This app provides tools to explore data quality frameworks for social science research. It features an interactive Decision Tree and an Evidence Gap Map to help you filter and review frameworks based on fine-grained criteria of your data use case and assessment needs. More information on the development and the context of the tools is available from the corresponding GESIS Guide to DBD.
This collection of data quality frameworks is based on a systematic review outlined in Daikeler et al. (2024). This study was driven by the growing use of digital behavioral data alongside traditional sources, such as survey data, in social science research, which presents various challenges related to data quality. To guide researchers, Daikeler et al. conducted a systematic literature review, identifying 58 frameworks that address data quality issues. In addition to a comprehensive discussion of the general challenges associated with the data quality of these new data types, the paper introduces two tools:
The Decision Tree serves as an initial guide to select appropriate data quality frameworks for different use cases. Decision trees are commonly utilized to facilitate decision-making in complex and high-dimensional scenarios. The one presented here enables you to choose frameworks that best suit your specific research problem. The Decision Tree filters the frameworks along three dimensions:
Once filtered, you can download selected frameworks as a BibTeX file.
The Evidence Gap Map serves as a detailed guide to the error sources and data types targeted by the (intrinsic) data quality frameworks. It displays the selected data types on the x-axis, mapping them against the selected error types for social science data on the y-axis. The size of the bubble represents how many frameworks include the respective error source by data type. The errors covered are:
Larger circles mean more publications on the topic. Hover over the bubbles to see the exact counts.