Dear Colleagues,

maybe some of you are also involvend with teaching survey methodology and could benefit from participating at the 11th conference of the European Survey Research Association (ESRA) (https://www.europeansurveyresearch.org/conference/utrecht-2025/call-for-abstracts/), which takes place at Utrecht University, The Netherlands, from July 14 to July 18, 2025.

The session "Teaching Survey Methodology – Challenges and New Approaches" (https://www.europeansurveyresearch.org/conf2025/sessions.php?sess=79) is organized by me (Katrin Drasch, FAU Erlangen-Nürnberg) but I hope that there are some of you somewhere who are in a similar situation/position and we can share some experiences.

Please submit your abstract (max. 300 words) via the ESRA conference management system by December 20, 2024. 

 

Session Details

Many higher education curricula involve survey data collection by students, aiming to teach a complete empirical research circle from formulating a research question to finding a preliminary empirical answer. This approach is often justified because it constitutes a crucial skill for future careers in both academic and non-academic environments. However, over the past two decades, data collection has increasingly shifted from written or oral/telephone interviews to more complex methods like online surveys, longitudinal studies, and alternative data sources, making it increasingly challenging to teach survey methodology in its entirety. Current curricula often face two major issues: first, the limited time available for empirical research projects, typically a maximum of two academic terms, often necessitates shortcuts, such as skipping cognitive pretesting, to complete data analysis on time. Second, there is often little financial support for material expenses (e.g., postal charges, printing questionnaires) or technical resources (e.g., access to sampling frames). This can lead to the misinterpretation of results, such as treating non-probability snowball sampling as representative. As a result, self-collected data often suffer from quality issues and are thus not suitable for scientific publications. This session aims to discuss these challenges and explore new approaches to collecting scientifically valuable data in a teaching environment. For instance, experimental designs (e.g., factorial surveys or choice experiments) that do not require large or representative samples could be considered. Additionally, incorporating AI tools into survey methodology instruction, such as using them to generate and pretest questionnaires, could provide new opportunities. Alternative data sources (e.g., social media data or anonymized university records) and collaborations with external (non-academic) partners, along with the challenges they pose (e.g., data protection and exchange), could also be explored. These and other alternative and/or innovative topics are welcome to this session.

 

I am looking forward to your submissions. Please feel free to forward this call within your networks!

Best regards

Katrin Drasch