other race

(42.6 %).

3. The third highest level of merit was achieved in 499

instances when the Issue contested was demotion or

job assignment (29.8 %), and was driven higher if the

industry sector was finance, insurance, real estate or

construction (34.8 %) and the issue was job classifi-

cation or assignment (39.9 %).

4. The fourth highest level of merit was achieved in 2,779

instances when the issue contested was retirement or

the terms/conditions of employment (26.6 %), and was

driven higher if the industry sector was transportation,

utilities, mining or construction (29.3 %), or whole-

sale, retail, or manufacturing (26.9 %).

Other industry sectors shared a baseline merit rate

consistent with the first prong, or were very low but too

small to have much effect upon ‘‘regarded as’’ merit (union

representation). It is curious that the most prevalent issues

in the entire IMS as well as the ‘‘regarded as’’ cohort

(termination, failure to accommodate, and harassment) do

not distinguish themselves in the ‘‘regarded as’’ CHAID

findings as drivers of merit. This demonstrates that when it

comes to merit resolution, the alternative prong of

‘‘regarded as’’ has a unique role in the implementation of

ADA and the wisdom of Congress, for its inclusion in the

Act is affirmed.

Conclusions

The authors followed up on a single finding of interest in

previous studies, which examined the nature and scope of

workplace discrimination in America involving charging

parties who filed allegations that they were ‘‘regarded as’’

individuals with disabilities, a special status afforded pro-

tections in the ADA by an expanded definition of the term

‘‘disability.’’ Specifically, we have endeavored to explore

the circumstances surrounding an EEOC finding of merit

(actual discrimination) when such cases are concluded as

opposed to a routine allegation that does not rise to the

level of discrimination. In this study we have asked and

answered the question of ‘‘What influences merit out-

comes?’’ by applying a CHAID analysis to the universe of

34,222 closed investigations. The first clear conclusion was

that merit rates increased markedly when a handful of

specific issues were contested: qualification standards,

hiring, reinstatement, demotion, job assignment, retire-

ment, or the terms/conditions of employment. These rates

were driven even higher in a handful of industry sectors,

depending upon the issue involved: manufacturing, mining,

construction, agriculture, wholesale, retail, transportation,

utilities, finance, insurance, real estate, and other services.

Employers in these industries who receive notification that

a ‘‘regarded as’’ allegation has been received should

understand that their prospects for prevailing in the

investigation can be markedly reduced if the aforemen-

tioned issues are contested. Furthermore, some pre-emptive

training about ‘‘regarded as’’ and other alternative prong

definitions is readily available at low cost from the

National Network of ADA Centers and their affiliates.

Guidance of this nature may mitigate the frequency of such

allegations or expedite their mediation and internal reso-

lution when they arise.

It is also apparent that the ‘‘regarded as’’ prong expands

the reach of the ADA far beyond the disability community

in terms of coverage. Indeed, these 34,222 allegations

likely derived from Americans with minimal impairments

or no disability whatsoever. Such individuals are less likely

to be ADA literate and, moreover, are even less likely to

understand that they have ADA protections when mistak-

enly ‘‘regarded as,’’ and thus the third prong is offering an

above average level of effectiveness as a remedy if mea-

sured by its merit rate. No ADA architect envisioned that

‘‘regarded as disabled’’ claims would rise in number to

become nearly 10 % of all ADA files. None anticipated a

merit rate higher than that of the first prong. Yet that is

precisely what has occurred, and we now have a better

understanding of the issues and industries involved in

actual findings of ‘‘regarded as’’ discrimination. The find-

ings highlight the need for ADA training for all Americans

(with or without disabilities), to encourage self-advocacy,

potentially modifying behaviors of employers and reducing

future allegations.

A limitation of this study is the lack of information in

the database about disability type for the alternate prongs.

Potentially revealing and useful future research could

emerge if we better understood the perceived disability

type in ‘‘regarded as’’ claims. For example, concerning the

most frequent issue, discharge, it would be useful to know

how cognitive errors regarding disability inform the deci-

sion to terminate an employee.

Acknowledgments This project was approved by Virginia Com-

monwealth University’s Internal Review Board, #HM11303.

Conflict of interest The authors Draper, Hawley, McMahon, Reid

and Barbir declare no conflict of interest.

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