The Journal of Survey Statistics and Methodology (JSSAM) seeks submissions for a special issue on recent advances in data integration. Submissions to the special issue are welcomed through March 31, 2022. Upon submission, manuscripts will be peer-reviewed in accordance with standard journal practice and will be published online soon after acceptance.
The availability of both structured and unstructured databases, such as electronic health data, social media data, patent databases, and surveys that are often updated in real time, among others, has grown rapidly over the past decade. With this expansion in data, the statistical and methodological questions around data integration, or rather merging multiple data sources, has also grown. This special issue highlights innovative approaches in data integration and related topics. Growth in data integration has led to the creation and innovation of linking records at the micro-level, small area models using multiple data sources, blending probability and nonprobability sources, and in many other areas. In addition, this area of research has led to creation and innovation of end-to-end procedures and products-from study conceptualization to estimation-that have been applied on real data sources. Specifically, combining multiple data sources includes record linkage tasks that are deterministic or stochastic. It also includes combining data that have harmonized features/variables. In addition, the data can either be at the individual level or at an aggregate level. For example, entity resolution (or record linkage, de-duplication, data linkage, statistical matching) has become widely used in survey statistics and methodology. Furthermore, recent work has been done on harmonization and estimation when combining multiple data sources.
As combining data sources becomes more prevalent, record linkage/entity resolution, in particular, continues to pose privacy and ethical challenges. Obtaining informed consent from units for access to and linkage of their data from non-survey sources continues to be challenging. Additional risks related to disclosing the identities of linked units in anonymized data sources also arise. Further, missing data problems are inherent in blended, combined, and linked data, necessitating tools to overcome information loss and potential bias.
This special issue invites standard research articles and brief research notes for both the statistics and the survey methods sections of the journal. Topics include but are not limited to:
- Developments in new statistical models and methods for combining, linking, or blending multiple data sources.
- Linking multiple structured and/or unstructured data sources.
- Novel applications, case studies, and/or methodology that merge more than two data sources.
- Innovative methods of obtaining informed linkage consent and reducing non-consent bias.
- Evaluation of data collection and survey processing methods needed to facilitate combining, linking, or blending multiple data sources.
- Development of quality indicators for blended, combined, or linked data sources.
- Practical approaches that preserve the privacy and confidentiality of blended, combined, or linked data.
- Sharing or linking data across multiple agencies with data sharing restrictions.
Electronic copies of the manuscripts should be uploaded here following the manuscript preparation instructions. To ensure consideration in the special issue, authors must include a cover letter that clearly states that the manuscript has been submitted for consideration for the special issue on recent advances in data integration. Queries about this special issue should be directed to Guest Editors Joe Sakshaug (joesaks@umich.edu) or Beka Steorts (rebecca.steorts@duke.edu).
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Jenny Thompson
U.S. Census Bureau
Co-Editor, Journal of Survey Statistics and Methodology
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