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  • 21. Data Literacy Training Products
    Journals and periodicals: 89-20-0006
    Description: Statistics Canada is committed to sharing our knowledge and expertise to help all Canadians develop their data literacy skills by developing a series of data literacy training resources. Data literacy is a key skill needed in the 21st century. It is generally described as the ability to derive meaning from data. Data literacy focuses on the competencies or skills involved in working with data, including the ability to read, analyze, interpret, visualize data, as well as to drive good decision-making.
    Release date: 2024-07-16
  • 22. Monthly Survey of Manufacturing May 2024
    Stats in brief: 11-001-X20241973628
    Description: Release published in The Daily – Statistics Canada’s official release bulletin
    Release date: 2024-07-15
  • 23. Wholesale trade May 2024
    Stats in brief: 11-001-X20241973647
    Description: Release published in The Daily – Statistics Canada’s official release bulletin
    Release date: 2024-07-15
  • 24. Detailed preliminary data on confirmed cases of COVID-19 January 15, 2020, to July 5, 2024 (revised data)
    Stats in brief: 11-001-X202419423503
    Description: Release published in The Daily – Statistics Canada’s official release bulletin
    Release date: 2024-07-12
  • 25. Building permits May 2024
    Stats in brief: 11-001-X20241943592
    Description: Release published in The Daily – Statistics Canada’s official release bulletin
    Release date: 2024-07-12
  • 26. Leading indicator of international arrivals to Canada June 2024
    Stats in brief: 11-001-X202419227643
    Description: Release published in The Daily – Statistics Canada’s official release bulletin
    Release date: 2024-07-10
  • 27. Census of Population web pages: Introduction
    Stats in brief: 98-20-00032021031
    Description: This video is part of a series that is designed to give you a basic understanding of the Census of Population web pages. This video will provide an overview of the major links and products on the main page that are available to all users.
    Release date: 2024-07-10
  • 28. Census of Population web pages: Profile Part 1, How to find and filter data
    Stats in brief: 98-20-00032021032
    Description: This video is part of a series that is designed to give you a basic understanding of the Census of Population web pages. The purpose of this video is to explain where to find the most popular standard data product of the Census of Population, the 2021 Census Profile, and how to filter the data.
    Release date: 2024-07-10
  • 29. Census of Population web pages: Profile Part 2, Adding geographies and downloading options
    Stats in brief: 98-20-00032021033
    Description: This video is part of a series that is designed to give you a basic understanding of the Census of Population web pages. The purpose of this video is to explain how to add geographies in the 2021 Census Profile and to present the various downloading options to see the data.
    Release date: 2024-07-10
  • 30. Census of Population concept videos
    Journals and periodicals: 98-20-0003
    Description: Once every five years, the Census of Population provides a detailed and comprehensive statistical portrait of Canada that is vital to our country. It is the primary source of sociodemographic data for specific population groups such as lone-parent families, Indigenous peoples, immigrants, seniors and language groups.

    In order to help users of census products to better understand the various Census of Population concepts, Statistics Canada has developed, in the context of the activities of the 2021 Census and previous censuses, a collection of short videos. These videos are a reference source for users who are new to census concepts or those who have some experience with these concepts, but may need a refresher or would like to expand their knowledge.

    Release date: 2024-07-10
Stats in brief (571)

Stats in brief (571) (0 to 10 of 571 results)

Articles and reports (1,185)

Articles and reports (1,185) (10 to 20 of 1,185 results)

