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1. Bridging BigData and sampling methodology: What is big and where is the bridge?
Articles and reports: 12-001-X202500100009
Description: BigData users and the BigData research community are expanding rapidly, while statisticians at large are seemingly becoming divided between those who are enthusiastic and those who are concerned, if not downright hostile. Is BigData also a big step ahead, truly advancing our ability to extract meaningful information and actual knowledge from data? Is BigData underplaying traditional statistical inference as we know it, supplanting survey methodology as a low-cost futuristic option? In this paper I will attempt to unravel the multifaceted relationship bridging BigData to sampling methodology. Starting by reasoning why it should be interesting to look at BigData from a sampling statistician’s perspective, I will delve deeper into the somewhat ambiguous definition of BigData and share some very personal considerations and views on the matter. In the process, several open questions will arise while discussing a personal selection of insights that are traceable through the vast body of statistical literature around BigData and sampling methodology. The discussion will take various angles explored across nine key points, and it will conclude with a forward-looking perspective on a main challenge for future research: addressing the strong assumptions needed to manage deviations from purely randomized data collection.
Release date: 2025-06-30
2. Comments by Risto Lehtonen on “Progress in survey science and practice: Yesterday-today-tomorrow”
Articles and reports: 12-001-X202500100018
Description: In his article, Professor Carl-Erik Särndal presents for sample-based statistics a new conceptual framework with only a few key assumptions. Selected aspects of the research tradition in Survey Science are briefly discussed in my comments.
Release date: 2025-06-30
3. Comments by Aditi Sen on “Exploring the assumption that commercial online nonprobability survey respondents are answering in good faith”
Articles and reports: 12-001-X202400100010
Description: This discussion summarizes the interesting new findings around measurement errors in opt-in surveys by Kennedy, Mercer and Lau (KML). While KML enlighten readers about “bogus responding” and possible patterns in them, this discussion suggests combining these new-found results with other avenues of research in nonprobability sampling, such as improvement of representativeness.
Release date: 2024-06-25
4. Improvements to the Canadian Income Survey Methodology for the 2022 Reference Year
Articles and reports: 75F0002M2024005
Description: The Canadian Income Survey (CIS) has introduced improvements to the methods and data sources used to produce income and poverty estimates with the release of its 2022 reference year estimates. Foremost among these improvements is a significant increase in the sample size for a large subset of the CIS content. The weighting methodology was also improved and the target population of the CIS was changed from persons aged 16 years and over to persons aged 15 years and over. This paper describes the changes made and presents the approximate net result of these changes on the income estimates and data quality of the CIS using 2021 data. The changes described in this paper highlight the ways in which data quality has been improved while having little impact on key CIS estimates and trends.
Release date: 2024-04-26
5. ABS DataLab output checking tools Archived
Articles and reports: 11-522-X202200100006
Description: The Australian Bureau of Statistics (ABS) is committed to improving access to more microdata, while ensuring privacy and confidentiality is maintained, through its virtual DataLab which supports researchers to undertake complex research more efficiently. Currently, the DataLab research outputs need to follow strict rules to minimise disclosure risks for clearance. However, the clerical-review process is not cost effective and has potential to introduce errors. The increasing number of statistical outputs from different projects can potentially introduce differencing risks even though these outputs from different projects have met the strict output rules. The ABS has been exploring the possibility of providing automatic output checking using the ABS cellkey methodology to ensure that all outputs across different projects are protected consistently to minimise differencing risks and reduce costs associated with output checking.
Release date: 2024-03-25
6. Mapping Production Activity in Yukon: Experimental Indexes of Grid Square-based Gross Domestic Product
Articles and reports: 11-633-X2023003
Description: This paper spans the academic work and estimation strategies used in national statistics offices. It addresses the issue of producing fine, grid-level geography estimates for Canada by exploring the measurement of subprovincial and subterritorial gross domestic product using Yukon as a test case.
Release date: 2023-12-15
7. Improvements to the Canadian Income Survey Methodology for the 2021 Reference Year
Articles and reports: 75F0002M2023005
Description: The Canadian Income Survey (CIS) has introduced improvements to the methods and systems used to produce income estimates with the release of its 2021 reference year estimates. This paper describes the changes and presents the approximate net result of these changes on income estimates using data for 2019 and 2020. The changes described in this paper highlight the ways in which data quality has been improved while producing minimal impact on key CIS estimates and trends.
Release date: 2023-08-29
8. Understanding Confidence Intervals for the 2021 Census
Articles and reports: 98-20-00012021003
Description:
This fact sheet provides a concise description of the context to the understanding of confidence intervals. Confidence intervals are a useful data quality indicator. Confidence intervals will usually be available in data tables accessible through the Statistics Canada website.

