Department of Rehabilitation Counseling, Virginia Commonwealth University, PO Box 980330, Richmond, VA, 23298, USA.
J Occup Rehabil. 2014 Jun;24(2):213-9. doi: 10.1007/s10926-013-9464-7.
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.
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?
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.
本研究旨在探讨大型政府数据库中与感知残疾索赔相关的预测变量之间可能存在的相互作用。具体来说，这是对美国平等就业机会委员会（EEOC）数据的回顾性分析，这些数据基于《美国残疾人法》（ADA）残疾定义中“视为残疾”的规定，涵盖了整个工作场所歧视索赔人群。
该研究利用 EEOC 综合任务系统的“主数据库”中提取的记录进行，该数据库包含自 1992 年 7 月 26 日至 2008 年 12 月 31 日期间提交的所有与 ADA 相关的歧视指控。卡方自动交互检测（CHAID）用于分析相关变量（如问题[申诉]和行业类型）的交互作用。CHAID 分析的研究问题是：对于被“视为”残疾的 ADA EEOC 指控者，哪些因素组合与他们的优势结果相关？
CHAID 分析显示了优势结果如何通过相关变量的交互作用来预测。问题被发现是确定优势结果的最突出变量，其次是行业类型，但由于年龄和种族数据的资格规定，情况变得更加复杂。尽管解雇是感知残疾群体中投诉方最常见的申诉，但它的优势结果明显低于招聘的主要因素。
