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One Size Doesn’t Fit All: Administrative Data Quality Frameworks for Production of Official Statistics
Author(s) -
Sara Correia,
Jack Sim
Publication year - 2019
Publication title -
international journal of population data science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.602
H-Index - 7
ISSN - 2399-4908
DOI - 10.23889/ijpds.v4i3.1288
Subject(s) - quality (philosophy) , respondent , computer science , data quality , quality assurance , official statistics , production (economics) , presentation (obstetrics) , data science , statistics , business , marketing , mathematics , economics , service (business) , political science , medicine , philosophy , epistemology , radiology , law , macroeconomics
Background with rationaleThe use of administrative data is key to achieving the UK Statistics Authority’s strategy of Better Statistics, Better Decisions. Integrating administrative data into official statistics can benefit policy decisions by allowing the possibility of greater granularity and improved timeliness in outputs, while delivering efficiency gains and reducing respondent burden. Quality assessment and communicating uncertainty of administrative data sources is critical to their effective integration into official statistical outputs. Main AimThis presentation will discuss the main challenges of quality assuring statistical outputs containing administrative data. The differences in existing quality frameworks and identified quality metrics will be discussed. In addition, the presentation will cover the need to tailor quality assessment to answer a specific research question that an identified source is being used for and the considerations required. Methods/ApproachA comprehensive literature review was carried out, bringing together existing quality frameworks and metrics from National Statistical Institutes (NSIs) and academia for production of statistics using administrative data sources. ResultsThe main challenges and considerations faced when quality assuring outputs produced using administrative sources have been identified. The quality requirements for different outputs across social, business and census statistics were summarised and a general quality framework for admin data developed. This framework draws on international best practices for use in the UK statistical system. ConclusionIntegrating administrative data presents challenges can’t be solved by a one-size fits all framework. Through unifying available guidance, an adaptable quality assurance methodology has been created, enabling the use of public data for the public good.

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