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All (9,993) (60 to 70 of 9,993 results)

  • 61. Wholesale trade April 2024
    Stats in brief: 11-001-X20241663647
    Description: Release published in The Daily – Statistics Canada’s official release bulletin
    Release date: 2024-06-14
  • 62. Census of Environment: Renewable freshwater for selected drainage regions and ecoprovinces 1971 to 2020
    Stats in brief: 11-001-X202416636525
    Description: Release published in The Daily – Statistics Canada’s official release bulletin
    Release date: 2024-06-14
  • 63. Labour Statistics at a Glance
    Journals and periodicals: 71-222-X
    Description: Labour Statistics at a Glance features short analytical articles on specific topics of interest related to Canada's labour market. The studies examine recent or historical trends using data produced by the Centre for Labour Market Information, i.e., the Labour Force Survey, the Survey of Employment Payrolls and Hours, the Job Vacancy and Wage Survey, the Employment Insurance Coverage Survey and the Employment Insurance Statistics Program.
    Release date: 2024-06-13
  • 64. Analysis on small businesses in Canada, second quarter of 2024
    Articles and reports: 11-621-M2024007
    Description: With the proportion of small businesses making up nearly all of the employer businesses in Canada, small businesses are an important role in employing Canadians and are a significant driver towards economic recovery. This article provides insights on the expectations of small businesses as well as the unique conditions faced by these businesses in the second quarter of 2024. It involves an examination of the data produced by the Canadian Survey on Business Conditions.
    Release date: 2024-06-13
  • 65. Labour market characteristics of persons with and without disabilities 2023
    Stats in brief: 11-001-X202416538104
    Description: Release published in The Daily – Statistics Canada’s official release bulletin
    Release date: 2024-06-13
  • 66. National balance sheet and financial flow accounts first quarter 2024
    Stats in brief: 11-001-X20241655421
    Description: Release published in The Daily – Statistics Canada’s official release bulletin
    Release date: 2024-06-13
  • 67. Labour market characteristics of persons with and without disabilities, 2023
    Articles and reports: 71-222-X2024002
    Description: This article examines trends in rates of employment and unemployment, as well as hourly wages and work hours, for the year 2023, and explores how disability intersects with age, sex, educational attainment, and racialized groups to influence labour market outcomes.
    Release date: 2024-06-13
  • 68. Canada's international investment position first quarter 2024
    Stats in brief: 11-001-X20241643339
    Description: Release published in The Daily – Statistics Canada’s official release bulletin
    Release date: 2024-06-12
  • 69. Employer pension plans (trusteed pension funds) fourth quarter 2023
    Stats in brief: 11-001-X20241643631
    Description: Release published in The Daily – Statistics Canada’s official release bulletin
    Release date: 2024-06-12
  • 70. Health Research Working Paper Series
    Journals and periodicals: 82-622-X
    Geography: Canada
    Description: The Health Research Working Paper Series publishes: analytical work-in-progress; background documentation for specific research projects (e.g methodological papers); lengthy reports intended for specific clients, and; compendiums of data tables. Publication in this series does not preclude publication of specific aspects of the work in a peer-reviewed journal.
    Release date: 2024-06-11
Stats in brief (2,664)

Stats in brief (2,664) (0 to 10 of 2,664 results)

Articles and reports (7,006)

Articles and reports (7,006) (10 to 20 of 7,006 results)

  • 11. 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
  • 12. 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
  • 13. 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
  • 14. 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
  • 15. 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
  • 16. 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
  • 17. 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
  • 18. 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
  • 19. 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
  • 20. Comments by Michael R. Elliott on “Exploring the assumption that commercial online nonprobability survey respondents are answering in good faith”
    Articles and reports: 12-001-X202400100011
    Description: Kennedy, Mercer, and Lau explore misreporting by respondents in non-probability samples and discover a new feature, namely that of deliberate misreporting of demographic characteristics. This finding suggests that the “arms race” between researchers and those determined to disrupt the practice of social science is not over and researchers need to account for such respondents if using high-quality probability surveys to help reduce error in non-probability samples.
    Release date: 2024-06-25
Journals and periodicals (323)

Journals and periodicals (323) (10 to 20 of 323 results)

  • 11. Labour Statistics at a Glance
    Journals and periodicals: 71-222-X
    Description: Labour Statistics at a Glance features short analytical articles on specific topics of interest related to Canada's labour market. The studies examine recent or historical trends using data produced by the Centre for Labour Market Information, i.e., the Labour Force Survey, the Survey of Employment Payrolls and Hours, the Job Vacancy and Wage Survey, the Employment Insurance Coverage Survey and the Employment Insurance Statistics Program.
    Release date: 2024-06-13
  • 12. Health Research Working Paper Series
    Journals and periodicals: 82-622-X
    Geography: Canada
    Description: The Health Research Working Paper Series publishes: analytical work-in-progress; background documentation for specific research projects (e.g methodological papers); lengthy reports intended for specific clients, and; compendiums of data tables. Publication in this series does not preclude publication of specific aspects of the work in a peer-reviewed journal.
    Release date: 2024-06-11
  • 13. Environment Fact Sheets
    Journals and periodicals: 16-508-X
    Description: Environment fact sheets will include short, focused, single-theme analysis on key issues within the changing environment with regards to all Canadians. Over the course of the series, analysis will include topics on: air and climate, pollution and waste, environmental protection and quality, and natural resources.
    Release date: 2024-06-06
  • 14. The Eh Sayers Podcast
    Journals and periodicals: 45-20-0003
    Description: The ‘Eh Sayers’ podcast explores data of interest to Canadians, like social or news-worthy topics. It also aims to foster data literacy and deliver insight into the lives of Canadians by exploring the data the agency produces and tying it to real life situations through storytelling.
    Release date: 2024-06-06
  • 15. Spotlight on Canadians: Results from the General Social Survey
    Journals and periodicals: 89-652-X
    Geography: Canada
    Description: This publication presents key highlights and results from the General Social Survey on the topics of caregiving and care receiving; social identity; giving, volunteering and participating; victimization; time use; and family.
    Release date: 2024-06-05
  • 16. Videos - Statistics Canada
    Journals and periodicals: 11-629-X
    Description: Statistics Canada produces videos that present key communications messages to multiple publics in an easy-to-understand way. As a communications tool, they make complex information and ideas easy to interpret by telling a visual story. Statistics Canada has videos on a variety of topics.
    Release date: 2024-05-28
  • 17. 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: 2024-05-28
  • 18. 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: 2024-05-23
  • 19. Insights on Canadian Society
    Journals and periodicals: 75-006-X
    Geography: Canada
    Description: This publication brings together and analyzes a wide range of data sources in order to provide information on various aspects of Canadian society, including labour, income, education, social, and demographic issues, that affect the lives of Canadians.
    Release date: 2024-05-23
  • 20. Annual Demographic Estimates: Subprovincial Areas
    Journals and periodicals: 91-214-X
    Description: This publication presents annual estimates of population for subprovincial areas of Canada, such as census metropolitan areas (CMAs), census agglomerations (CAs), economic regions (ERs) and census divisions (CDs). The following components of population change are also presented: births, deaths, immigration, emigration, returning emigration, net temporary emigration, net non-permanent residents and interprovincial and intraprovincial migration. The estimates are based on the most recent census of population results available at the time of publication, which have been adjusted for census net undercoverage (including adjustment for incompletely enumerated Indian reserves). This publication also contains highlights and an analysis of the most recent demographic trends, as well as a description of the concepts, methods and data quality of the estimates.
    Release date: 2024-05-22
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