Quality assurance

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  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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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  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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) (70 to 80 of 78 results)

  • Surveys and statistical programs – Documentation: 11-522-X19980015034
    Description:

    A model of secondary school progression has been estimated using data from the 1991 School Leavers Survey conducted by Statistics Canada. The data on which the school progression model was based comprised current educational status and responses to retrospective questions on the timing of schooling events. These data were sufficient for approximate reconstruction of educational event histories of each respondent. The school progression model was designed to be included in a larger, continuous time micro-simulation model. Its main features involve estimation -- by age, month of birth and season for both sexes in each province -- of rates of graduation, of dropout, of return and of dropout graduation. Estimation was reinforced with auxiliary 1991 Census and administative data.

    Release date: 1999-10-22

  • Surveys and statistical programs – Documentation: 11-522-X19980015035
    Description:

    In a longitudinal survey conducted for k periods some units may be observed for less than k of the periods. Examples include, surveys designed with partially overlapping subsamples, a pure panel survey with nonresponse, and a panel survey supplemented with additional samples for some of the time periods. Estimators of the regression type are exhibited for such surveys. An application to special studies associated with the National Resources Inventory is discussed.

    Release date: 1999-10-22

  • Surveys and statistical programs – Documentation: 11-522-X19980015036
    Description:

    Multivariate logistic regression, introduced by Glonek and McCullagh (1995) as a generalisation of logistic regression, is useful in the analysis of longitudinal data as it allows for dependent repeated observations of a categorical variable and for incomplete response profiles. We show how the method can be extended to deal with data from complex surveys and we illustrate it on data from the Swiss Labour Force Survey. The effect of the sampling weights on the parameter estimates and their standard errors is considered.

    Release date: 1999-10-22

  • Surveys and statistical programs – Documentation: 11-522-X19980015037
    Description:

    For longitudinal data, mixed models are often used, since they allow analysts to take account of the correlation between different observations from the same individual. The finite mixture model may be considered as a special case of a mixed model. In this document, attention will be given to the maximum likelihood method. The maximization of the likelihood function for a finite mixture of distributions is generally more difficult than in the usual case of a single distribution and can require considerable time. The objective of this project was therefore primarily to identify the one or more algorithms that best meet the criteria of run time and of efficiency in finding the solution. To achieve this objective, a simulation study was carried out. Only the situation in which the dependent variable is dichotomous was considered. This situation is very useful in practice, since among other things it can be used to model discrete durations, such as the length of time in "low income" status.

    Release date: 1999-10-22

  • Surveys and statistical programs – Documentation: 75F0002M1993007
    Description:

    This report presents a summary evaluation of the quality of the data collected during the Survey of Labour and Income Dynamics (SLID) field test of labour market activity data, held in January and February 1993.

    Release date: 1995-12-30

  • Surveys and statistical programs – Documentation: 75F0002M1993011
    Description:

    This report presents a summary evaluation of the quality of the data collected during the Survey of Labour and Income Dynamics (SLID) field test of income and wealth, held in April and May 1993.

    Release date: 1995-12-30

  • Surveys and statistical programs – Documentation: 75F0002M1994015
    Description:

    This paper describes how the computer-assisted interviewing (CAI) income application was programmed for a Survey of Labour and Income Dynamics (SLID) test conducted in 1993.

    Release date: 1995-12-30

  • Surveys and statistical programs – Documentation: 75F0002M1995007
    Description:

    This paper describes the impact of computer-assisted interviewing (CAI) on the quality of the Survey of Labour and Income Dynamics (SLID) data in three content areas: labour force activity, respondent-sensitive sources of income, and household relationships.

    Release date: 1995-12-30
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