Automated Preloading and Definition of Panel Samples |
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| Coordinator 1 | Ms Stephanie Stuck (SHARE Berlin Institute) |
| Coordinator 2 | Dr Fabio Franzese (SHARE Berlin Institute) |
| Coordinator 3 | Ms Carolina Brändle (SHARE Berlin Institute) |
Panel surveys can use demographic information and responses from previous interviews to enhance their efficacy through methods such as dependent interviewing, where previous answers guide subsequent questioning, and (sub-) sample definition, where specific respondent characteristics inform survey sampling strategies of a follow-up interview.
However, the increasing frequency and flexibility of panel surveys, including simultaneous conducted surveys and multi-mode administration (with or without interviewers), present significant challenges for preloading data. When multiple surveys are conducted simultaneously in the same sample, the question arises as to which information, i.e., which source (respondent self-completion, interviewers, survey institute, sampling/register information), should be trusted and used for subsequent interviews. This issue is closely linked with data cleaning processes, as a high frequency of interviewing may not allow for proper data cleaning before sampling or preloading the next survey wave. Addressing these challenges requires robust software infrastructure and innovative implementation strategies that meet the needs of both researchers and survey institutes.
This session aims to facilitate an exchange of knowledge, experiences, and best practices regarding the technical and conceptual aspects of preloading and defining panel samples. The session invites contributions that encompass several key areas:
• Technological Tools: A demonstration of the software and tools currently employed to preload and manage panel samples efficiently.
• Data Management: Best practices for handling data in the context of preloaded panel samples, ensuring data integrity and usability across survey waves.
• Practical Considerations: Addressing the logistical and operational challenges encountered in implementing preloading strategies across different survey modes.
• Impact on Data Quality: Evaluating how preloading and dependent interviewing impact the accuracy and reliability of survey data.
• Methodological Innovations: Highlighting recent advances and future directions in the field, focusing on innovations that enhance the flexibility and effectiveness of panel surveys.