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Results
All (183)
All (183) (0 to 10 of 183 results)
- Surveys and statistical programs – Documentation: 19-20-0001Description: Documents in this series provide insight into the statistical methods used by Statistics Canada to produce official statistics. They include introductory material, in-depth descriptions of techniques and methods, best practices, and guidelines. All documents have undergone review to ensure that they conform to Statistics Canada's mandate and adhere to generally accepted methodological standards and practices.Release date: 2026-06-16
- Surveys and statistical programs – Documentation: 19-20-00012026002Description: This reference document provides answers on selected topics related to the use, interpretation, and calculation of trend-cycle estimates for seasonally adjusted data. It is designed to complement more technical discussions of seasonal adjustment and trend-cycle estimation found in Statistics Canada publications and reference manuals.Release date: 2026-06-08
- Surveys and statistical programs – Documentation: 19-20-00012026001Description: This reference document provides nontechnical answers on selected topics related to the use and interpretation of seasonally adjusted data. It is designed to complement more technical discussions of seasonal adjustment found in Statistics Canada publications and reference manuals.Release date: 2026-05-11
- Articles and reports: 11-633-X2025005Description: This study presents an approach to model changes in the numbers of elementary, secondary and postsecondary students who are immigrants (including both permanent residents and non permanent residents) in response to changes in overall immigration levels.Release date: 2025-12-22
- Articles and reports: 12-001-X202500100007Description: We introduce a novel approach to model-assisted calibration estimation in survey sampling using generalized entropy. The method builds upon recent work by Kwon, Kim and Qiu (2024) and extends it to a model-assisted framework. Unlike traditional calibration techniques, this approach employs a generalized entropy function as the objective for optimization and incorporates a debiasing calibration constraint to ensure design consistency. The proposed estimator is shown to be asymptotically equivalent to an augmented generalized regression (GREG) estimator. It allows for unequal model variance, potentially improving efficiency when the sampling design is informative. The paper presents both design-based and model-based justifications for the method, along with asymptotic properties and variance estimation techniques. Computational aspects are discussed, including an unconstrained optimization approach that facilitates implementation, especially for high-dimensional auxiliary variables. The method’s performance is evaluated through a simulation study, demonstrating its effectiveness in improving estimation efficiency, particularly when the sampling design is informative.Release date: 2025-06-30
- Articles and reports: 12-001-X202500100008Description: Tightened budgets, continuing decrease of response rates in traditional probability surveys and increasing pressure by users for more timely data, has stimulated research on the use of nonprobability sample data, such as administrative records, web scraping, mobile phone data and voluntary internet surveys, for inference on finite population parameters like means and totals. These data are often easier, faster and cheaper to collect than traditional probability samples. However, a major concern with the use of this kind of data for official statistics is their nonrepresentativeness due to possible selection bias, which if not accounted for properly, could bias the inference. In this article, we review and discuss methods considered in the literature to deal with this problem and propose new methods, distinguishing between methods based on integration of the nonprobability sample with an appropriate probability sample, and methods that base the inference solely on the nonprobability sample. Empirical illustrations, based on simulated data are provided.Release date: 2025-06-30
- Articles and reports: 12-001-X202500100009Description: 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
- Articles and reports: 12-001-X202500100011Description: This discussion examines some advancements in survey design and estimation, inspired by the comprehensive appraisal of Professors Jon Rao and Sharon Lohr on current trends in the field. It delves into three specific areas: balanced sampling, calibration, and small area estimation. Probabilistic balanced sampling methods, such as the cube method and penalized balanced sampling, are explored, with an emphasis on addressing emerging challenges, including extensions to linear mixed models, nonparametric regression models, and spatially balanced designs. Calibration is discussed using a modular framework that incorporates modern regression techniques, and highlights innovative uses of model calibration for data editing and causal inference. Small area estimation is considered in the context of latent variable modeling and data integration, emphasizing its role when the variable(s) of interest cannot be measured either directly or without error. Applications in integrating probability and non-probability data and conducting causal analysis at local level are also discussed.Release date: 2025-06-30
- Articles and reports: 12-001-X202500100012Description: In this discussion, we complement the excellent overview by Profs. Lohr and Rao with some additional topics. The first topic is a call for more recognition of the central role of modeling in survey estimation. The second is a brief discussion of the use of partial frame information in survey design. Finally, we draw the attention to recent increases of synthetic methods, in particular, multilevel regression and poststratification (MRP) in small area estimation applications.Release date: 2025-06-30
- 10. Comments by Mary E. Thompson on “Progress in survey science and practice: Yesterday-today-tomorrow”Articles and reports: 12-001-X202500100016Description: These comments on C.-E. Särndal’s paper, “Progress in survey science and practice: yesterday-today-tomorrow”, will touch on probability sampling fundamentals, progress through competing approaches to inference, connections with other parts of statistics, and data in the twenty-first century.Release date: 2025-06-30
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Data (1)
Data (1) ((1 result))
- Public use microdata: 12M0022XDescription:
This package was designed to enable users to access and manipulate the microdata file for Cycle 22 (2008) of the General Social Survey (GSS). It contains information on the objectives, methodology and estimation procedures, as well as guidelines for releasing estimates based on the survey. Cycle 22 collected data from persons 15 years and over living in private households in Canada, excluding residents of the Yukon, Northwest Territories and Nunavut; and full-time residents of institutions. The survey covered a range of topics such as social networks, and social and civic participation. Information was also collected on major changes in respondents' lives in the last 12 months, the resources they used during these transitions and unmet needs for help. Questions were also asked on trust, sense of belonging, volunteering and unpaid work.
