CANDIDATE: A tool for generating anonymous participant-linking IDs in multi-session studies

Abstract

Background To ensure the privacy of participants is an ethical and legal obligation for researchers. Yet, achieving anonymity can be technically difficult. When observing participants over time one needs mechanisms to link the data from the different sessions. Also, it is often necessary to expand the sample of participants during a project. Objectives To help researchers simplify the administration of such studies the CANDIDATE tool is proposed. This tool allows simple, unique, and anonymous participant IDs to be generated on the fly. Method Simulations were used to validate the uniqueness of the IDs as well as their anonymity. Results The tool can successfully generate IDs with a low collision rate while maintaining high anonymity. A practical compromise between integrity and anonymity was achieved when the ID space is about ten times the number of participants. Implications The tool holds potential for making it easier to collect more comprehensive empirical evidence over time that in turn will provide a more solid basis for drawing reliable conclusions based on research data. An open-source implementation of the tool that runs locally in a web-browser is made available.

Online laboratory simulations as a pedagogy to reduce anxiety and build confidence for student success

“…All participants were provided with a participant information sheet and consent form on which they could provide consent to participate. Participants were assigned a randomised identifier using the CANDIDATE ID randomiser, available at https://frode-sandnes.github.io/CANDIDATE/ (Sandnes, 2021). This ensured anonymity of interview transcript files used in coding and surveys.…”