Contextualizing Inter- & Multinational Survey Research. Discussing Regional Perspectives on Effects & Outcomes of Global Trends / Linear & Non-Linear (Multi-Level-)Modelling with Aggregate or Regional Data for Policy Analysis & Evidence Based Councelling
RC20 Comparative Sociology
WG02 Historical and Comparative Sociology
Contextualizing Inter- and Multinational Survey Research. Discussing Regional Perspectives on Effects and Outcomes of Global Trends
Technological advances and societal changes experienced in the last decade led to a number of new possibilities and challenges for survey research on a global scale. It becomes seemingly easier than ever before to organize and administer an international or at least multi-national research project.
But how are regional concepts and traditions affecting the design, implementation and outcome of global studies and how are global studies affecting these steps within the survey process used in regional studies? Using this as a starting point the session invites all researchers that are interested to participate in the discussion about the challenges survey research faces in such a setting. We want to specifically address the following points:
- How and at which points do the global trends in survey research and usage intersect with and/or influence established and often long-standing regional traditions and concepts?
- Which theoretical and methodical challenges emerge for regional survey research, as there is an evident shift towards more globalized perspective? How are those challenges met?
- How can the results from both global and regional survey research be put into context with each other? Which pitfalls and opportunities have to be addressed?
Contributions can both address theoretical ideas and concepts in regards to survey research as well as practical experiences of realized research projects. However, the proposed papers should indeed address how the global or regional scopes found in a project are reflected on in the methodological background and if necessary even put into relation with each other.
Linear and Non-Linear (Multi-Level-)Modelling with Aggregate or Regional Data for Policy Analysis and Evidence Based Counselling
In order to “struggle for a “better world”, policy implications and evidence-based counselling should refer to scientific results based on state-of-the-art theorizing and analysis. If – as is often the case for this specific kind of research – aggregated/regional data serve as the empirical basis, several methodological and statistical challenges occur.
The most common problems pertain to e.g. the identification and assessment of causal effects derived from aggregated/regional data, violations of conventional model assumptions, sample inherent problems or challenges of adequately modelling specific phenomena such as inequality.
Consequently, this session welcomes papers presenting problem identifications and solutions to such complexities arising from the nature of aggregate data in the context of policy analysis and evidence-based counselling. Each paper should refer to specific methodological or statistical aspects and may answer questions like the following:
- How can causal effects at different levels of analysis (including the individual level) be simultaneously identified when aggregate data are applied?
- How can multi-level designed studies contribute to comparative analysis aiming at the formulation of policy recommendations?
- How do we deal with sample inherent problems of aggregate/regional data, e.g. (extremely) small sample sizes or different contextual conditions?
- How can we measure complex social phenomena by indices and apply index-based results in ways that improvements in policy analysis can be achieved?
- How can we accurately account for specific characteristics of global social key phenomena such as inequality, corruption or democracy?
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