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The Anonymous Collection of Longitudinal Data: An Evaluation of Self-Generated Identification Codes and
Jodi B A McKibben1, Kimberly Bucklin1, Jordan Salafia1
1West Chester University of Pennsylvania, USA.
Educational and Psychological Measurement
|June 8, 2026
Summary
Self-generated identification codes (SGICs) can effectively link longitudinal data, but perfect matches depend on code structure and participant trust. Reliability is enhanced when participants perceive the study as trustworthy and anonymous.
Area of Science:
- Psychology
- Data Science
- Research Methodology
Background:
- Self-generated identification codes (SGICs) are crucial for anonymous data linkage in longitudinal studies.
- Low participant match rates across time points in longitudinal research lead to data loss and reduced statistical power.
- There is a lack of empirical evaluation for SGIC development methods.
Purpose of the Study:
- To evaluate the effectiveness and reliability of a newly developed SGIC.
- To analyze how perceptions of data sensitivity influence SGIC matching rates.
- To identify factors predicting successful SGIC matching in longitudinal research.
Main Methods:
- 135 participants completed a newly developed SGIC in two sessions, 8 weeks apart.
- An experimental group was led to believe sensitive information was collected, while a control group focused on the SGIC.
- Multivariate logistic regression analyzed predictors of group membership and perfect SGIC matches.
Main Results:
- Over 91.9% of participants achieved at least 10 correct SGIC element matches.
- Only 40.0% of participants achieved perfect matches across all 12 SGIC elements.
- Higher trust ratings predicted control group membership (OR=0.541, p=.021); trustworthiness predicted perfect matches (OR=4.975, p=.003).
Conclusions:
- SGICs can be effective for longitudinal data linkage under specific conditions.
- SGIC reliability is influenced by code structure and participant perceptions of trustworthiness, anonymity, and engagement.
- The findings highlight the need for empirically grounded standards in SGIC development to balance privacy and data accuracy.
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