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Compilation of Meta Data. Presentation to OG6 Canberra, Australia May 2011. What is meta data?. Information used to describe other data Everything you need to know about a particular set of data in order to understand and use it
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Compilation of Meta Data Presentation to OG6 Canberra, Australia May 2011
What is meta data? • Information used to describe other data • Everything you need to know about a particular set of data in order to understand and use it • Information about concepts, definitions, collection, processing, methodology, quality, etc.
What is meta data used for? • To help the user: • To interpret, understand, analyse the data • To judge the quality of the data & the “fitness for use” • To transform statistical data into information • To facilitate comparability of data • To support data producers: • To retain and transfer knowledge • To promote harmonization between data sets • To improve collection
Meta data is an integral part of quality assurance • Elements of data quality: • Relevance • Accuracy • Timeliness • Accessibility • Coherence • Interpretability
General principles for documentation • Provide users with the information necessary to understand both strengths and weaknesses • Allow users to determine whether the data meet their needs • Should be clear, organized, accessible • Should be integrated wherever necessary to support the user’s understanding • Should be standardized, mandatory, updated as required
Defining meta data content • See IRES chapter 9 for a template • Handout: Excerpt of the Statistics Canada “Policy on informing users of data quality and methodology” • Handout: Example of meta data documentation for Canada’s “Industrial Consumption of Energy” survey • What are the minimum requirements?
Proposed meta data content (1) • Survey/Product name • Objectives of survey: • Why are the data collected? • Who are the intended users? • Timeframe • Frequency of collection? • Reference period? • Collection period?
Proposed meta data content (2) • Concepts and definitions • Target population • Survey universe/sampling frame • Classifications used • Collection method • Direct survey (sample/census; mandatory/voluntary) • Administrative data sources
Proposed meta data content (3) • For sample surveys: • Sample size, sampling error • Response rates • Imputation rates • For administrative data: • Sources • Purpose of original collection • Merits/shortcomings of data (coverage, conceptual) • Processing, correction, reliability, caveats
Proposed meta data content (4) • Error detection • Missing data, entry errors, validity problems, edits, reconciliation • Imputation of missing data • Disclosure control • Rules of confidentiality, confidentiality analysis • Revisions • Policy, explanation of changes
Proposed meta data content (5) • Description of analytical methods used • Seasonal adjustment, rounding • Other explanatory notes • Breaks in time series • Other supporting documents • Questionnaires, reporting guides, procedures manuals
Concluding comments • Documentation has often been the last work done and the first work to be dropped • But it is important on many levels • Needs to be maintained & updated; standards and templates help • In the future, new surveys or changes may be meta data driven – a growing role and importance • To support planning, development • To encourage harmonization, integration
For more information… Andy Kohut Director, Manufacturing & Energy Division Statistics Canada 11th Floor, Jean Talon Building, section B-8 Ottawa, Ontario CANADA K1A 0T6 613-951-5858 Andy.Kohut@statcan.gc.ca