Release date: 2010-03-05
Analysis (151)
Analysis (151) (0 to 10 of 151 results)
- Surveys and statistical programs – Documentation: 19-20-0001Description: Documents in this series provide insight into the statistical methods used by Statistics Canada to produce official statistics. They include introductory material, in-depth descriptions of techniques and methods, best practices, and guidelines. All documents have undergone review to ensure that they conform to Statistics Canada's mandate and adhere to generally accepted methodological standards and practices.Release date: 2026-06-16
- Articles and reports: 11-633-X2025005Description: This study presents an approach to model changes in the numbers of elementary, secondary and postsecondary students who are immigrants (including both permanent residents and non permanent residents) in response to changes in overall immigration levels.Release date: 2025-12-22
- Articles and reports: 12-001-X202500100007Description: We introduce a novel approach to model-assisted calibration estimation in survey sampling using generalized entropy. The method builds upon recent work by Kwon, Kim and Qiu (2024) and extends it to a model-assisted framework. Unlike traditional calibration techniques, this approach employs a generalized entropy function as the objective for optimization and incorporates a debiasing calibration constraint to ensure design consistency. The proposed estimator is shown to be asymptotically equivalent to an augmented generalized regression (GREG) estimator. It allows for unequal model variance, potentially improving efficiency when the sampling design is informative. The paper presents both design-based and model-based justifications for the method, along with asymptotic properties and variance estimation techniques. Computational aspects are discussed, including an unconstrained optimization approach that facilitates implementation, especially for high-dimensional auxiliary variables. The method’s performance is evaluated through a simulation study, demonstrating its effectiveness in improving estimation efficiency, particularly when the sampling design is informative.Release date: 2025-06-30
- Articles and reports: 12-001-X202500100008Description: Tightened budgets, continuing decrease of response rates in traditional probability surveys and increasing pressure by users for more timely data, has stimulated research on the use of nonprobability sample data, such as administrative records, web scraping, mobile phone data and voluntary internet surveys, for inference on finite population parameters like means and totals. These data are often easier, faster and cheaper to collect than traditional probability samples. However, a major concern with the use of this kind of data for official statistics is their nonrepresentativeness due to possible selection bias, which if not accounted for properly, could bias the inference. In this article, we review and discuss methods considered in the literature to deal with this problem and propose new methods, distinguishing between methods based on integration of the nonprobability sample with an appropriate probability sample, and methods that base the inference solely on the nonprobability sample. Empirical illustrations, based on simulated data are provided.Release date: 2025-06-30
- Articles and reports: 12-001-X202500100009Description: 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
- Articles and reports: 12-001-X202500100011Description: This discussion examines some advancements in survey design and estimation, inspired by the comprehensive appraisal of Professors Jon Rao and Sharon Lohr on current trends in the field. It delves into three specific areas: balanced sampling, calibration, and small area estimation. Probabilistic balanced sampling methods, such as the cube method and penalized balanced sampling, are explored, with an emphasis on addressing emerging challenges, including extensions to linear mixed models, nonparametric regression models, and spatially balanced designs. Calibration is discussed using a modular framework that incorporates modern regression techniques, and highlights innovative uses of model calibration for data editing and causal inference. Small area estimation is considered in the context of latent variable modeling and data integration, emphasizing its role when the variable(s) of interest cannot be measured either directly or without error. Applications in integrating probability and non-probability data and conducting causal analysis at local level are also discussed.Release date: 2025-06-30
- Articles and reports: 12-001-X202500100012Description: In this discussion, we complement the excellent overview by Profs. Lohr and Rao with some additional topics. The first topic is a call for more recognition of the central role of modeling in survey estimation. The second is a brief discussion of the use of partial frame information in survey design. Finally, we draw the attention to recent increases of synthetic methods, in particular, multilevel regression and poststratification (MRP) in small area estimation applications.Release date: 2025-06-30
- 8. Comments by Mary E. Thompson on “Progress in survey science and practice: Yesterday-today-tomorrow”Articles and reports: 12-001-X202500100016Description: These comments on C.-E. Särndal’s paper, “Progress in survey science and practice: yesterday-today-tomorrow”, will touch on probability sampling fundamentals, progress through competing approaches to inference, connections with other parts of statistics, and data in the twenty-first century.Release date: 2025-06-30
- Journals and periodicals: 11-632-XDescription: The newsletter offers information aimed at three main groups, businesses (small to medium), communities and ethno-cultural groups/communities. Articles and outreach materials will assist their understanding of national and local data from the many relevant sources found on the Statistics Canada website.Release date: 2025-01-16
- Stats in brief: 89-20-00062024003Description: This video is intended for professionals, policymakers, and researchers who are interested in understanding how data linkage can be used to gain deeper insights into various issues. It demonstrates how combining data from different sources can help address gaps in information, leading to better-informed policies and improved outcomes.Release date: 2024-11-25
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Reference (28)
Reference (28) (20 to 30 of 28 results)
- Surveys and statistical programs – Documentation: 99-012-X2011007Description:
This reference guide provides information that enables users to effectively use, apply and interpret data from the 2011 National Household Survey (NHS). This guide contains definitions and explanations of concepts, classifications, data quality and comparability to other sources. Additional information is included for specific variables to help general users better understand the concepts and questions used in the NHS.
