A Methodology for Reducing Respondent Duplication and Impersonation in Samples of Hidden Populations

Abstract

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.