Release date: 2022-09-21
9. Measuring the undercoverage of two data sources with a nearly perfect coverage through capture and recapture in the presence of linkage errors Archived
Articles and reports: 11-522-X202100100006
Description:
In the context of its "admin-first" paradigm, Statistics Canada is prioritizing the use of non-survey sources to produce official statistics. This paradigm critically relies on non-survey sources that may have a nearly perfect coverage of some target populations, including administrative files or big data sources. Yet, this coverage must be measured, e.g., by applying the capture-recapture method, where they are compared to other sources with good coverage of the same populations, including a census. However, this is a challenging exercise in the presence of linkage errors, which arise inevitably when the linkage is based on quasi-identifiers, as is typically the case. To address the issue, a new methodology is described where the capture-recapture method is enhanced with a new error model that is based on the number of links adjacent to a given record. It is applied in an experiment with public census data.
Key Words: dual system estimation, data matching, record linkage, quality, data integration, big data.

Release date: 2021-10-22
10. Canadian Survey on Disability, 2017: Concepts and Methods Guide Archived
Articles and reports: 89-654-X2018001
Description:
The Canadian Survey on Disability (CSD) is a national survey of Canadians aged 15 and over whose everyday activities are limited because of a long-term condition or health-related problem.
The 2017 CSD Concepts and Methods Guide is designed to assist CSD data users by providing relevant information on survey content and concepts, sampling design, collection methods, data processing, data quality and product availability. Chapter 1 of this guide provides an overview of the 2017 CSD by introducing the survey's background and objectives. Chapter 2 explains the key concepts and definitions and introduces the indicators measured by the CSD questionnaire modules. Chapters 3 to 6 cover important aspects of survey methodology, from sampling design to data collection and processing. Chapters 7 and 8 cover issues of data quality, including the approaches used to minimize and correct errors throughout all stages of the survey. Users are cautioned against making comparisons with data from the 2012 CSD. Chapter 9 outlines the survey products that are available to the public, including data tables, an analytical article and reference material. Appendices provide more detail on the survey's indicators and other supporting documents for the CSD.
Release date: 2018-11-28

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Analysis (97)

