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All (3) ((3 results))

  • 1. Development of a composite quality indicator for statistical products derived from administrative sources
    Articles and reports: 46-28-0001202200100001
    Description:

    When a survey publishes statistics with a quality indicator, it is usually derived from measures based on sampling theory. The production of quality indicators is a significant challenge when statistics are produced using alternative sources for which no sampling is done. This paper describes a new method used to create a quality indicator that combines indicators obtained at different stages of data processing. An example of the application of the method in the Canadian Housing Statistics Program is provided in the Appendix.

    Release date: 2022-01-06
  • 2. 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
  • 3. Quality measures related to the telephone first contact collection process in the Labour force survey Archived
    Articles and reports: 11-522-X20050019487
    Description:

    The goal of this presentation is to present the different quality measures used to evaluate and manage the collection process related to the Telephone First Contact methodology in LFS.

    Release date: 2007-03-02
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Articles and reports (3)

Articles and reports (3) ((3 results))

  • 1. Development of a composite quality indicator for statistical products derived from administrative sources
    Articles and reports: 46-28-0001202200100001
    Description:

    When a survey publishes statistics with a quality indicator, it is usually derived from measures based on sampling theory. The production of quality indicators is a significant challenge when statistics are produced using alternative sources for which no sampling is done. This paper describes a new method used to create a quality indicator that combines indicators obtained at different stages of data processing. An example of the application of the method in the Canadian Housing Statistics Program is provided in the Appendix.

    Release date: 2022-01-06
  • 2. 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
  • 3. Quality measures related to the telephone first contact collection process in the Labour force survey Archived
    Articles and reports: 11-522-X20050019487
    Description:

    The goal of this presentation is to present the different quality measures used to evaluate and manage the collection process related to the Telephone First Contact methodology in LFS.

    Release date: 2007-03-02
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