  • 11. An update on the profile of children with affirmative responses to the 2021 Census questions on difficulties with activities of daily living
    Articles and reports: 36-28-0001202400600006
    Description: This study presents an updated sociodemographic profile of children aged 0 to 14 years with affirmative responses largely based on parent reports to the questions on the 2021 Census long-form questionnaire about difficulties with activities of daily living.
    Release date: 2024-06-26
  • 12. Authors’ response to comments on “Handling non-probability samples through inverse probability weighting with an application to Statistics Canada’s crowdsourcing data”: Some new developments on likelihood approaches to estimation of participation probabilities for non-probability samples
    Articles and reports: 12-001-X202400100001
    Description: Inspired by the two excellent discussions of our paper, we offer some new insights and developments into the problem of estimating participation probabilities for non-probability samples. First, we propose an improvement of the method of Chen, Li and Wu (2020), based on best linear unbiased estimation theory, that more efficiently leverages the available probability and non-probability sample data. We also develop a sample likelihood approach, similar in spirit to the method of Elliott (2009), that properly accounts for the overlap between both samples when it can be identified in at least one of the samples. We use best linear unbiased prediction theory to handle the scenario where the overlap is unknown. Interestingly, our two proposed approaches coincide in the case of unknown overlap. Then, we show that many existing methods can be obtained as a special case of a general unbiased estimating function. Finally, we conclude with some comments on nonparametric estimation of participation probabilities.
    Release date: 2024-06-25
  • 13. Comments by Changbao Wu on “Handling non-probability samples through inverse probability weighting with an application to Statistics Canada’s crowdsourcing data”
    Articles and reports: 12-001-X202400100002
    Description: We provide comparisons among three parametric methods for the estimation of participation probabilities and some brief comments on homogeneous groups and post-stratification.
    Release date: 2024-06-25
  • 14. Comments by Julie Gershunskaya and Vladislav Beresovsky on “Handling non-probability samples through inverse probability weighting with an application to Statistics Canada’s crowdsourcing data”
    Articles and reports: 12-001-X202400100003
    Description: Beaumont, Bosa, Brennan, Charlebois and Chu (2024) propose innovative model selection approaches for estimation of participation probabilities for non-probability sample units. We focus our discussion on the choice of a likelihood and parameterization of the model, which are key for the effectiveness of the techniques developed in the paper. We consider alternative likelihood and pseudo-likelihood based methods for estimation of participation probabilities and present simulations implementing and comparing the AIC based variable selection. We demonstrate that, under important practical scenarios, the approach based on a likelihood formulated over the observed pooled non-probability and probability samples performed better than the pseudo-likelihood based alternatives. The contrast in sensitivity of the AIC criteria is especially large for small probability sample sizes and low overlap in covariates domains.
    Release date: 2024-06-25
  • 15. Handling non-probability samples through inverse probability weighting with an application to Statistics Canada’s crowdsourcing data
    Articles and reports: 12-001-X202400100004
    Description: Non-probability samples are being increasingly explored in National Statistical Offices as an alternative to probability samples. However, it is well known that the use of a non-probability sample alone may produce estimates with significant bias due to the unknown nature of the underlying selection mechanism. Bias reduction can be achieved by integrating data from the non-probability sample with data from a probability sample provided that both samples contain auxiliary variables in common. We focus on inverse probability weighting methods, which involve modelling the probability of participation in the non-probability sample. First, we consider the logistic model along with pseudo maximum likelihood estimation. We propose a variable selection procedure based on a modified Akaike Information Criterion (AIC) that properly accounts for the data structure and the probability sampling design. We also propose a simple rank-based method of forming homogeneous post-strata. Then, we extend the Classification and Regression Trees (CART) algorithm to this data integration scenario, while again properly accounting for the probability sampling design. A bootstrap variance estimator is proposed that reflects two sources of variability: the probability sampling design and the participation model. Our methods are illustrated using Statistics Canada’s crowdsourcing and survey data.
    Release date: 2024-06-25
