A dilemma arises for researchers who sample hidden populations, such as injection drug users (IDUs), and use financial incentives to recruit respondents. To prevent respondent duplication (a subject participates in a study multiple times by using different identities) and respondent impersonation (a subject assumes the identity of other respondents), researchers must confirm their subjects' identities. Documentation, however, introduces sampling bias against those who lack such identification, or who wish to remain anonymous. Definitive forms of identification like photography and fingerprints introduce a bias against the more distrustful members of the population, and scanner-based biometrics can be expensive. Most research projects therefore rely on staff to recognize former respondents, but staff turnover and a large number of respondents compromise accuracy. We describe and assess quantitatively the accuracy of a method for subject identification based on a statistical principle, the interchangeability of indicators, in which multiple weak indicators combine to form a stronger aggregate measure. The analysis shows that observable indicators of identity (scars, birthmarks, tattoos, eye color, ethnicity, and gender) and five biometric measures (height, forearm lengths, and wrist widths) provide the basis for a reliable and easily administered method for subject identification.
研究人员在对隐藏人群(如注射毒品者)进行抽样调查并使用财务激励来招募受访者时会遇到一个困境。为了防止受访者重复参与研究(同一受访者使用不同身份多次参与研究)和受访者冒充(一个受访者冒用其他受访者的身份),研究人员必须确认受访者的身份。然而,文件化方法会引入采样偏倚,对那些缺乏身份证明或希望保持匿名的人造成不利影响。确凿的身份识别形式(如摄影和指纹)会对人群中更加不信任的成员产生偏见,而基于扫描仪的
生物特征识别可能成本高昂。因此,大多数研究项目依赖工作人员识别先前的受访者,但工作人员流动和大量受访者会影响准确性。我们描述并定量评估了一种基于统计原理的主体识别方法的准确性,即指标的可互换性,即多个弱指标结合形成更强的综合度量。分析表明,身份的可观察指标(疤痕、胎记、纹身、眼睛颜色、种族和性别)以及五个
生物测量(身高、前臂长度和腕围)为主体识别提供了可靠且易于管理的基础方法。