Quality assurance

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  • 1. Income Research Paper Series
    Journals and periodicals: 75F0002M
    Description: This series provides detailed documentation on income developments, including survey design issues, data quality evaluation and exploratory research.
    Release date: 2024-04-26
  • 2. Census of Agriculture: Data quality report
    Surveys and statistical programs – Documentation: 32-26-0007
    Description: Census of Agriculture data provide statistical information on farms and farm operators at fine geographic levels and for small subpopulations. Quality evaluation activities are essential to ensure that census data are reliable and that they meet user needs.

    This report provides data quality information pertaining to the Census of Agriculture, such as sources of error, error detection, disclosure control methods, data quality indicators, response rates and collection rates.
    Release date: 2024-02-06
  • 3. Distributions of Household Economic Accounts, estimates of asset, liability and net worth distributions, 2010 to 2023, technical methodology and quality report
    Articles and reports: 13-604-M2024001
    Description: This documentation outlines the methodology used to develop the Distributions of household economic accounts published in January 2024 for the reference years 2010 to 2023. It describes the framework and the steps implemented to produce distributional information aligned with the National Balance Sheet Accounts and other national accounts concepts. It also includes a report on the quality of the estimated distributions.
    Release date: 2024-01-22
  • 4. Distributions of Household Economic Accounts, estimates of asset, liability and net worth distributions, 2010 to 2022, technical methodology and quality report
    Articles and reports: 13-604-M2023001
    Description: This documentation outlines the methodology used to develop the Distributions of household economic accounts published in March 2023 for the reference years 2010 to 2022. It describes the framework and the steps implemented to produce distributional information aligned with the National Balance Sheet Accounts and other national accounts concepts. It also includes a report on the quality of the estimated distributions.
    Release date: 2023-03-31
  • 5. Distributions of Household Economic Accounts, estimates of asset, liability and net worth distributions, 2010 to 2021, technical methodology and quality report
    Articles and reports: 13-604-M2022002
    Description:

    This documentation outlines the methodology used to develop the Distributions of household economic accounts published in August 2022 for the reference years 2010 to 2021. It describes the framework and the steps implemented to produce distributional information aligned with the National Balance Sheet Accounts and other national accounts concepts. It also includes a report on the quality of the estimated distributions.

    Release date: 2022-08-03
  • 6. The Importance of Data Quality
    19-22-0009
    Description:

    Join us as Statistics Canada’s Quality Secretariat will give a presentation on the importance of data quality. We are living in an exciting time for data: sources are more abundant, they are being generated in innovative ways, and they are available quicker than ever. However, a data source is not only worthless if it does not meet basic quality standards – it can be misleading, and worse than having no data at all! Statistics Canada’s Quality Secretariat has a mandate to promote good quality practices within the agency, across the Government of Canada, and internationally. For quality to truly be present, it must be incorporated into each process (from design to analysis) and into the product itself – whether that product is a microdata file or estimates derived from it. We will address why data quality is important and how one can evaluate it in practice. We will cover some basic concepts in data quality (quality assurance vs. control, metadata, etc.), and present data quality as a multidimensional concept. Finally, we will show data quality in action by evaluating a data source together. All data quality literacy levels are welcome. After all, everybody plays a part in quality!

    https://www.statcan.gc.ca/en/services/webinars/19220009

    Release date: 2022-01-26
  • 7. Creation of a composite quality indicator for administrative data-based estimates using clustering Archived
    Articles and reports: 11-522-X202100100015
    Description: National statistical agencies such as Statistics Canada have a responsibility to convey the quality of statistical information to users. The methods traditionally used to do this are based on measures of sampling error. As a result, they are not adapted to the estimates produced using administrative data, for which the main sources of error are not due to sampling. A more suitable approach to reporting the quality of estimates presented in a multidimensional table is described in this paper. Quality indicators were derived for various post-acquisition processing steps, such as linkage, geocoding and imputation, by estimation domain. A clustering algorithm was then used to combine domains with similar quality levels for a given estimate. Ratings to inform users of the relative quality of estimates across domains were assigned to the groups created. This indicator, called the composite quality indicator (CQI), was developed and experimented with in the Canadian Housing Statistics Program (CHSP), which aims to produce official statistics on the residential housing sector in Canada using multiple administrative data sources.

    Keywords: Unsupervised machine learning, quality assurance, administrative data, data integration, clustering.