Release date: 2013-06-26 - Surveys and statistical programs – Documentation: 99-012-X2011008Description:
This reference guide provides information that enables users to effectively use, apply and interpret data from the 2011 National Household Survey (NHS). This guide contains definitions and explanations of concepts, classifications, data quality and comparability to other sources. Additional information is included for specific variables to help general users better understand the concepts and questions used in the NHS.
Release date: 2013-06-26 - Surveys and statistical programs – Documentation: 99-013-X2011006Description:
This reference guide provides information that enables users to effectively use, apply and interpret data from the 2011 National Household Survey (NHS). This guide contains definitions and explanations of concepts, classifications, data quality and comparability to other sources. Additional information is included for specific variables to help general users better understand the concepts and questions used in the NHS.
Release date: 2013-06-26 - Surveys and statistical programs – Documentation: 91-549-XGeography: CanadaDescription:
The main objective of this document is to raise awareness among Statistics Canada data users of the different sources of language data available at Statistics Canada. Along with the census, surveys with an important sample of official-language minority groups and/or with information on languages are listed by themes. Users will find a description of the survey and its target population, sample sizes (total and according to available linguistic characteristics), available language variables based on questions asked, date of the first release, year for which the data is available and a direct internet link to additional information on the various surveys.
Release date: 2013-05-29 - 25. Families Reference Guide, 2011 Census ArchivedSurveys and statistical programs – Documentation: 98-312-X2011005Description:
This guide focuses on the following topic: Family variables. Provides information that enables users to effectively use, apply and interpret data from the 2011 Census. Each guide contains definitions and explanations on census concepts, talks about changes made to the 2011 Census, data quality and historical comparability, as well as comparison with other data sources. Additional information will be included for specific variables to help general users better understand the concepts and questions used in the census.
Release date: 2012-09-19 - Surveys and statistical programs – Documentation: 98-313-X2011001Description:
This guide focuses on the following topic: Structural Type of Dwelling and Collectives variables.
Provides information that enables users to effectively use, apply and interpret data from the 2011 Census. Each guide contains definitions and explanations on census concepts, talks about changes made to the 2011 Census, data quality and historical comparability, as well as comparison with other data sources. Additional information will be included for specific variables to help general users better understand the concepts and questions used in the census.
Release date: 2012-09-19 - Notices and consultations: 93-600-XDescription:
This guide aids users in providing feedback for the 2016 Census Metropolitan Area (CMA) and Census Agglomeration (CA) Strategic Review and in contributing ideas and suggestions towards shaping the rules that will define the 2016 CMA/CAs. Readers will find the definition of the concepts, as well as discussion points and questions.
Release date: 2010-09-17 - Surveys and statistical programs – Documentation: 89-634-X2009008Geography: CanadaDescription:
The Strengths and Difficulties Questionnaire (SDQ) is a parent-reported instrument designed to provide information on children's behaviours and relationships. The SDQ consists of 25 items which are grouped into five subscales: (1) pro-social, (2) inattention-hyperactivity, (3) emotional symptoms, (4) conduct problems, and (5) peer problems. The SDQ was used to provide information on children aged 2 to 5 years in the 2006 Aboriginal Children's Survey (ACS). Though validated on general populations, the constructs of the SDQ have not been validated for off-reserve First Nations, Métis and Inuit children in Canada. The first objective of this evaluation is to examine if the five subscales of the SDQ demonstrate construct validity and reliability for off-reserve First Nations, Métis and Inuit children. The second objective is to examine if an alternative set of subscales, using the 25 SDQ items, may be more valid and reliable for off-reserve First Nations, Métis and Inuit children.
Release date: 2009-11-25