The Anonymous Collection of Longitudinal Data: An Evaluation of Self-Generated Identification Codes and Methodological Challenges

Abstract

Self-generated identification codes (SGICs) are commonly used in longitudinal research studies to anonymously link data collected from a participant across time points. However, the lack of research evaluating the methods of developing SGICs is concerning, given that low match rates of participants across time points are common, resulting in significant data loss over time and diminishing statistical power. The current study aims to fill this gap by evaluating the effectiveness and reliability of a newly developed SGIC. Participants (n = 135) completed an SGIC during two sessions, 8 weeks apart. Researchers informed the experimental group that the second survey included sensitive information and the control group that the study focused solely on the SGIC. Although no sensitive information was requested, this allowed analysis of how the perception that sensitive information was being collected might affect the consequent matching of the SGIC. On average, more than 91.9% (91.5% experimental group, 92.2% control group) of participants had at least 10 correct SGIC element matches. Upon examining perfect matches, only 40.0% of participants matched on all 12 elements across the two sessions. Multivariate logistic analyses revealed that the trust rating was a significant predictor of group membership with higher trust ratings being associated with control group membership (odds ratio [OR] = 0.541, 95% confidence interval [CI] = [0.32, 0.91], p = .021) and trustworthiness was a significant predictor of matching on all 12 SGIC elements (OR = 4.975, 95% CI = [1.70, 14.55], p = .003). This study demonstrates that SGICs can function effectively under the right conditions, but reliability depends on the structure of the code itself and on participants’ perceptions of trustworthiness, anonymity, and engagement. As researchers continue to seek methods that balance participant privacy with data accuracy, the field must move toward empirically grounded standards.