  • 16. Author’s response to comments on “Exchangeability assumption in propensity-score based adjustment methods for population mean estimation using non-probability samples”
    Articles and reports: 12-001-X202400100005
    Description: In this rejoinder, I address the comments from the discussants, Dr. Takumi Saegusa, Dr. Jae-Kwang Kim and Ms. Yonghyun Kwon. Dr. Saegusa’s comments about the differences between the conditional exchangeability (CE) assumption for causal inferences versus the CE assumption for finite population inferences using nonprobability samples, and the distinction between design-based versus model-based approaches for finite population inference using nonprobability samples, are elaborated and clarified in the context of my paper. Subsequently, I respond to Dr. Kim and Ms. Kwon’s comprehensive framework for categorizing existing approaches for estimating propensity scores (PS) into conditional and unconditional approaches. I expand their simulation studies to vary the sampling weights, allow for misspecified PS models, and include an additional estimator, i.e., scaled adjusted logistic propensity estimator (Wang, Valliant and Li (2021), denoted by sWBS). In my simulations, it is observed that the sWBS estimator consistently outperforms or is comparable to the other estimators under the misspecified PS model. The sWBS, as well as WBS or ABS described in my paper, do not assume that the overlapped units in both the nonprobability and probability reference samples are negligible, nor do they require the identification of overlap units as needed by the estimators proposed by Dr. Kim and Ms. Kwon.
    Release date: 2024-06-25
  • 17. Comments by Takumi Saegusa on “Exchangeability assumption in propensity-score based adjustment methods for population mean estimation using non-probability samples”: Causal inference, non-probability sample, and finite population
    Articles and reports: 12-001-X202400100006
    Description: In some of non-probability sample literature, the conditional exchangeability assumption is considered to be necessary for valid statistical inference. This assumption is rooted in causal inference though its potential outcome framework differs greatly from that of non-probability samples. We describe similarities and differences of two frameworks and discuss issues to consider when adopting the conditional exchangeability assumption in non-probability sample setups. We also discuss the role of finite population inference in different approaches of propensity scores and outcome regression modeling to non-probability samples.
    Release date: 2024-06-25
  • 18. Comments by Jae Kwang Kim and Yonghyun Kwon on “Exchangeability assumption in propensity-score based adjustment methods for population mean estimation using non-probability samples”
    Articles and reports: 12-001-X202400100007
    Description: Pseudo weight construction for data integration can be understood in the two-phase sampling framework. Using the two-phase sampling framework, we discuss two approaches to the estimation of propensity scores and develop a new way to construct the propensity score function for data integration using the conditional maximum likelihood method. Results from a limited simulation study are also presented.
    Release date: 2024-06-25
  • 19. Exchangeability assumption in propensity-score based adjustment methods for population mean estimation using non-probability samples
    Articles and reports: 12-001-X202400100008
    Description: Nonprobability samples emerge rapidly to address time-sensitive priority topics in different areas. These data are timely but subject to selection bias. To reduce selection bias, there has been wide literature in survey research investigating the use of propensity-score (PS) adjustment methods to improve the population representativeness of nonprobability samples, using probability-based survey samples as external references. Conditional exchangeability (CE) assumption is one of the key assumptions required by PS-based adjustment methods. In this paper, I first explore the validity of the CE assumption conditional on various balancing score estimates that are used in existing PS-based adjustment methods. An adaptive balancing score is proposed for unbiased estimation of population means. The population mean estimators under the three CE assumptions are evaluated via Monte Carlo simulation studies and illustrated using the NIH SARS-CoV-2 seroprevalence study to estimate the proportion of U.S. adults with COVID-19 antibodies from April 01-August 04, 2020.
    Release date: 2024-06-25
  • 20. Authors’ response to comments on “Exploring the assumption that commercial online nonprobability survey respondents are answering in good faith”
    Articles and reports: 12-001-X202400100009
    Description: Our comments respond to discussion from Sen, Brick, and Elliott. We weigh the potential upside and downside of Sen’s suggestion of using machine learning to identify bogus respondents through interactions and improbable combinations of variables. We join Brick in reflecting on bogus respondents’ impact on the state of commercial nonprobability surveys. Finally, we consider Elliott’s discussion of solutions to the challenge raised in our study.
    Release date: 2024-06-25
Journals and periodicals (135)

