HaSpaD - Harmonizing and synthesizing partnership histories from different research data infrastructures
A model project for linking research data from various infrastructures
Leader: Dr. Sonja Schulz, stellv. Lisa Schmid, Dr. Bernd Weiß
Scientific unit: Data Archive for the Social Sciences (DAS), Survey Design and Methodology (SDM), Knowledge Technologies for the Social Sciences (WTS)
This research project intends to harmonize and cumulate survey-based, longitudinal data compiled from relationship biographies. By pooling these data sources, the project pursues two primary goals:
(1) Using all relevant German data sets available for secondary analyses, social science research syntheses will be performed that identify determinants of relationship events such as mate choice or separation and divorce. These data sets, though, can be characterized as heterogeneous since they differ with respect to data format, weighting scheme or measurements. In this project we will study if and how strong these heterogeneities impact the data harmonization and the research synthesis, and will provide solutions to overcome these issues. Additionally, we will address issues with research syntheses using non-experimental, survey-based data. Utilizing and improving contemporary research methods for data synthesis, this research project aims to further increase the importance and visibility of evidence-based social science research.
(2) Furthermore, the pooled data set will allow us and future researchers from a diverse set of disciplines (e.g., sociology, demography, psychology and epidemiology) to investigate previously unanswered questions regarding relationship stability from a historical and life-course perspective, for example concerning the reasons for the increase of divorce rates over the last decades and whether risk factors for separation have changed. Since the survey-based social sciences have little experience with this kind of data pooling and research synthesis, this projects intends to develop best practices for aggregating survey-based longitudinal data.
Runtime01.10.2017 – 30.09.2020
- Prof. Dr. Oliver Arránz Becker (University Halle-Wittenberg)
- Prof. Dr. Thomas Klein (Heidelberg University)
- Prof. Dr. Michael Wagner (University of Cologne)