    Release date: 2021-10-22
  • 8. Empowering analysts to consider the ethics of their work: A case study of the UK Statistics Authority's data ethics framework Archived
    Articles and reports: 11-522-X202100100023
    Description:

    Our increasingly digital society provides multiple opportunities to maximise our use of data for the public good – using a range of sources, data types and technologies to enable us to better inform the public about social and economic matters and contribute to the effective development and evaluation of public policy. Ensuring use of data in ethically appropriate ways is an important enabler for realising the potential to use data for public good research and statistics. Earlier this year the UK Statistics Authority launched the Centre for Applied Data Ethics to provide applied data ethics services, advice, training and guidance to the analytical community across the United Kingdom. The Centre has developed a framework and portfolio of services to empower analysts to consider the ethics of their research quickly and easily, at the research design phase thus promoting a culture of ethics by design. This paper will provide an overview of this framework, the accompanying user support services and the impact of this work.

    Key words: Data ethics, data, research and statistics

    Release date: 2021-10-22
  • 9. Distributions of Household Economic Accounts, estimates of asset, liability and net worth distributions, 2010 to 2020, technical methodology and quality report
    Articles and reports: 13-604-M2021001
    Description:

    This documentation outlines the methodology used to develop the Distributions of household economic accounts published in September 2021 for the reference years 2010 to 2020. It describes the framework and the steps implemented to produce distributional information aligned with the National Balance Sheet Accounts and other national accounts concepts. It also includes a report on the quality of the estimated distributions.

    Release date: 2021-09-07
  • 10. Data Quality in Six Dimensions Archived
    Stats in brief: 89-20-00062020001
    Description:

    In this video, you will be introduced to the fundamentals of data quality, which can be summed up in six dimensions—or six different ways to think about quality. You will also learn how each dimension can be used to evaluate the quality of data.

    Release date: 2020-09-23
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Analysis (171)

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  • 1. Income Research Paper Series
    Journals and periodicals: 75F0002M
    Description: This series provides detailed documentation on income developments, including survey design issues, data quality evaluation and exploratory research.
    Release date: 2024-04-26
  • 2. Distributions of Household Economic Accounts, estimates of asset, liability and net worth distributions, 2010 to 2023, technical methodology and quality report
    Articles and reports: 13-604-M2024001
    Description: This documentation outlines the methodology used to develop the Distributions of household economic accounts published in January 2024 for the reference years 2010 to 2023. It describes the framework and the steps implemented to produce distributional information aligned with the National Balance Sheet Accounts and other national accounts concepts. It also includes a report on the quality of the estimated distributions.
    Release date: 2024-01-22
  • 3. Distributions of Household Economic Accounts, estimates of asset, liability and net worth distributions, 2010 to 2022, technical methodology and quality report
    Articles and reports: 13-604-M2023001
    Description: This documentation outlines the methodology used to develop the Distributions of household economic accounts published in March 2023 for the reference years 2010 to 2022. It describes the framework and the steps implemented to produce distributional information aligned with the National Balance Sheet Accounts and other national accounts concepts. It also includes a report on the quality of the estimated distributions.
    Release date: 2023-03-31
  • 4. Distributions of Household Economic Accounts, estimates of asset, liability and net worth distributions, 2010 to 2021, technical methodology and quality report
    Articles and reports: 13-604-M2022002
    Description:

    This documentation outlines the methodology used to develop the Distributions of household economic accounts published in August 2022 for the reference years 2010 to 2021. It describes the framework and the steps implemented to produce distributional information aligned with the National Balance Sheet Accounts and other national accounts concepts. It also includes a report on the quality of the estimated distributions.

    Release date: 2022-08-03
  • 5. Creation of a composite quality indicator for administrative data-based estimates using clustering Archived
    Articles and reports: 11-522-X202100100015
    Description: National statistical agencies such as Statistics Canada have a responsibility to convey the quality of statistical information to users. The methods traditionally used to do this are based on measures of sampling error. As a result, they are not adapted to the estimates produced using administrative data, for which the main sources of error are not due to sampling. A more suitable approach to reporting the quality of estimates presented in a multidimensional table is described in this paper. Quality indicators were derived for various post-acquisition processing steps, such as linkage, geocoding and imputation, by estimation domain. A clustering algorithm was then used to combine domains with similar quality levels for a given estimate. Ratings to inform users of the relative quality of estimates across domains were assigned to the groups created. This indicator, called the composite quality indicator (CQI), was developed and experimented with in the Canadian Housing Statistics Program (CHSP), which aims to produce official statistics on the residential housing sector in Canada using multiple administrative data sources.

