Transcript Compilation of Metadata, Statistics Canada

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.
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What is meta data used for?
 To help the user:
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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
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Meta data is an integral part of
quality assurance
 Elements of data quality:
 Relevance
 Accuracy
 Timeliness
 Accessibility
 Coherence
 Interpretability
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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
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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?
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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?
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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
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Proposed meta data content (3)
 For sample surveys:
• Sample size, sampling error
• Response rates
• Imputation rates
 For administrative data:
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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
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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
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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
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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
[email protected]
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