ADA perceived disability claims: a decision-tree analysis.
BACKGROUND
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 retrospective 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 Integrated 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 variables, such as issue (grievance) and industry type. The research question addressed by CHAID is: What combination 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 prominent variable in determining merit outcome, followed by industry type, but the picture is made more complex by qualifications regarding age and race data. Although discharge 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.
Department of Rehabilitation Counseling, Virginia Commonwealth University, PO Box 980330, Richmond, VA, 23298, USA.
William R
WR
Draper
Carolyn E
CE
Hawley
Brian T
BT
McMahon
Christine A
CA
Reid
Lara A
LA
Barbir
D000328:Adult; D016009:Chi-Square Distribution; D016208:Databases, Factual; D003663:Decision Trees; D006233:Persons with Disabilities / Q000331:legislation & jurisprudence / Q000706:statistics & numerical data*; D004651:Employment / Q000331:legislation & jurisprudence / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D007221:Industry / Q000706:statistics & numerical data*; D008297:Male; D008875:Middle Aged; D010560:Personnel Selection / Q000706:statistics & numerical data; D010563:Personnel Turnover / Q000706:statistics & numerical data; D011287:Prejudice / Q000331:legislation & jurisprudence / Q000706:statistics & numerical data*; D012189:Retrospective Studies; D014481:United States; D017132:Workplace / Q000331:legislation & jurisprudence / Q000706:statistics & numerical data