Research focus Data Quality

To ensure the credibility and integrity of social science data, the data needs to be of high quality. This is crucial because inaccurate, incomplete, or inconsistent data can have profound consequences for the inferences that are drawn, which can, in turn, undermine the validity of analyses and erode trust in communicated results. Consequently, any potential threats to the quality of social science data need to be identified, transparently reported, and ultimately diminished.

However, the current state of data quality practices in the social sciences is fragmented. This fragmentation stems from the diverse range of methodologies and theoretical frameworks in use across different social science disciplines, each with their own set of standards and norms. Additionally, the rapid evolution of new forms of data collection methods and data types has outpaced the development of unified data quality standards. Furthermore, the interdisciplinary nature of many social science projects complicates the establishment of a common ground for data quality assessments, as researchers from different fields may have differing priorities and perspectives on what constitutes ‘high-quality’ data.