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  • 1. 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
  • 2. 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
  • 3. 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
  • 4. 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
  • 5. 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
  • 6. 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
  • 7. 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
  • 8. 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
  • 9. 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
  • 10. 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
Data (12,024)

Data (12,024) (11,950 to 11,960 of 12,024 results)

Analysis (9,984)

Analysis (9,984) (20 to 30 of 9,984 results)

  • 21. Quality of early-career employment for Indigenous graduates with a bachelor’s degree
    Stats in brief: 11-627-M2024029
    Description: The infographic uses data from the integrated file of the Postsecondary Student Information System, the 2016 Census, the 2021 Census and the T1 Family File to compare the job quality of Indigenous graduates with a bachelor's degree with that of non-racialized and non-Indigenous graduates two years after graduation. Job quality indicators include employment income, unionization rate, and employer pension plan coverage rate.
    Release date: 2024-06-24
  • 22. Natural resource indicators first quarter 2024
    Stats in brief: 11-001-X202417616361
    Description: Release published in The Daily – Statistics Canada’s official release bulletin
    Release date: 2024-06-24
  • 23. Population projections: Canada, provinces and territories 2023 to 2073
    Stats in brief: 11-001-X20241764039
    Description: Release published in The Daily – Statistics Canada’s official release bulletin
    Release date: 2024-06-24
  • 24. Industrial product and raw materials price indexes May 2024
    Stats in brief: 11-001-X20241733556
    Description: Release published in The Daily – Statistics Canada’s official release bulletin
    Release date: 2024-06-21
  • 25. Retail trade April 2024
    Stats in brief: 11-001-X20241733660
    Description: Release published in The Daily – Statistics Canada’s official release bulletin
    Release date: 2024-06-21
  • 26. Employment Insurance April 2024
    Stats in brief: 11-001-X20241723537
    Description: Release published in The Daily – Statistics Canada’s official release bulletin
    Release date: 2024-06-20
  • 27. Analysis on artificial intelligence use by businesses in Canada, second quarter of 2024
    Articles and reports: 11-621-M2024008
    Description: This article explores results from the survey related to the use of AI in producing goods and delivering services. Furthermore, this article explains the specific types of AI being used, such as machine learning, virtual agents and voice recognition, as well as the impact of AI adoption on tasks performed by employees and on employment levels. It involves an examination of the data produced by the Canadian Survey on Business Conditions.
    Release date: 2024-06-20
  • 28. International postsecondary students in Canada: 2020 graduates
    Stats in brief: 11-627-M2024027
    Description: This infographic provides details about the number of graduates and median employment income two years after graduation for international postsecondary students, by educational qualification and field of study.
    Release date: 2024-06-20
  • 29. Analysis in Brief
    Journals and periodicals: 11-621-M
    Geography: Canada
    Description: The papers published in the Analysis in Brief analytical series shed light on current economic issues. Aimed at a general audience, they cover a wide range of topics including National Accounts, business enterprises, trade, transportation, agriculture, the environment, manufacturing, science and technology, services, etc.
    Release date: 2024-06-20
  • 30. The impacts of extreme heat events on non-accidental, cardiovascular, and respiratory mortality: An analysis of 12 Canadian cities from 2000 to 2020
    Articles and reports: 82-003-X202400600001
    Description: Extreme heat has significant impacts on mortality. In Canada, past research has analyzed the degree to which non-accidental mortality increases during single extreme heat events; however, few studies have considered multiple causes of death and the impacts of extreme heat events on mortality over longer time periods. This study analyzes the impacts of extreme heat events on nonaccidental, cardiovascular, and respiratory deaths from 2000 to 2020 in 12 of the largest cities in Canada.
    Release date: 2024-06-19
Reference (1,891)

Reference (1,891) (40 to 50 of 1,891 results)

  • 41. 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: 2023-07-13
  • 42. 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: 2023-07-13
  • 43. T1 Family File, Final Estimates, 2021
    Surveys and statistical programs – Documentation: 72-212-X2023001
    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: 2023-07-12
  • 44. 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: 2023-07-12
  • 45. 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: 2023-07-12
  • 46. 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: 2023-07-12
  • 47. 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: 2023-07-12
  • 48. Place of Birth, Generation Status, Citizenship and Immigration Reference Guide, Census of Population, 2021
    Surveys and statistical programs – Documentation: 98-500-X2021007
    Description:

    This reference guide provides information to help users effectively use and interpret place of birth, generation status, citizenship and immigration data from the 2021 Census. This guide contains definitions and explanations of concepts, questions, classifications, data quality and comparability with other sources for this topic.

    Release date: 2023-06-21
  • 49. Census of Population Reference Guide
    Surveys and statistical programs – Documentation: 98-500-X
    Description:

    Provides information that enables users to effectively use, apply and interpret data from the Census of Population. Each guide contains definitions and explanations on census concepts as well as a data quality and historical comparability section. Additional information will be included for specific variables to help users better understand the concepts and questions used in the census.

    Release date: 2023-06-21
  • 50. Guide to the Job Vacancy and Wage Survey, 2023 Archived
    Surveys and statistical programs – Documentation: 75-514-G2023001
    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: 2023-05-25
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