Recommended Tabulations and Dissemination

1. Recommended Tabulations and Dissemination Section B

2. Quality Assurance In moving information from statistical programs into the hands of users we have to guard against the introduction of error. Quality assurance systems that minimize the possibility of errors are a necessary component of catalogue and data delivery systems.

3. Quality Assurance in Assembling and Delivering Data Products • Central or individual program management of assembly and delivery of data products • Each activity has specific quality considerations that need to be addressed

4. Assembling the Product • Formatting of tabulations run during the processing stage • Data review and verification procedures are absolutely necessary to maintain accuracy • Some programs can facilitate the assembly of tailored products • Where possible, it is best to use automated conversion of formats

5. Delivering the Product • Product delivery is a complex process requiring many quality control checks • Tabulations must undergo quality control checks, including electronic files for Internet products.

6. Quality Controls in Assembly and Delivery of Data Products • Accuracy • Timeliness • Accessibility • Interpretability • Coherence

7. Accuracy • Should be focused on preventing the introduction of error • Assess accuracy by keeping documentation of errors • If an error control system is in place, errors should be detected during production and delivery

8. Timeliness • Must include necessary approvals in any delivery estimates • Cannot expedite data delivery by skipping quality reviews without sacrificing quality • Assess timeliness by: • Comparing scheduled delivery with actual delivery date

9. Accessibility Data accessibility can be accomplished via: • Agency-wide catalogue system • Agency or program based delivery system • Proper management of catalogue and delivery systems • Constant improvement based on usage and user satisfaction feedback

10. Accessibility • Other points of access to newly available statistical data: • Official release mechanisms • Statistical agency web site • There are opportunities for partnerships with external public and private organizations, but must ensure: • Identification as the source of data remains visible • Where appropriate, encourage linkages back the original data sources held by the statistical agency

11. Pricing Policy Must strike a balance between freely accessible information and recovering costs, pricing policies can: • Promote accessibility • Provide information on relevance • Ensure balance of statistical resources

12. Improving Accessibility: Data Delivery Service Center • Manage customer calls • Publish product catalogues and updates • Special order procedures • Internet and bi-weekly newsletters • Prices, sells, and ships products • Maintains financial records

13. User Feedback Documenting and tracking user feedback can help improve delivery systems. Feedback data can be provided through: • usage statistics • Surveys of user satisfaction • Voluntary user feedback

14. Interpretability The information needed to understand statistical data must be written in simple terms and includes: • The concepts and classifications that underlie the data • The methodology used to collect and compile the data • Measures of data accuracy

15. Management of Interpretability To properly manage interpretability you need: • A policy on informing users of the basic information they need to interpret data • An integrated base of metadata that contains the information needed to describe each of the statistical agency’s data holdings • Direct interpretation and commentary on the data by the statistical organization

16. Assessing Interpretability • Measure compliance with metadata policies • Documentation and tracking of user feedback • Correct use and interpretation of data

17. Coherence Coherence is the degree that data is comparable between different data items, different points in time, and internationally coherent. It requires: • Development and use of standard frameworks: • Concepts • Variables • Classifications

18. Coherence The process of measurement should not introduce inconsistency between data when the quantities being measured are designed in a consistent way. • The use of a common sampling frames • The use of commonly formulated questions • The use of common methodology and systems

19. Coherence The third step in building coherence into your data involves the comparison and integration of data from different sources. Including: • Regular and routine integration activities • National accounts, for example • Exploratory and ad hoc data integration • Historical revisions of data, for example

20. Assessing Coherence Three broad measures can help you assess the coherence of your data - the documentation, monitoring and analysis of: • The existence and use of standard frameworks • Common survey tools and methodologies • Inconsistencies in published data

21. Planning Guidelines for Data Delivery • Build in time for final review • Check tabulations and major findings • Enforce sign-off procedures for releases • Provide description of survey methods with data • Procedures to inform users of revisions • Use only supported software to put files on the Internet • Review all releases for technical accuracy

22. Section B Quiz • What is the main goal of quality assurance activities during data delivery? • What five quality elements should be considered during data delivery? • Should user feedback be captured and analyzed? • What are some advantage of pricing policies beyond cost recovery?