Reposted by Florian Keusch
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![Survey Recruitment on LinkedIn: A Step-by-Step Guide to Targeted Outreach
March 25, 2026 31 min read tutorials [Zaza Zindel Lisa de Vries]
LinkedIn’s professional user base and precise targeting capabilities present social science researchers with a valuable opportunity to recruit survey participants from highly specific populations, particularly those relevant to research on employment and industry dynamics. While platforms such as Facebook and Instagram are commonly used for recruitment of survey participants, LinkedIn’s potential in survey participant recruitment remains underutilized. Its professional focus, career-centric context, and only minimal off-topic content make it ideal for reaching the potential labor force population. This is particularly relevant for studies requiring insights from specific industries, occupations, or education levels. In this Methods Bites Tutorial, Dr. Zaza Zindel (German Centre for Integration and Migration Research (DeZIM)) and Dr. Lisa de Vries (FernUniversität in Hagen) provide a step-by-step guide on how to use LinkedIn ads to recruit participants for survey research.
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Social media Survey data Data collection](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:oizrkjzyr6vekl76tjctghcl/bafkreictz42gbz2dbeds5r26mqj62rmnhpdpctp3t2rrwd4gszeehmv4x4)
![2026-04-22 | Input talk | Leah von der Heyde (GESIS)
AIn't Nothing But a Survey? Using Large Language Models for Coding Open-Ended Survey Responses
Hybrid event [A5, 6, Room A231 + Zoom]
April 22, 2026, 13:45-15:15
Abstract
Large language models (LLMs) have the potential to make survey research more efficient, including the classification of open-ended survey responses. However, as most existing research on this topic has focused on English-language text or single LLMs, it is unclear whether their applicability generalizes and how the quality of classifications compares to established methods. In this talk, I will demonstrate how LLMs can be used for coding open-ended responses using different access options and prompting and fine-tuning techniques. I will present a study testing these approaches on a dataset of German open-ended survey responses, comparing several LLMs to human coders and other automated methods. Finally, I will discuss the implications of the study findings for practitioners, including the many trade-offs researchers need to consider.
Presenter(s)
Leah von der Heyde is a computational social scientist and survey methodologist. Her research focuses on the potential and pitfalls of new data sources, such as large language models, for improving the measurement and representation of public opinion. Substantively, she is particularly interested in political attitudes and voting behavior. Leah received her PhD in Social Data Science and Research Methodology from the University of Mannheim. She has a background in political science from LMU Munich, the University of Mannheim, and Georgetown University. Previously, Leah was part of the Social Data Science and AI Lab at LMU Munich, worked for the European Social Survey at GESIS - Leibniz Institute for the Social Sciences, the European Parliamentary Research Service, and several market and public opinion research institutes in Germany and Sweden. At GESIS, she is part of KODAQS, researching and educating…](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:oizrkjzyr6vekl76tjctghcl/bafkreigb4xgjzges3yeoup7w3z3goxg6fgei4qiystwxbq4zkvjughks6m)
