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: 2024-02-22
  • 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)

Analysis (171) (0 to 10 of 171 results)

  • 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: 2024-02-22
  • 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) (10 to 20 of 78 results)

  • 11. Survey of Household Spending 2009: Data Quality Indicators Archived
    Surveys and statistical programs – Documentation: 62F0026M2011001
    Description:

    This report describes the quality indicators produced for the 2009 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: 2011-06-16
  • 12. Survey of Household Spending 2007: Data Quality Indicators Archived
    Surveys and statistical programs – Documentation: 62F0026M2010004
    Description:

    This report describes the quality indicators produced for the 2007 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: 2010-12-13
  • 13. Survey of Household Spending 2008: Data Quality Indicators Archived
    Surveys and statistical programs – Documentation: 62F0026M2010005
    Description:

    This report describes the quality indicators produced for the 2008 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: 2010-12-13
  • 14. Survey of Household Spending 2004: Data Quality Indicators Archived
    Surveys and statistical programs – Documentation: 62F0026M2010001
    Description:

    This report describes the quality indicators produced for the 2004 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: 2010-04-26
  • 15. Survey of Household Spending 2005: Data Quality Indicators Archived
    Surveys and statistical programs – Documentation: 62F0026M2010002
    Description:

    This report describes the quality indicators produced for the 2005 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: 2010-04-26
  • 16. Survey of Household Spending 2006: Data Quality Indicators Archived
    Surveys and statistical programs – Documentation: 62F0026M2010003
    Description:

    This report describes the quality indicators produced for the 2006 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: 2010-04-26
  • 17. Data Quality for the 2006 Survey of Labour and Income Dynamics (SLID) Archived
    Surveys and statistical programs – Documentation: 75F0002M2008005
    Description:

    The Survey of Labour and Income Dynamics (SLID) is a longitudinal survey initiated in 1993. The survey was designed to measure changes in the economic well-being of Canadians as well as the factors affecting these changes. Sample surveys are subject to sampling errors. In order to consider these errors, each estimates presented in the "Income Trends in Canada" series comes with a quality indicator based on the coefficient of variation. However, other factors must also be considered to make sure data are properly used. Statistics Canada puts considerable time and effort to control errors at every stage of the survey and to maximise the fitness for use. Nevertheless, the survey design and the data processing could restrict the fitness for use. It is the policy at Statistics Canada to furnish users with measures of data quality so that the user is able to interpret the data properly. This report summarizes the set of quality measures of SLID data. Among the measures included in the report are sample composition and attrition rates, sampling errors, coverage errors in the form of slippage rates, response rates, tax permission and tax linkage rates, and imputation rates.

    Release date: 2008-08-20
  • 18. Quality Assurance Review: Summary Report Archived
    Surveys and statistical programs – Documentation: 12-594-X
    Description:

    This Summary Report provides an overview of the findings of a Quality Assurance Review that was conducted for nine key statistical programs during the period September 2006 to February 2007. The review was commissioned by Statistics Canada's Policy Committee in order to assess the soundness of quality assurance processes for these nine programs and to propose improvements where needed. The Summary Report describes the principal themes that recur frequently throughout these programs, as well as providing guidance for future reviews of this type.

    Release date: 2007-06-20
  • 19. Survey of Household Spending 2003: Data Quality Indicators Archived
    Surveys and statistical programs – Documentation: 62F0026M2005006
    Description:

    This report describes the quality indicators produced for the 2003 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: 2005-10-06
  • 20. Unpaid Work, 2001 Census Technical Report (Reference Products: 2001 Census) Archived
    Surveys and statistical programs – Documentation: 92-397-X
    Description:

    This report covers concepts and definitions, the imputation method and data quality for this variable. The 2001 Census collected information on three types of unpaid work performed during the week preceding the Census: looking after children, housework and caring for seniors. The 2001 data on unpaid work are compared with the 1996 Census data and with the data from the General Social Survey (use of time in 1998). The report also includes historical tables.

    Release date: 2005-01-11
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