Creating new Opportunities for Data Analysis? Using Data Fusion of Social Surveys to Expand Their Potential for Analysis ? Implications Based on Case-Studies from Austria
Sprache des Vortragstitels:
ESRA 23 - European Survey Research Association
Sprache des Tagungstitel:
Testing complex stochastic models via survey data often requires a large set of specific variables to operationalize theoretical constructs. This fact often limits the reuse potential of multi-topic social surveys, as they seldom include all the variables necessary for such an analysis. However, as survey research is challenged by e.g., rising costs, dropping response rates and limited resources to administer high quality studies, scientists are often unable to field research-question specific questionnaires as well.
Accordingly, our paper explores the potential of data fusion, based on multiple imputation, using shared variables from core modules to fuse datasets and donate missing information from one dataset to another.
In theory, the concept of fusing surveys, that feature shared core questions, as well as specialized elements, that are already designed with the idea of fusion, could help in creating data that allows for analysing complex models, going beyond the limitations (e.g. length and the related decision to focus on only a few topics or create a limited overview) found in singular surveys, omitting the problem of missing variables.
Yet two conditions must be met for the fusion to produce useful results.
(1.) The statistical model that is used for data fusion must have a certain explanatory power.
(2.) The assumption of local stochastic independence must hold. The variables Z shared between the datasets explain survey specific variables X of survey 1 and Y of survey 2.
The presentation will discuss different methods to fuse data, the assumptions above and their actual applications as well as limitations. Case studies include e.g. the Austrian presidential elections in 2016, were some relevant data was only present in the Austrian Social Survey (SSÖ) 2016, while additional key-variables were only available in the ESS of the same year.