    Keywords: Unsupervised machine learning, quality assurance, administrative data, data integration, clustering.

    Release date: 2021-10-22
  • 6. Empowering analysts to consider the ethics of their work: A case study of the UK Statistics Authority's data ethics framework Archived
    Articles and reports: 11-522-X202100100023
    Description:

    Our increasingly digital society provides multiple opportunities to maximise our use of data for the public good – using a range of sources, data types and technologies to enable us to better inform the public about social and economic matters and contribute to the effective development and evaluation of public policy. Ensuring use of data in ethically appropriate ways is an important enabler for realising the potential to use data for public good research and statistics. Earlier this year the UK Statistics Authority launched the Centre for Applied Data Ethics to provide applied data ethics services, advice, training and guidance to the analytical community across the United Kingdom. The Centre has developed a framework and portfolio of services to empower analysts to consider the ethics of their research quickly and easily, at the research design phase thus promoting a culture of ethics by design. This paper will provide an overview of this framework, the accompanying user support services and the impact of this work.

    Key words: Data ethics, data, research and statistics

    Release date: 2021-10-22
  • 7. Distributions of Household Economic Accounts, estimates of asset, liability and net worth distributions, 2010 to 2020, technical methodology and quality report
    Articles and reports: 13-604-M2021001
    Description:

    This documentation outlines the methodology used to develop the Distributions of household economic accounts published in September 2021 for the reference years 2010 to 2020. It describes the framework and the steps implemented to produce distributional information aligned with the National Balance Sheet Accounts and other national accounts concepts. It also includes a report on the quality of the estimated distributions.

    Release date: 2021-09-07
  • 8. Data Quality in Six Dimensions Archived
    Stats in brief: 89-20-00062020001
    Description:

    In this video, you will be introduced to the fundamentals of data quality, which can be summed up in six dimensions—or six different ways to think about quality. You will also learn how each dimension can be used to evaluate the quality of data.

    Release date: 2020-09-23
  • 9. Data Accuracy and Validation: Methods to ensure the quality of data Archived
    Stats in brief: 89-20-00062020008
    Description:

    Accuracy is one of the six dimensions of Data Quality used at Statistics Canada.   Accuracy refers to how well the data reflects the truth or what actually happened.   In this video we will present methods to describe accuracy in terms of validity and correctness. We will also discuss methods to validate and check the accuracy of data values.

    Release date: 2020-09-23
  • 10. Distributions of Household Economic Accounts, estimates of asset, liability and net worth distributions, 2010 to 2019, technical methodology and quality report
    Articles and reports: 13-604-M2020002
    Description:

    This documentation outlines the methodology used to develop the Distributions of household economic accounts published in June 2020 for the reference years 2010 to 2019. It describes the framework and the steps implemented to produce distributional information aligned with the National balance sheet accounts and other national accounts concepts. It also includes a report on the quality of the estimated distributions.

    Release date: 2020-06-26
Reference (78)

Reference (78) (20 to 30 of 78 results)

  • 21. A Study of Data Revisions to the National Tourism Indicators Archived
    Surveys and statistical programs – Documentation: 13-604-M2004045
    Description:

    How "good" are the National Tourism Indicators (NTI)? How can their quality be measured? This study looks to answer these questions by analysing the revisions to the NTI estimates for the period 1997 through 2001.

    Release date: 2004-10-25
  • 22. Survey of Household Spending 2002: Data Quality Indicators Archived
    Surveys and statistical programs – Documentation: 62F0026M2004001
    Description:

    This report describes the quality indicators produced for the 2002 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.

    Release date: 2004-09-15
  • 23. Occupation, 2001 Census Technical Report (Reference Products: 2001 Census) Archived
    Surveys and statistical programs – Documentation: 92-388-X
    Description:

    This report contains basic conceptual and data quality information to help users interpret and make use of census occupation data. It gives an overview of the collection, coding (to the 2001 National Occupational Classification), edit and imputation of the occupation data from the 2001 Census. The report describes procedural changes between the 2001 and earlier censuses, and provides an analysis of the quality level of the 2001 Census occupation data. Finally, it details the revision of the 1991 Standard Occupational Classification used in the 1991 and 1996 Censuses to the 2001 National Occupational Classification for Statistics used in 2001. The historical comparability of data coded to the two classifications is discussed. Appendices to the report include a table showing historical data for the 1991, 1996 and 2001 Censuses.

