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)

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: 2024-04-26
  • 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) (30 to 40 of 78 results)

  • 31. The unified enterprise survey - its approach to quality Archived
    Surveys and statistical programs – Documentation: 11-522-X20010016229
    Description:

    This paper discusses the approach that Statistics Canada has taken to improve the quality of annual business surveys through their integration in the Unified Enterprise Survey (UES). The primary objective of the UES is to measure the final annual sales of goods and services accurately by province, in sufficient detail and in a timely manner.

    This paper describes the methodological approaches that the UES has used to improve financial and commodity data quality in four broad areas. These include improved coherence of the data collected from different levels of the enterprise, better coverage of industries, better depth of information (in the sense of more content detail and estimates for more detailed domains) and better consistency of the concepts and methods across industries.

    The approach, in achieving quality, has been to (a) establish a base measure of the quality of the business survey program prior to the UES, (b) measure the annual data quality of the UES, and (c) carry out specific studies to better understand the quality of UES data and methods.

    Release date: 2002-09-12
  • 32. 2000 Survey of Household Spending - Data Quality Indicators Archived
    Surveys and statistical programs – Documentation: 62F0026M2002001
    Description:

    This report describes the quality indicators produced for the 2000 Survey of Household Spending. It covers the usual quality indicators that help users interpret the data, such as coefficients of variation, non-response rates, slippage rates and imputation rates.

    Release date: 2002-06-28
  • 33. 1998 Survey of Household Spending Data Quality Indicators Archived
    Surveys and statistical programs – Documentation: 62F0026M2001001
    Description:

    This report describes the quality indicators produced for the 1998 Survey of Household Spending. It covers the usual quality indicators that help users interpret data, such as coefficients of variation, nonresponse rates, imputation rates and the impact of imputed data on the estimates. Added to these are various less often used indicators such as slippage rates and measures of the representativity of the sample for particular characteristics that are useful for evaluating the survey methodology.

    Release date: 2001-10-15
  • 34. 1999 Survey of Household Spending Data Quality Indicators Archived
    Surveys and statistical programs – Documentation: 62F0026M2001002
    Description:

    This report describes the quality indicators produced for the 1999 Survey of Household Spending. It covers the usual quality indicators that help users interpret data, such as coefficients of variation, nonresponse rates, imputation rates and the impact of imputed data on the estimates. Added to these are various less often used indicators such as slippage rates and measures of the representativity of the sample for particular characteristics that are useful for evaluating the survey methodology.

    Release date: 2001-10-15
  • 35. Combining census, survey, demographic and administrative data to produce a one number census Archived
    Surveys and statistical programs – Documentation: 11-522-X19990015638
    Description:

    The focus of Symposium'99 is on techniques and methods for combining data from different sources and on analysis of the resulting data sets. In this talk we illustrate the usefulness of taking such an "integrating" approach when tackling a complex statistical problem. The problem itself is easily described - it is how to approximate, as closely as possible, a "perfect census", and in particular, how to obtain census counts that are "free" of underenumeration. Typically, underenumeration is estimated by carrying out a post enumeration survey (PES) following the census. In the UK in 1991 the PEF failed to identify the full size of the underenumeration and so demographic methods were used to estimate the extent of the undercount. The problems with the "traditional" PES approach in 1991 resulted in a joint research project between the Office for National Statistics and the Department of Social Statistics at the University of Southampton aimed at developing a methodology which will allow a "One Number Census" in the UK in 2001. That is, underenumeration will be accounted for not just at high levels of aggregation, but right down to the lowest levels at which census tabulations are produced. In this way all census outputs will be internally consistent, adding to the national population estimates. The basis of this methodology is the integration of information from a number of data sources in order to achieve this "One Number".

    Release date: 2000-03-02
  • 36. Statistical processing in the next millennium Archived
    Surveys and statistical programs – Documentation: 11-522-X19990015640
    Description:

    This paper states how SN is preparing for a new era in the making of statistics, as it is triggered by technological and methodological developments. An essential feature of the turn to the new era is the farewell to the stovepipe way of data processing. The paper discusses how new technological and methodological tools will affect processes and their organization. Special emphasis is put on one of the major chances and challenges the new tools offer: establishing coherence in the content of statistics and in the presentation to users.

    Release date: 2000-03-02
  • 37. Combining administrative data with survey data: experience in the Australian survey of employment and unemployment patterns Archived
    Surveys and statistical programs – Documentation: 11-522-X19990015644
    Description:

    One method of enriching survey data is to supplement information collected directly from the respondent with that obtained from administrative systems. The aims of such a practice include being able to collect data which might not otherwise be possible, provision of better quality information for data items which respondents may not be able to report accurately (or not at all) reduction of respondent load, and maximising the utility of information held in administrative systems. Given the direct link with administrative information, the data set resulting from such techniques is potentially a powerful basis for policy-relevant analysis and evaluation. However, the processes involved in effectively combining data from different sources raise a number of challenges which need to be addressed by the parties involved. These include issues associated with privacy, data linking, data quality, estimation, and dissemination.

    Release date: 2000-03-02
  • 38. Modeling labour force careers for the lifepaths simulation model Archived
    Surveys and statistical programs – Documentation: 11-522-X19990015648
    Description:

    We estimate the parameters of a stochastic model for labour force careers involving distributions of correlated durations employed, unemployed (with and without job search) and not in the labour force. If the model is to account for sub-annual labour force patterns as well as advancement towards retirement, then no single data source is adequate to inform it. However, it is possible to build up an approximation from a number of different sources.

    Release date: 2000-03-02
  • 39. Creation of an occupational surveillance system in Canada: Combining data for a unique Canadian study Archived
    Surveys and statistical programs – Documentation: 11-522-X19990015652
    Description:

    Objective: To create an occupational surveillance system by collecting, linking, evaluating and disseminating data relating to occupation and mortality with the ultimate aim of reducing or preventing excess risk among workers and the general population.

    Release date: 2000-03-02
  • 40. Particulate matter and daily mortality: Combining time series information from eight U.S. cities Archived
    Surveys and statistical programs – Documentation: 11-522-X19990015656
    Description:

    Time series studies have shown associations between air pollution concentrations and morbidity and mortality. These studies have largely been conducted within single cities, and with varying methods. Critics of these studies have questioned the validity of the data sets used and the statistical techniques applied to them; the critics have noted inconsistencies in findings among studies and even in independent re-analyses of data from the same city. In this paper we review some of the statistical methods used to analyze a subset of a national data base of air pollution, mortality and weather assembled during the National Morbidity and Mortality Air Pollution Study (NMMAPS).

    Release date: 2000-03-02
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