The role of administrative data in the big data revolution in social science research
Open accessAbstract
The term big data is currently a buzzword in social science, however its precise meaning is ambiguous. In this paper we focus on administrative data which is a distinctive form of big data. Exciting new opportunities for social science research will be afforded by new administrative data resources, but these are currently under appreciated by the research community. The central aim of this paper is to discuss the challenges associated with administrative data. We emphasise that it is critical for researchers to carefully consider how administrative data has been produced. We conclude that administrative datasets have the potential to contribute to the development of high-quality and impactful social science research, and should not be overlooked in the emerging field of big data.
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Public-sector practice often uses aggregate administrative data that is poorly collected.
“Second, current practices of using aggregate administrative data that is often poorly collected [31,53,107] and biased to predict an individual's behavior is a complex and hard task that may lead to unfairness in decision-making outcomes and is, in most western, liberal democratic systems unconstitutional and/or illegal [123].”
From A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare (2021) · cites this paper as [31]
Section 6.4 (Data quality; page unavailable) says some administrative data contain errors and gives an example of information that was not collected diligently. It also says quality varies between systems. The paper does not establish that public-sector practice often uses aggregate administrative data that is poorly collected. (LLM, conf=0.85)
Experimental — accuracy not yet measuredNecessary predictive information may be unavailable or inconsistently available with contradicting factors.
“Moreover, the necessary information may not always be available or inconsistently available with contradicting factors [31].”
From A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare (2021) · cites this paper as [31]
Section 6.1 (Data analysis) says administrative datasets often lack background or auxiliary measures needed for some analyses. Section 6.4 (Data quality) says some measures may not be collected diligently and that errors vary across datasets and items. These passages support unavailable or inconsistent information, but do not specifically address predictive information or “contradicting factors.” No page number is available in our copy. (LLM, conf=0.82)
Experimental — accuracy not yet measured
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“Moreover, the necessary information may not always be available or inconsistently available with contradicting factors [31].”
Background
- Authors
- Connelly, R., Playford, C. J., Gayle, V., & Dibben, C.
- Affiliations
- University of Warwick (GB)
- University of Edinburgh (GB)
- Corresponding author
- Connelly, Roxanne
- Venue
- Social Science Research
- Publisher
- Elsevier BV
- Published
- 2016-09
- Volume
- 59
- Pages
- 1-12
- ISSN
- 0049-089X (ISSN-L)
- OpenAlex
- W2340865919
- Cited by
- 330
- References
- 4
- Citations by year
- 2026: 19 · 2025: 30 · 2024: 20 · 2023: 35 · 2022: 50 · 2021: 50 · 2020: 42 · 2019: 40 · 2018: 27 · 2017: 15 · 2016: 2
- FWCI
- 54.38
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Social Science Research (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Open Research Exeter (University of Exeter) (publishedVersion, cc-by)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (submittedVersion)
- Warwick Research Archive Portal (University of Warwick) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
- Funders
- Economic and Social Research Council (ES/L007487/1)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Social Sciences › Sociology and Political Science › Data Analysis and Archiving
- Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
- Keywords
- administrative data, big data, data quality, data production, data sources, research data
- MeSH terms
- Organizations, Social Sciences, Statistics as Topic
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