A hierarchical clustering approach to identify repeated enrollments in web survey data.

ABSTRACT:

Introduction

Online surveys are a valuable tool for social science research, but the perceived anonymity provided by online administration may lead to problematic behaviors from study participants. Particularly, if a study offers incentives, some participants may attempt to enroll multiple times. We propose a method to identify clusters of non-independent enrollments in a web-based study, motivated by an analysis of survey data which tests the effectiveness of an online skin-cancer risk reduction program.

Methods

To identify groups of enrollments, we used a hierarchical clustering algorithm based on the Euclidean distance matrix formed by participant responses to a series of Likert-type eligibility questions. We then systematically identified clusters that are unusual in ter

SUBMITTER: Handorf EA 

PROVIDER: S-EPMC6155511 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature