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All (183)

All (183) (10 to 20 of 183 results)

  • Journals and periodicals: 11-632-X
    Description: 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

  • Surveys and statistical programs – Documentation: 11-633-X2024004
    Description: The Longitudinal Immigration Database (IMDB) is a comprehensive source of data that plays a key role in the understanding of the economic behaviour of immigrants. It is the only annual Canadian dataset that allows users to study the characteristics of immigrants to Canada at the time of admission and their economic outcomes and regional (inter-provincial) mobility over a time span of more than 40 years.
    Release date: 2024-12-09

  • Notices and consultations: 95-635-X
    Description: To stay relevant, preparing for a new Census of Agriculture requires a thorough evaluation of data requirements. Before each census, Statistics Canada conducts consultations to solicit input and feedback on the Census of Agriculture's content. This report describes those consultations and the process that was followed to test and determine which topics could be potentially retained for the next census.
    Release date: 2024-11-27

  • Stats in brief: 89-20-00062024003
    Description: 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

  • Articles and reports: 11-522-X202200100017
    Description: In this paper, we look for presence of heterogeneity in conducting impact evaluations of the Skills Development intervention delivered under the Labour Market Development Agreements. We use linked longitudinal administrative data covering a sample of Skills Development participants from 2010 to 2017. We apply a causal machine-learning estimator as in Lechner (2019) to estimate the individualized program impacts at the finest aggregation level. These granular impacts reveal the distribution of net impacts facilitating further investigation as to what works for whom. The findings suggest statistically significant improvements in labour market outcomes for participants overall and for subgroups of policy interest.
    Release date: 2024-06-28

  • Articles and reports: 13-605-X202400100003
    Description: The document focuses on the evolution of Statistics Canada's labour productivity program, tracing its historical background, outlining its structure, as well as detailing the methodology and data sources used. It then discusses the diverse applications of provincial productivity data, identifies key users of productivity statistics, and highlights essential considerations for their interpretation. Finally, the document addresses the review process for quarterly and annual productivity measures and recent program improvements.
    Release date: 2024-06-05

  • Articles and reports: 11-522-X202200100003
    Description: Estimation at fine levels of aggregation is necessary to better describe society. Small area estimation model-based approaches that combine sparse survey data with rich data from auxiliary sources have been proven useful to improve the reliability of estimates for small domains. Considered here is a scenario where small area model-based estimates, produced at a given aggregation level, needed to be disaggregated to better describe the social structure at finer levels. For this scenario, an allocation method was developed to implement the disaggregation, overcoming challenges associated with data availability and model development at such fine levels. The method is applied to adult literacy and numeracy estimation at the county-by-group-level, using data from the U.S. Program for the International Assessment of Adult Competencies. In this application the groups are defined in terms of age or education, but the method could be applied to estimation of other equity-deserving groups.
    Release date: 2024-03-25

  • Articles and reports: 11-522-X202200100005
    Description: Sampling variance smoothing is an important topic in small area estimation. In this paper, we propose sampling variance smoothing methods for small area proportion estimation. In particular, we consider the generalized variance function and design effect methods for sampling variance smoothing. We evaluate and compare the smoothed sampling variances and small area estimates based on the smoothed variance estimates through analysis of survey data from Statistics Canada. The results from real data analysis indicate that the proposed sampling variance smoothing methods work very well for small area estimation.
    Release date: 2024-03-25

  • Articles and reports: 11-522-X202200100013
    Description: Respondents to typical household surveys tend to significantly underreport their potential use of food aid distributed by associations. This underreporting is most likely related to the social stigma felt by people experiencing great financial difficulty. As a result, survey estimates of the number of recipients of that aid are much lower than the direct counts from the associations. Those counts tend to overestimate due to double counting. Through its adapted protocol, the Enquête Aide alimentaire (EAA) collected in late 2021 in France at a sample of sites of food aid distribution associations, controls the biases that affect the other sources and determines to what extent this aid is used.
    Release date: 2024-03-25

  • Articles and reports: 11-522-X202200100014
    Description: Ethnic minorities are often underrepresented in survey research, due to the challenges many researchers face in including these populations. While some studies discuss several methods in comparison, few have directly compared these methods empirically, leaving researchers seeking to include ethnic minorities in their studies unsure of their best options. In this article, I briefly review the methodological and ethical reasons for increasing ethnic minority representation in social science research, as well as challenges of doing so. I then present findings from ten studies which empirically compare methods of sampling and/or recruiting ethnic minority individuals. Finally, I discuss some implications for future research.
    Release date: 2024-03-25
Data (1)

Data (1) ((1 result))

  • Public use microdata: 12M0022X
    Description:

    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: 19-20-0001
    Description: 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-X2025005
    Description: 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-X202500100007
    Description: 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-X202500100008
    Description: 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-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

  • Articles and reports: 12-001-X202500100011
    Description: 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-X202500100012
    Description: 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

  • Articles and reports: 12-001-X202500100016
    Description: 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-X
    Description: 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: 89-20-00062024003
    Description: 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
Reference (28)

Reference (28) (0 to 10 of 28 results)

  • Surveys and statistical programs – Documentation: 19-20-0001
    Description: 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: 19-20-00012026002
    Description: 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: 19-20-00012026001
    Description: 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

  • Surveys and statistical programs – Documentation: 11-633-X2024004
    Description: The Longitudinal Immigration Database (IMDB) is a comprehensive source of data that plays a key role in the understanding of the economic behaviour of immigrants. It is the only annual Canadian dataset that allows users to study the characteristics of immigrants to Canada at the time of admission and their economic outcomes and regional (inter-provincial) mobility over a time span of more than 40 years.
    Release date: 2024-12-09

  • Notices and consultations: 95-635-X
    Description: To stay relevant, preparing for a new Census of Agriculture requires a thorough evaluation of data requirements. Before each census, Statistics Canada conducts consultations to solicit input and feedback on the Census of Agriculture's content. This report describes those consultations and the process that was followed to test and determine which topics could be potentially retained for the next census.
    Release date: 2024-11-27

  • Surveys and statistical programs – Documentation: 11-633-X2024001
    Description: The Longitudinal Immigration Database (IMDB) is a comprehensive source of data that plays a key role in the understanding of the economic behaviour of immigrants. It is the only annual Canadian dataset that allows users to study the characteristics of immigrants to Canada at the time of admission and their economic outcomes and regional (inter-provincial) mobility over a time span of more than 35 years.
    Release date: 2024-01-22

  • Surveys and statistical programs – Documentation: 84-538-X
    Geography: Canada
    Description: This electronic publication presents the methodology underlying the production of the life tables for Canada, provinces and territories.
    Release date: 2023-08-28

  • Surveys and statistical programs – Documentation: 11-633-X2022009
    Description: The Longitudinal Immigration Database (IMDB) is a comprehensive source of data that plays a key role in the understanding of the economic behaviour of immigrants. It is the only annual Canadian dataset that allows users to study the characteristics of immigrants to Canada at the time of admission and their economic outcomes and regional (inter-provincial) mobility over a time span of more than 35 years.

    This report will discuss the IMDB data sources, concepts and variables, record linkage, data processing, dissemination, data evaluation and quality indicators, comparability with other immigration datasets, and the analyses possible with the IMDB.

    Release date: 2022-12-05

  • Surveys and statistical programs – Documentation: 98-304-X
    Description: The Guide to the Census of Population is a reference document that describes the various phases of the 2021 Census of Population. The guide provides an overview of content determination, sampling design, collection, data processing, data quality assessment, confidentiality guidelines and dissemination. It also includes response rates and other data quality information. This product may be useful to both new and experienced users who wish to familiarize themselves with and find specific information about the 2021 Census of Population.

    The Guide to the Census of Population combines information previously available in the Overview of the Census, National Household Survey User Guide and the Data Quality and Confidentiality Standards and Guidelines from 2011. 

    Release date: 2022-11-30

  • Surveys and statistical programs – Documentation: 11-633-X2021008
    Description: The Longitudinal Immigration Database (IMDB) is a comprehensive source of data that plays a key role in the understanding of the economic behaviour of immigrants. It is the only annual Canadian dataset that allows users to study the characteristics of immigrants to Canada at the time of admission and their economic outcomes and regional (inter-provincial) mobility over a time span of more than 35 years. The IMDB includes Immigration, Refugees and Citizenship Canada (IRCC) administrative records which contain exhaustive information about immigrants who were admitted to Canada since 1952. It also includes data about non-permanent residents who have been issued temporary resident permits since 1980. This report will discuss the IMDB data sources, concepts and variables, record linkage, data processing, dissemination, data evaluation and quality indicators, comparability with other immigration datasets, and the analyses possible with the IMDB.
    Release date: 2021-12-06