ESRA 2019 - Call For Abstracts https://www.europeansurveyresearch.org/conferences/call_for_abstracts Session: Towards Strong Evidence-Based Survey Methodology using Replications, Systematic Reviews, or Bayesian Approaches https://www.europeansurveyresearch.org/conferences/sessionCategories?sess=4 Deadline: 18th November 2018 Dear Colleagues, we would like to draw your attention to the call for abstracts for our session "Towards Strong Evidence-Based Survey Methodology using Replications, Systematic Reviews, or Bayesian Approaches" at the 8th Conference of the European Survey Research Association, 15th-19th July 2019 in Zagreb, Croatia. Please submit an abstract (max. 300 words) to the session of your choice via the ESRA conference management system by 18th November 2018. To submit an abstract you must login to your ESRA account (or create a new account if you do not already have one) and then follow the instructions provided. Please note that it is only possible to submit two abstracts as the first author/presenter. Best regards, Bernd Weiss* Jessica Daikeler* Henning Silber* Michael Bosnjak+ * GESIS - Leibniz-Institute for the Social Sciences + Leibniz Institute for Psychology Information Session details: The conscientious, explicit and judicious use of current best empirical evidence in making decisions is the central paradigm of the evidence-based practice movement. This movement originated in the medical sciences but found its way in others disciplines like economics, social work and, to a much lesser extent, in survey methodology. The quality of the evidence can be evaluated according to various rating systems that distinguish between different levels of evidence. A common characteristic of these systems is that they value accumulated evidence based on full or partial replications higher than evidence based on a single (and singular) study. Furthermore, within the category of replicative studies, systematic reviews and meta-analysis are considered to promote the best available evidence -- with the gold standard for causal inference being systematic reviews and meta-analyses based on experimental studies. A related, but also overlooked question is how to appropriately incorporate existing evidence in a prospective survey methodological study. Usually, this is done in a qualitative and subjective manner by citing and discussing previous work. However, Bayesian approaches can help to be more rigorous and formal in terms of incorporating past evidence using informative priors, which then are contrasted and updated with the current study and data at hand. The overall aim of this session is to promote evidence-based survey methodology that is studies aimed at systematically aggregating high-quality evidence on issues relevant for preparing, implementing, and analyzing survey-based research. We encourage the submission of methodological papers, applications, and software/tool demonstrations. Eligible contributions may address, but are not limited to, the following topics: - Applications and challenges of replicative survey methodology, e.g. issues of pre-registration, determinants of replicability in survey methodology, etc. - Replicability in the context of big data - Systematic reviews, gap maps, and meta-analyses in survey methodology - Using Bayesian approaches to incorporate previous research findings - Software and tools supporting replications, systematic reviews and meta-analyses or Bayesian approaches in survey methodology contexts -- Dr. Bernd Weiß GESIS - Leibniz Institute for the Social Sciences Survey Design and Methodology | Team GESIS Panel Head of the GESIS Panel P.O. Box 12 21 55 | 68072 Mannheim | Germany Phone +49 621 1246 557 | Fax +49 621 1246 577 http://www.gesis.org<http://www.gesis.org/> | http://berndweiss.net/ -- Dr. Bernd Weiß GESIS - Leibniz Institute for the Social Sciences Survey Design and Methodology | Team GESIS Panel Head of the GESIS Panel P.O. Box 12 21 55 | 68072 Mannheim | Germany Phone +49 621 1246 557 | Fax +49 621 1246 577 http://www.gesis.org<http://www.gesis.org/> | http://berndweiss.net/