Compilation of Meta Data Presentation to OG6 Canberra, Australia May 2011

2 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.

3 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

4 Meta data is an integral part of quality assurance  Elements of data quality:  Relevance  Accuracy  Timeliness  Accessibility  Coherence  Interpretability

5 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

6 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?

7 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?

8 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

9 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

10 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

11 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

12 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

13 For more information… Andy Kohut Director, Manufacturing & Energy Division Statistics Canada 11 th Floor, Jean Talon Building, section B-8 Ottawa, Ontario CANADA K1A 0T