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All (24,389)

All (24,389) (60 to 70 of 24,389 results)

  • 61. Census families by total income, family type and number of children
    Table: 11-10-0013-01
    Geography: Province or territory, Census metropolitan area, Census agglomeration, Census metropolitan area part, Census agglomeration part
    Frequency: Annual
    Description: Families of tax filers; Census families by total income, family type and number of children (final T1 Family File; T1FF).
    Release date: 2024-06-27
  • 62. Sources of income by census family type
    Table: 11-10-0014-01
    Geography: Province or territory, Census metropolitan area, Census agglomeration, Census metropolitan area part, Census agglomeration part
    Frequency: Annual
    Description: Families of tax filers; Sources of income by census family type (final T1 Family File; T1FF).
    Release date: 2024-06-27
  • 63. Census families by family type and family composition including before and after-tax median income of the family
    Table: 11-10-0017-01
    Geography: Province or territory, Census metropolitan area, Census agglomeration, Census metropolitan area part, Census agglomeration part
    Frequency: Annual
    Description:

    Families of tax filers; Census families by family type and family composition including before and after-tax median income of the family (final T1 Family File; T1FF).

    Release date: 2024-06-27
  • 64. After-tax low income status of tax filers and dependants based on Census Family Low Income Measure (CFLIM-AT), by family type and family type composition
    Table: 11-10-0018-01
    Geography: Province or territory, Census metropolitan area, Census agglomeration, Census metropolitan area part, Census agglomeration part
    Frequency: Annual
    Description:

    Individuals; After-tax low income status of tax filers and dependants based on Census Family Low Income Measure (CFLIM-AT), by family type and family type composition (final T1 Family File; T1FF).

    Release date: 2024-06-27
  • 65. Economic dependency profile of census families by family type and source of income
    Table: 11-10-0019-01
    Geography: Province or territory, Census metropolitan area, Census agglomeration, Census metropolitan area part, Census agglomeration part
    Frequency: Annual
    Description: Families of tax filers; Economic dependency profile of census families by family type and source of income (final T1 Family File; T1FF).
    Release date: 2024-06-27
  • 66. After-tax low income status of census families based on Census Family Low Income Measure (CFLIM-AT), by family type and family composition
    Table: 11-10-0020-01
    Geography: Province or territory, Census metropolitan area, Census agglomeration, Census metropolitan area part, Census agglomeration part
    Frequency: Annual
    Description:

    Families of tax filers; After-tax low income status of census families based on Census Family Low Income Measure (CFLIM-AT), by family type and family composition (final T1 Family File; T1FF).

    Release date: 2024-06-27
  • 67. Labour income profile of census families by family type
    Table: 11-10-0022-01
    Geography: Province or territory, Census metropolitan area, Census agglomeration, Census metropolitan area part, Census agglomeration part
    Frequency: Annual
    Description: Families of tax filers; Labour income profile of census families by family type (final T1 Family File; T1FF).
    Release date: 2024-06-27
  • 68. Tax filers and dependants 15 years of age and over with labour income by sex and age
    Table: 11-10-0023-01
    Geography: Province or territory, Census metropolitan area, Census agglomeration, Census metropolitan area part, Census agglomeration part
    Frequency: Annual
    Description: Individuals; Tax filers and dependants 15 years of age and over with labour income by sex and age groups (final T1 Family File; T1FF).
    Release date: 2024-06-27
  • 69. Tax filers and dependants 15 years of age and over receiving employment insurance by age and sex
    Table: 11-10-0027-01
    Geography: Canada, Province or territory, Census metropolitan area, Census agglomeration, Census metropolitan area part, Census agglomeration part
    Frequency: Annual
    Description: Individuals; Tax filers and dependants 15 years of age and over receiving employment insurance by age groups and sex (final T1 Family File; T1FF).
    Release date: 2024-06-27
  • 70. Single-earner and dual-earner census families by number of children
    Table: 11-10-0028-01
    Geography: Province or territory, Census metropolitan area, Census agglomeration, Census metropolitan area part, Census agglomeration part
    Frequency: Annual
    Description: Families of tax filers; Single-earner and dual-earner census families by number of children (final T1 Family File; T1FF).
    Release date: 2024-06-27
Data (12,031)

