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All (24,399) (30 to 40 of 24,399 results)

  • 31. Building permits, by type of structure and type of work
    Table: 34-10-0285-01
    Geography: Canada, Province or territory, Census metropolitan area, Census agglomeration, Census metropolitan area part
    Frequency: Monthly
    Description: Monthly building and demolition permits value of construction by type of structure and type of work.
    Release date: 2024-07-12
  • 32. Building permits, by type of structure and type of work, percentage change
    Table: 34-10-0285-02
    Geography: Canada, Province or territory, Census metropolitan area, Census agglomeration, Census metropolitan area part
    Frequency: Monthly
    Description: Monthly building and demolition permits value of construction by type of structure and type of work, percentage change (x1,000).
    Release date: 2024-07-12
  • 33. Detailed preliminary information on cases of COVID-19, 2020-2024: 5-Dimensions (Aggregated data), Public Health Agency of Canada
    Table: 13-10-0863-01
    Geography: Canada, Geographical region of Canada
    Frequency: Occasional
    Description: In collaboration with the Public Health Agency of Canada (PHAC), this table provides Canadians and researchers with preliminary data to monitor only the confirmed cases of coronavirus (COVID-19) in Canada. Given the rapidly-evolving nature of this situation, these data are considered preliminary. This table will provide an aggregate summary of the data available in the publication 13-26-0003.
    Release date: 2024-07-12
  • 34. Detailed preliminary information on cases of COVID-19, 2020-2024: 4-Dimensions (Aggregated data), Public Health Agency of Canada
    Table: 13-10-0864-01
    Geography: Canada, Geographical region of Canada
    Frequency: Occasional
    Description: In collaboration with the Public Health Agency of Canada (PHAC), this table provides Canadians and researchers with preliminary data to monitor only the confirmed cases of coronavirus (COVID-19) in Canada. Given the rapidly-evolving nature of this situation, these data are considered preliminary. This table will provide an aggregate summary of the data available in the publication 13-26-0003.
    Release date: 2024-07-12
  • 35. Accounting Services Price Index, annual
    Table: 18-10-0021-01
    Geography: Canada
    Frequency: Annual
    Description:

    Accounting Services Price Index (ASPI) by North American Industrial Classification System (NAICS). Annual data is available from 1999. The table includes data for the most recent reference period and the last four periods. The current base period for the index is (2010=100).

    Release date: 2024-07-12
  • 36. Accounting Services Price Index, percentage change, annual
    Table: 18-10-0021-02
    Geography: Canada
    Frequency: Annual
    Description:

    Accounting Services Price Index (ASPI) by North American Industrial Classification System (NAICS). Data is available from 1999. The table presents year-to-year percentage changes for various aggregation levels. The current base period for the index is (2010=100).  

    Release date: 2024-07-12
  • 37. Preliminary dataset on confirmed cases of COVID-19, Public Health Agency of Canada
    Table: 13-26-0003
    Description:

    In collaboration with the Public Health Agency of Canada (PHAC), this data file provides Canadians and researchers with preliminary data to monitor only the confirmed cases of coronavirus (COVID-19) in Canada. Given the rapidly-evolving nature of this situation, these data are considered preliminary.

    Release date: 2024-07-12
  • 38. Building permits: Interactive Dashboard
    Data Visualization: 71-607-X2021005
    Description: Building permits: Interactive Dashboard can be used to visualize monthly data or trend analysis of the value of permits issued by Canadian municipalities as well as monthly changes on residential units created. The user can view those data by selecting reference period, geography, type of building structure and value type for seasonal adjustment.
    Release date: 2024-07-12
  • 39. Real-time Local Business Conditions Index
    Data Visualization: 71-607-X2021017
    Description: The need for alternative data sources is of growing importance for both supplementing Statistics Canada's data holdings and for nowcasting economic activity. In response to this need, Statistics Canada initiated the development of a Real-time Local Business Conditions Index (RT-LBCI). The index brings together data from a few different sources, including Google's Places API (containing data on temporary and permanent businesses closures), TomTom Real-time traffic API (road traffic data), as well as information from Statistics Canada data holdings (monthly retail and wholesale, Business Register, etc.). The project aims to compute a near real-time index of economic activity in Canadian major cities.
    Release date: 2024-07-12
  • 40. Bank of Canada, assets and liabilities, at month-end
    Table: 10-10-0108-01
    Geography: Canada
    Frequency: Monthly
    Description: This table contains 38 series, with data starting from 1935 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Assets and liabilities (38 items: Total assets; Total; Government of Canada; bonds; Total; Government of Canada; direct and guaranteed securities; Government of Canada; Treasury Bills ...).
    Release date: 2024-07-12
Data (12,036)

Data (12,036) (10 to 20 of 12,036 results)

Analysis (9,996)

Analysis (9,996) (40 to 50 of 9,996 results)