Journals and periodicals (135) (0 to 10 of 135 results)

  • 1. The Business and Community Newsletter
    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: 2024-07-22
  • 2. Statistics Canada - Infographics
    Journals and periodicals: 11-627-M
    Description: Every year, Statistics Canada collects data from hundreds of surveys. As the amount of data gathered increases, Statistics Canada has introduced infographics to help people, business owners, academics, and management at all levels, understand key information derived from the data. Infographics can be used to quickly communicate a message, to simplify the presentation of large amounts of data, to see data patterns and relationships, and to monitor changes in variables over time.

    These infographics will provide a quick overview of Statistics Canada survey data.

    Release date: 2024-07-18
  • 3. Health Reports
    Journals and periodicals: 82-003-X
    Geography: Canada
    Description:

    Health Reports, published by the Health Analysis Division of Statistics Canada, is a peer-reviewed journal of population health and health services research. It is designed for a broad audience that includes health professionals, researchers, policymakers, and the general public. The journal publishes articles of wide interest that contain original and timely analyses of national or provincial/territorial surveys or administrative databases. New articles are published electronically each month.

    Health Reports had an impact factor of 5.0 for 2022 and a five-year impact factor of 5.6. All articles are indexed in PubMed. Our online catalogue is free and receives more than 700,000 visits per year. External submissions are welcome.
    Release date: 2024-07-17
  • 4. Data Literacy Training Products
    Journals and periodicals: 89-20-0006
    Description: Statistics Canada is committed to sharing our knowledge and expertise to help all Canadians develop their data literacy skills by developing a series of data literacy training resources. Data literacy is a key skill needed in the 21st century. It is generally described as the ability to derive meaning from data. Data literacy focuses on the competencies or skills involved in working with data, including the ability to read, analyze, interpret, visualize data, as well as to drive good decision-making.
    Release date: 2024-07-16
  • 5. Census of Population concept videos
    Journals and periodicals: 98-20-0003
    Description: Once every five years, the Census of Population provides a detailed and comprehensive statistical portrait of Canada that is vital to our country. It is the primary source of sociodemographic data for specific population groups such as lone-parent families, Indigenous peoples, immigrants, seniors and language groups.

    In order to help users of census products to better understand the various Census of Population concepts, Statistics Canada has developed, in the context of the activities of the 2021 Census and previous censuses, a collection of short videos. These videos are a reference source for users who are new to census concepts or those who have some experience with these concepts, but may need a refresher or would like to expand their knowledge.

    Release date: 2024-07-10
  • 6. Reports on Disability and Accessibility in Canada
    Journals and periodicals: 89-654-X
    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.
    Release date: 2024-07-08
  • 7. Aviation
    Table: 51-004-X
    Description: This bulletin presents the most up-to-date available information extracted from all of the Aviation Statistics Centre's surveys. Regular features include releases on principal statistics for Canada's major air carriers, airport data, fare basis statistics and traffic data for Canada's most important markets.
    Release date: 2024-07-04
  • 8. Statistics Canada International Symposium Series: Proceedings
    Journals and periodicals: 11-522-X
    Description: Since 1984, an annual international symposium on methodological issues has been sponsored by Statistics Canada. Proceedings have been available since 1987.
    Release date: 2024-06-28
  • 9. Labour Statistics: Technical Papers
    Journals and periodicals: 75-005-M
    Description: The papers in this series cover a variety of technical topics related to the Centre for Labour Market Information programs, such as the Labour Force Survey, the Survey of Employment Payrolls and Hours, the Employment insurance Coverage Survey, the Employment Insurance Statistics program as well as data from administrative sources.
    Release date: 2024-06-27
  • 10. Economic and Social Reports
    Journals and periodicals: 36-28-0001
    Description: Economic and Social Reports includes in-depth research, brief analyses, and current economic updates on a variety of topics, such as labour, immigration, education and skills, income mobility, well-being, aging, firm dynamics, productivity, economic transitions, and economic geography. All the papers are institutionally reviewed and the research and analytical papers undergo peer review to ensure that they conform to Statistics Canada's mandate as a governmental statistical agency and adhere to generally accepted standards of good professional practice.
    Release date: 2024-06-26
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