Quilting a Time-Place Mosaic: Concluding Remarks
Abstract
Social science is at a pivotal moment. The advent of “big data” from the Internet, social media, and smartphones as well as newly available administrative data from electronic sources has opened the door to new understandings of people and society. That said, realizing this promise requires a vision for the future and a practical plan for reaching it. The articles in this volume begin this work. Each addresses some aspect of data linkage. Each can be considered a patch in a time-place mosaic. This concluding article considers the articles as a collection and how they might be quilted together. It discusses the diversity of sources available, differences in time depth and sociospatial coverage, and the many challenges of using data not designed for research. It identifies the benefits that would cumulate as a set of regional data centers to assemble, link, curate, and share diverse data sources is established and coordinated. These data centers could provide the combination of national coverage, local depth, and temporal precision needed to advance our understanding of the American population.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Recent scientific advances include an expanded array of methods and measurement tools.
“advances in the array of methods and measurement tools now available (e.g., Entwisle, Hofferth, & Moran, 2017;van der Maas & Molenaar, 1992)”
From Malleability, plasticity, and individuality: How children learn and develop in context<sup>1</sup> (2019) · cites this paper as Entwisle, Hofferth, & Moran, 2017
Cited by
Sorted by citations
Malleability, plasticity, and individuality: How children learn and develop in context<sup>1</sup>
Cantor, P., Osher, D., Berg, J., Steyer, L., & Rose, T. (2019) · Applied Developmental Science · doi:10.1080/10888691.2017.1398649 · Cited by 328 · see citing works
- Authors
- Entwisle, B., Hofferth, S. L., & Moran, E. F.
- Affiliations
- University of Maryland, College Park (US)
- Corresponding author
- Entwisle, Barbara
- Venue
- The ANNALS of the American Academy of Political and Social Science
- Publisher
- SAGE Publications
- Published
- 2016-12-20
- Volume
- 669
- Issue
- 1
- Pages
- 190-198
- ISSN
- 0002-7162 (ISSN-L)
- 1552-3349
- OpenAlex
- W2570863536
- Cited by
- 1
- References
- 0
- Citations by year
- 2018: 1
- FWCI
- 0.37
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Annals of the American Academy of Political and Social Science (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
- Social Sciences › Sociology and Political Science › Data Analysis and Archiving
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Keywords
- data linkage, big data, administrative data, social media data, data curation, data sharing, United States population, temporal precision
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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