  • 41. 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
  • 42. 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
  • 43. 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
  • 44. 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
  • 45. Comments by J. Michael Brick on “Exploring the assumption that commercial online nonprobability survey respondents are answering in good faith”
    Articles and reports: 12-001-X202400100012
    Description: Nonprobability samples are quick and low-cost and have become popular for some types of survey research. Kennedy, Mercer and Lau examine data quality issues associated with opt-in nonprobability samples frequently used in the United States. They show that the estimates from these samples have serious problems that go beyond representativeness. A total survey error perspective is important for evaluating all types of surveys.
    Release date: 2024-06-25
  • 46. Exploring the assumption that commercial online nonprobability survey respondents are answering in good faith
    Articles and reports: 12-001-X202400100013
    Description: Statistical approaches developed for nonprobability samples generally focus on nonrandom selection as the primary reason survey respondents might differ systematically from the target population. Well-established theory states that in these instances, by conditioning on the necessary auxiliary variables, selection can be rendered ignorable and survey estimates will be free of bias. But this logic rests on the assumption that measurement error is nonexistent or small. In this study we test this assumption in two ways. First, we use a large benchmarking study to identify subgroups for which errors in commercial, online nonprobability samples are especially large in ways that are unlikely due to selection effects. Then we present a follow-up study examining one cause of the large errors: bogus responding (i.e., survey answers that are fraudulent, mischievous or otherwise insincere). We find that bogus responding, particularly among respondents identifying as young or Hispanic, is a significant and widespread problem in commercial, online nonprobability samples, at least in the United States. This research highlights the need for statisticians working with commercial nonprobability samples to address bogus responding and issues of representativeness – not just the latter.
    Release date: 2024-06-25
  • 47. Preface to the special issue for papers presented at the 29th Morris Hansen Lecture on the use of nonprobability samples
    Articles and reports: 12-001-X202400100014
    Description: This paper is an introduction to the special issue on the use of nonprobability samples featuring three papers that were presented at the 29th Morris Hansen Lecture by Courtney Kennedy, Yan Li and Jean-François Beaumont.
    Release date: 2024-06-25
  • 48. Monthly Survey of Manufacturing: Flash estimate May 2024
    Stats in brief: 11-001-X202417724744
    Description: Release published in The Daily – Statistics Canada’s official release bulletin
    Release date: 2024-06-25
  • 49. Consumer Price Index May 2024
    Stats in brief: 11-001-X20241773665
    Description: Release published in The Daily – Statistics Canada’s official release bulletin
    Release date: 2024-06-25
  • 50. 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: 2024-06-25
Reference (1,892)

Reference (1,892) (0 to 10 of 1,892 results)

  • 1. T1 Family File, Final Estimates, 2022
    Surveys and statistical programs – Documentation: 72-212-X2024001
    Description: Data on income of census families, individuals and seniors are derived from the T1 Family File (T1FF). This file is based on information from the T1 form, Income Tax and Benefit Return, which Statistics Canada receives from Canada Revenue Agency (CRA) thirteen months after the end of the taxation year.
    Release date: 2024-06-27
  • 2. Technical Reference Guide for the Annual Income Estimates for Census Families, Individuals and Seniors, T1 Family File
    Surveys and statistical programs – Documentation: 72-212-X
    Description: Data on income of census families, individuals and seniors are derived from income tax returns. The data for the products associated with this release are derived from the T1 file that Statistics Canada receives from Canada Revenue Agency (CRA) thirteen months after the end of the taxation year.
    Release date: 2024-06-27
  • 3. Census Subdivision Boundary File, Reference Guide
    Geographic files and documentation: 92-162-G
    Description: This reference guide is intended for users of the Census Subdivisions Boundary File. The guide provides an overview of the file, the general methodology used to create it, and important technical information for users.
    Release date: 2024-06-26
  • 4. Census Subdivision Boundary File
    Geographic files and documentation: 92-162-X
    Description: The Census Subdivision Boundary File contains the boundaries of all census subdivisions which combined cover all of Canada. A census subdivision is a municipality or an area treated as an equivalent to a municipality for statistical purposes (for example, Indian reserves and unorganized territories). The file provides a framework for mapping and spatial analysis using commercially available geographic information systems (GIS) or other mapping software.

    The Census Subdivision Boundary File is portrayed in Lambert conformal conic projection and is based on the North American Datum of 1983 (NAD83). A reference guide is available (92-162-G).

    Release date: 2024-06-26
  • 5. Road Network File, Reference Guide
    Geographic files and documentation: 92-500-G
    Description: This reference guide is intended for users of the Road Network File. The guide provides an overview of the file, the general methodology used to create it, and important technical information for users.
    Release date: 2024-06-26
  • 6. Road Network File
    Geographic files and documentation: 92-500-X
    Geography: Canada
    Description: The Road Network File (RNF) is a digital representation of Canada's national road network, containing information such as street names, types, directions and address ranges. The information comes from the National Geographic Database (NGD).

    A reference guide is available (92-500-G).

    Release date: 2024-06-26
  • 7. Interim List of Changes to Municipal Boundaries, Status, and Names
    Notices and consultations: 92F0009X
    Description: This report provides a summary of changes to municipal boundaries, status and names. The list is usually produced on an annual basis for changes that occurred during the previous year. A five year list is produced on Census of population years.
    Release date: 2024-06-26
  • 8. Population Projections for Canada, Provinces and Territories: Technical Report on Methodology and Assumptions
    Surveys and statistical programs – Documentation: 91-620-X
    Description: This report aims to describe the methods used for the calculation of projection parameters, the various projection assumptions and their rationales.
    Release date: 2024-06-24
  • 9. Guide to the Job Vacancy and Wage Survey, 2024
    Surveys and statistical programs – Documentation: 75-514-G2024001
    Description: The Guide to the Job Vacancy and Wage Survey contains a dictionary of concepts and definitions, and covers topics such as survey methodology, data collection, processing, and data quality.
    Release date: 2024-06-18
  • 10. Guide to the Job Vacancy and Wage Survey
    Surveys and statistical programs – Documentation: 75-514-G
    Description: The Guide to the Job Vacancy and Wage Survey contains a dictionary of concepts and definitions, and covers topics such as survey methodology, data collection, processing, and data quality. The guide covers both components of the survey: the job vacancy component, which is quarterly, and the wage component, which is annual.
    Release date: 2024-06-18
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