    Release date: 2004-07-15
  • 24. Industry (Based on 1980 Standard Industrial Classification [SIC]), 2001 Census Technical Report (Reference Products: 2001 Census) Archived
    Surveys and statistical programs – Documentation: 92-391-X
    Description:

    This report contains basic conceptual and data quality information intended to facilitate the use and interpretation of census industry data. It provides an overview of the industry processing cycle, including elements such as regional processing, edit and imputation, and the tabulation of error rates. A detailed explanation of the automated coding systems used in the 2001 Census is also documented, in addition to notable changes in the imputation procedures. The report concludes with summary tables that indicate the level of data quality in the 2001 Census industry data. Appendices to the report contain historical data going back to the 1971 Census.

    Release date: 2004-06-02
  • 25. Industry (Based on 1997 North American Industry Classification System [NAICS]), 2001 Census Technical Report (Reference Products: 2001 Census) Archived
    Surveys and statistical programs – Documentation: 92-389-X
    Description:

    This report contains basic conceptual and data quality information intended to facilitate the use and interpretation of census industry data. It provides an overview of the industry processing cycle, including elements such as regional processing, edit and imputation, and the tabulation of error rates. Notable changes in the industrial classification structure are discussed as well as differences in the coding procedures from the previous census (1996). The report concludes with summary tables that indicate the level of data quality in the 2001 Census industry data.

    Release date: 2004-05-04
  • 26. Class of Worker, 2001 Census Technical Report (Reference Products: 2001 Census) Archived
    Surveys and statistical programs – Documentation: 92-398-X
    Description:

    This report contains basic conceptual and data quality information intended to facilitate the use and interpretation of census class of worker data. It provides an overview of the class of worker processing cycle including elements such as regional office processing, and edit and imputation. The report concludes with summary tables that indicate the level of data quality in the 2001 Census class of worker data.

    Release date: 2004-04-22
  • 27. A study of data revisions to the National Tourism Indicators Archived
    Surveys and statistical programs – Documentation: 13-009-X20030046842
    Description:

    How good are the National Tourism Indicators (NTI)? How can their quality be measured? This study looks to answer these questions through analysis of the revisions to the NTI estimates for the period 1997 through 2001.

    Release date: 2004-03-30
  • 28. Dwellings, Households and Shelter Costs, 2001 Census Technical Report (Reference Products: 2001 Census) Archived
    Surveys and statistical programs – Documentation: 92-382-X
    Description:

    This report discusses data quality pertaining to household variables (tenure, household maintainer, owner's major payments and gross rent) and dwelling characteristics (structural type of dwelling, number of rooms, number of bedrooms, period of construction, condition of dwelling and value of dwelling). The report also describes the various aspects of data processing that could impact data quality.

    Release date: 2003-12-18
  • 29. Survey of Household Spending 2001 - Data Quality Indicators Archived
    Surveys and statistical programs – Documentation: 62F0026M2003001
    Description:

    This report describes the quality indicators produced for the 2001 Survey of Household Spending. It covers the usual quality indicators that help users interpret the data, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates.

    Release date: 2003-11-26
  • 30. The EU Labour Force Survey : on the way to convergence and quality Archived
    Surveys and statistical programs – Documentation: 11-522-X20010016225
    Description:

    The European Union Labour Forces Survey (LFS) is based on national surveys that were originally very different. For the past decade, under pressure from increasingly demanding users (particularly with respect to timeliness, comparability and flexibility), the LFS has been subjected to a constant process of quality improvement.

    The following topics are presented in this paper:A. the quality improvement process, which comprises screening national survey methods, target structure, legal foundations, quality reports, more accurate and more explicit definitions of components, etc.;B. expected or achieved results, which include an ongoing survey producing quarterly results within reasonable time frames, comparable employment and unemployment rates over time and space in more than 25 countries, specific information on current political topics, etc.;C. continuing shortcomings, such as implementation delays in certain countries, possibilities of longitudinal analysis, public access to microdata, etc.; D. future tasks envisioned, such as adaptation of the list of ISCO and ISCED variables and nomenclatures (to take into account evolution in employment and teaching methods), differential treatment of structural variables and increased recourse to administrative files (to limit respondent burden), harmonization of questionnaires, etc.

    Release date: 2002-09-12
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