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Call for Sessions

The 12th  Conference of the European Survey Research Association (ESRA) will take place from 19th July to 23rd  July 2027. The conference will be hosted by a consortium of leading French research institutions. It includes Progedo (CNRS) – the national research infrastructure for quantitative social sciences, INED – the French Institute for Demographic Research and CDSP (Sciences Po) – the Center for Socio-Political Data. The Conference will be hosted by Campus Condorcet – one of Europe’s premier research campuses for social sciences and humanities. 

The conference theme is “Moving forward: integrating survey methods, statistics and social data science with new data sources, AI, and machine learning”.

The landscape of survey research is rapidly evolving. Technological advances, expanding digital infrastructures, and the development of artificial intelligence and machine learning are creating new opportunities to integrate survey methods, statistics, and social data science. Researchers and statistical agencies increasingly have access to diverse data sources such as digital transactions, mobile phone data, social media, satellite imagery, sensor data, and other forms of digital trace and geospatial information. At the same time, there is growing demand for data that are more timely, detailed, and cost-efficient. These developments offer important opportunities but also raise significant methodological challenges. While new data sources and computational tools can enrich traditional surveys, it is essential not to lose sight of the principles that have long ensured data quality and reliable inference. In this context, survey statistics and methodology play a crucial role in assessing data quality, addressing issues of coverage and representativeness, and supporting valid inference.

Therefore, the ESRA Conference Scientific Committee is now inviting researchers and practitioners to submit session proposals that explore how survey methods, statistical approaches, and social data science can be integrated to advance the field. Topics include the use of new data sources, applications of AI and machine learning, methodological innovations, and substantive research in the social and behavioural sciences.

Session abstracts (max. 300 words) should be submitted via the ESRA conference management system by 31st August 2026 here.

To do so, please log in to the system using your credentials. If you do not yet have an account, please create one first.

The following conference tracks and subtracks are of particular interest. Session proposals are expected to broadly align with these areas.

A) Survey methodology and survey practice

  1. Questionnaire design, testing and measurement 
  2. Online, mobile, and app-based surveys 
  3. Mixed-mode designs and mode effects
  4. Fieldwork processes, including responsive and adaptive designs
  5. Life-tracking and sensor-based data collection methods: Bluetooth technologies, near-field communication, smart surveys, and web scraping.
  6. Citizen Science
  7. Collecting biomarkers and other specimen data within surveys
  8. Analysing, monitoring and reducing Total Survey Error; using paradata to evaluate survey quality
  9. Interviewers and interviewer effects
  10. Cross-national and cross-cultural survey methods
  11. Survey experiments and causal inference
  12. Data management, processing, and open science
  13. Longitudinal survey design and analysis
  14. Proxy responses
  15. Nonresponse, survey participation, and survey climate
  16. Survey costs, value, and the economics of survey design
  17. Mixed methods: integrating qualitative and quantitative approaches
  18. Market research

B) Survey statistics

  1. Probability and non-probability sampling
  2. Spatial sampling and use of remote sensing data
  3. Missing data imputation
  4. Weighting approaches including survey calibration
  5. Small area estimation under non-probability and probability sampling
  6. Data integration methods (statistical matching and record linkage) with a particular focus on how to integrate new forms of data with traditional data sources
  7. How new forms of data can be used for population-size estimation
  8. More on post data collection processing such as coding and editing; related errors and how to take these into account
  9. Imputation approaches

C) Inclusive data collection and analysis in survey research

  1. Sampling and recruitment strategies for hard-to-reach, marginalised, and minority populations
  2. Multilingual survey design and cross-cultural questionnaire development
  3. Measuring gender identity, sexual orientation, and other diverse identities
  4. Accessibility in surveys (e.g., disabilities, digital exclusion, cognitive accessibility, literacy barriers)
  5. Ethical issues in inclusive data collection and sensitive identity measurement
  6. Participatory and community-engaged approaches to survey design and dissemination 
  7. Weighting, calibration, and adjustment methods for underrepresented populations
  8. Case studies and applications of inclusive survey methods in official statistics and social research 
  9. Survey research in conflict, crisis, and humanitarian contexts
  10. Capacity building and survey research in low-income contexts
  11. Analytical challenges with small and underrepresented subgroups

D) Data governance: documentation, archiving, and access

  1. Privacy, confidentiality and consent
  2. Data security and access
  3. Survey data harmonization: ex-ante and ex-post approaches
  4. Open data, FAIR principles, and data sharing 
  5. Reproducibility, transparency, and open science in survey research
  6. Data infrastructures and resources
  7. Data archiving, preservation, and long-term access

E) Artificial Intelligence and automated methods in survey research

  1. AI-assisted questionnaire design, translation, and pretesting
  2. Synthetic respondents and synthetic data
  3. AI interviewers and conversational survey agents
  4. Automated coding and processing of open-ended responses
  5. AI in data analysis
  6. Machine learning for weighting, imputation, and estimation
  7. AI in fieldwork management and adaptive survey operations
  8. AI in survey project management and workflow automation
  9. AI for dissemination, reporting, and public engagement with survey data
  10. Evaluating, auditing, and benchmarking AI tools in survey research
  11. Ethics, transparency, and accountability in AI-assisted survey research
  12. Bot detection and fraudulent responses in online surveys, including LLM-generated data

F) Substantive applications of survey research in the social and behavioural sciences.