ESRA 2027 sessions by theme

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Session Details

This session focuses on the opportunities of new data sources, AI, and computational methods for improving the data quality of longitudinal panel studies that collect information on respondents’ life histories. Longitudinal panels are important to track change within individuals and their dynamic social and economic environment. They also allow studying causes of individual success and / or potential gatekeeping mechanisms.
Maintaining high quality in longitudinal survey data comes with substantial methodological challenges in design, data collection, and data processing. New data sources, AI, and computational methods can offer new opportunities to improve longitudinal survey data collection and quality assurance.
This session aims to bring together scholars and practitioners working with longitudinal survey data and respondent biographies. It encourages exchange on successes and failures in using new technologies to tackle common problems, such as panel attrition, data inconsistencies during and post-fieldwork, harmonisation and coding, etc., and welcomes discussions on the experiences with responsive modes, respondent tracking, life event diaries, and beyong. Moreover, the session is interested in data linkage, e.g., can linked contextual data improve accuracy in respondent biographies effectively and efficiently.
The session intends to cover different stages of the panel survey cycle. It explicitly encourages quality assurance contributions connecting field work with post-data collection, giving insights to automated detection methods and reduction of manual labour in tackling potential problems. The goal of the session is to gain practical insights in order to provide guidance for researchers seeking to maximize the quality of panel survey data, reducing total survey error in complex studies. The plan is to submit a special issue proposal on panel surveys and new technology following the conference.