Data (12,031) (0 to 10 of 12,031 results)

  • 1. 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: 2024-06-28
  • 2. National Address Register
    Profile of a community or region: 46-26-0002
    Description: The National Address Register (NAR) is a list of commercial and residential addresses in Canada that are extracted from Statistics Canada's Building Register and deemed non-confidential.
    Release date: 2024-06-28
  • 3. Statistics Canada - Data Visualization Products
    Table: 71-607-X
    Description: Statistics Canada produces a variety of interactive visualization tools that present data in a graphical form. These tools provide a useful way of interpreting trends behind our data on various social and economic topics.
    Release date: 2024-06-28
  • 4. Financial market statistics, last Wednesday unless otherwise stated, Bank of Canada
    Table: 10-10-0122-01
    Geography: Canada
    Frequency: Monthly
    Description: This table contains 71 series, with data starting from 1934 (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 ...), Rates (71 items: Bank rate; last Tuesday or last Thursday; Bank rate; Chartered bank administered interest rates - prime business; Chartered bank - consumer loan rate ...).
    Release date: 2024-06-28
  • 5. Selected United States dollar interest rates, Wednesdays, Bank of Canada
    Table: 10-10-0138-01
    Frequency: Weekly
    Description: This table contains 12 series, with data starting from 1954 (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 item: United States); Rates (12 items: Federal Reserve Bank of New York - discount rate; Prime rate charged by banks; Federal funds rate;Commercial paper, adjusted: 1 month; ...).
    Release date: 2024-06-28
  • 6. Bank of Canada, money market and other interest rates
    Table: 10-10-0139-01
    Geography: Canada
    Frequency: Daily
    Description: This table contains 39 series, with data for starting from 1991 (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 item: Canada); Financial market statistics (39 items: Government of Canada Treasury Bills, 1-month (composite rates); Government of Canada Treasury Bills, 2-month (composite rates); Government of Canada Treasury Bills, 3-month (composite rates);Government of Canada Treasury Bills, 6-month (composite rates); ...).
    Release date: 2024-06-28
  • 7. Fisher commodity price index, United States dollar terms, Bank of Canada, Fisher commodity price index, United States dollar terms, Bank of Canada, weekly
    Table: 10-10-0143-01
    Geography: Canada
    Frequency: Weekly
    Description: This table contains 7 series, with data starting from 1972 (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 item: Canada), Commodity (7 items: Total, all commodities; Total excluding energy; Energy; Metals and Minerals; ...).
    Release date: 2024-06-28
  • 8. Financial market statistics, as at Wednesday, Bank of Canada
    Table: 10-10-0145-01
    Geography: Canada
    Frequency: Weekly
    Description: This table contains 38 series, with data starting from 1957 (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 item: Canada), Rates (38 items: Bank rate; Chartered bank administered interest rates - prime business; Chartered bank - consumer loan rate; Forward premium or discount (-), United States dollars in Canada: 1 month; ...).
    Release date: 2024-06-28
  • 9. Financial Soundness Indicators, Canada
    Table: 10-10-0146-01
    Geography: Canada
    Frequency: Quarterly
    Description:

    This table includes six Financial Soundness Indicators for Canada as provided by the Bank of Canada. These Indicators are compiled and disseminated with other Indicators as part of the Special Data Dissemination Standard Plus from the International Monetary Fund.

    Release date: 2024-06-28
  • 10. Tobacco, sales and inventories, monthly production
    Table: 16-10-0044-01
    Geography: Canada
    Frequency: Monthly
    Description: Tobacco products (cigarettes, cigars, manufactured tobacco, fine cut, manufactured tobacco, pipe tobacco), monthly production, sales (total, domestic, to ships, air stores and foreign embassies in Canada) and inventories for Canada.
    Release date: 2024-06-28
Analysis (9,992)

Analysis (9,992) (20 to 30 of 9,992 results)

  • 21. 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
  • 22. 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
  • 23. 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
  • 24. 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
  • 25. 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
  • 26. 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
  • 27. 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
  • 28. 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
  • 29. 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
  • 30. 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
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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