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1. Bridging BigData and sampling methodology: What is big and where is the bridge?
Articles and reports: 12-001-X202500100009
Description: BigData users and the BigData research community are expanding rapidly, while statisticians at large are seemingly becoming divided between those who are enthusiastic and those who are concerned, if not downright hostile. Is BigData also a big step ahead, truly advancing our ability to extract meaningful information and actual knowledge from data? Is BigData underplaying traditional statistical inference as we know it, supplanting survey methodology as a low-cost futuristic option? In this paper I will attempt to unravel the multifaceted relationship bridging BigData to sampling methodology. Starting by reasoning why it should be interesting to look at BigData from a sampling statistician’s perspective, I will delve deeper into the somewhat ambiguous definition of BigData and share some very personal considerations and views on the matter. In the process, several open questions will arise while discussing a personal selection of insights that are traceable through the vast body of statistical literature around BigData and sampling methodology. The discussion will take various angles explored across nine key points, and it will conclude with a forward-looking perspective on a main challenge for future research: addressing the strong assumptions needed to manage deviations from purely randomized data collection.
Release date: 2025-06-30
2. Comments by Risto Lehtonen on “Progress in survey science and practice: Yesterday-today-tomorrow”
Articles and reports: 12-001-X202500100018
Description: In his article, Professor Carl-Erik Särndal presents for sample-based statistics a new conceptual framework with only a few key assumptions. Selected aspects of the research tradition in Survey Science are briefly discussed in my comments.
Release date: 2025-06-30
3. Comments by Aditi Sen on “Exploring the assumption that commercial online nonprobability survey respondents are answering in good faith”
Articles and reports: 12-001-X202400100010
Description: This discussion summarizes the interesting new findings around measurement errors in opt-in surveys by Kennedy, Mercer and Lau (KML). While KML enlighten readers about “bogus responding” and possible patterns in them, this discussion suggests combining these new-found results with other avenues of research in nonprobability sampling, such as improvement of representativeness.
Release date: 2024-06-25
4. Improvements to the Canadian Income Survey Methodology for the 2022 Reference Year
Articles and reports: 75F0002M2024005
Description: The Canadian Income Survey (CIS) has introduced improvements to the methods and data sources used to produce income and poverty estimates with the release of its 2022 reference year estimates. Foremost among these improvements is a significant increase in the sample size for a large subset of the CIS content. The weighting methodology was also improved and the target population of the CIS was changed from persons aged 16 years and over to persons aged 15 years and over. This paper describes the changes made and presents the approximate net result of these changes on the income estimates and data quality of the CIS using 2021 data. The changes described in this paper highlight the ways in which data quality has been improved while having little impact on key CIS estimates and trends.
Release date: 2024-04-26
5. ABS DataLab output checking tools Archived
Articles and reports: 11-522-X202200100006
Description: The Australian Bureau of Statistics (ABS) is committed to improving access to more microdata, while ensuring privacy and confidentiality is maintained, through its virtual DataLab which supports researchers to undertake complex research more efficiently. Currently, the DataLab research outputs need to follow strict rules to minimise disclosure risks for clearance. However, the clerical-review process is not cost effective and has potential to introduce errors. The increasing number of statistical outputs from different projects can potentially introduce differencing risks even though these outputs from different projects have met the strict output rules. The ABS has been exploring the possibility of providing automatic output checking using the ABS cellkey methodology to ensure that all outputs across different projects are protected consistently to minimise differencing risks and reduce costs associated with output checking.
Release date: 2024-03-25
6. Mapping Production Activity in Yukon: Experimental Indexes of Grid Square-based Gross Domestic Product
Articles and reports: 11-633-X2023003
Description: This paper spans the academic work and estimation strategies used in national statistics offices. It addresses the issue of producing fine, grid-level geography estimates for Canada by exploring the measurement of subprovincial and subterritorial gross domestic product using Yukon as a test case.
Release date: 2023-12-15
7. Improvements to the Canadian Income Survey Methodology for the 2021 Reference Year
Articles and reports: 75F0002M2023005
Description: The Canadian Income Survey (CIS) has introduced improvements to the methods and systems used to produce income estimates with the release of its 2021 reference year estimates. This paper describes the changes and presents the approximate net result of these changes on income estimates using data for 2019 and 2020. The changes described in this paper highlight the ways in which data quality has been improved while producing minimal impact on key CIS estimates and trends.
Release date: 2023-08-29
8. Understanding Confidence Intervals for the 2021 Census
Articles and reports: 98-20-00012021003
Description:
This fact sheet provides a concise description of the context to the understanding of confidence intervals. Confidence intervals are a useful data quality indicator. Confidence intervals will usually be available in data tables accessible through the Statistics Canada website.

Release date: 2022-09-21
9. Measuring the undercoverage of two data sources with a nearly perfect coverage through capture and recapture in the presence of linkage errors Archived
Articles and reports: 11-522-X202100100006
Description:
In the context of its "admin-first" paradigm, Statistics Canada is prioritizing the use of non-survey sources to produce official statistics. This paradigm critically relies on non-survey sources that may have a nearly perfect coverage of some target populations, including administrative files or big data sources. Yet, this coverage must be measured, e.g., by applying the capture-recapture method, where they are compared to other sources with good coverage of the same populations, including a census. However, this is a challenging exercise in the presence of linkage errors, which arise inevitably when the linkage is based on quasi-identifiers, as is typically the case. To address the issue, a new methodology is described where the capture-recapture method is enhanced with a new error model that is based on the number of links adjacent to a given record. It is applied in an experiment with public census data.
Key Words: dual system estimation, data matching, record linkage, quality, data integration, big data.

Release date: 2021-10-22
10. Canadian Survey on Disability, 2017: Concepts and Methods Guide Archived
Articles and reports: 89-654-X2018001
Description:
The Canadian Survey on Disability (CSD) is a national survey of Canadians aged 15 and over whose everyday activities are limited because of a long-term condition or health-related problem.
The 2017 CSD Concepts and Methods Guide is designed to assist CSD data users by providing relevant information on survey content and concepts, sampling design, collection methods, data processing, data quality and product availability. Chapter 1 of this guide provides an overview of the 2017 CSD by introducing the survey's background and objectives. Chapter 2 explains the key concepts and definitions and introduces the indicators measured by the CSD questionnaire modules. Chapters 3 to 6 cover important aspects of survey methodology, from sampling design to data collection and processing. Chapters 7 and 8 cover issues of data quality, including the approaches used to minimize and correct errors throughout all stages of the survey. Users are cautioned against making comparisons with data from the 2012 CSD. Chapter 9 outlines the survey products that are available to the public, including data tables, an analytical article and reference material. Appendices provide more detail on the survey's indicators and other supporting documents for the CSD.
Release date: 2018-11-28

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Date modified:: 2026-08-13