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All (24,373) (10 to 20 of 24,373 results)

11. 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
12. 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
13. 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
14. 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 29^th Morris Hansen Lecture by Courtney Kennedy, Yan Li and Jean-François Beaumont.
Release date: 2024-06-25
15. 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
16. 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
17. Food Services and Drinking Places Sales
Data Visualization: 71-607-X2017003
Description: This web application provides access to data on the sales of food services and drinking places for Canada, provinces and territories. This dynamic application allows users to compare provincial and territorial data with interactive maps and charts. All data in this release are seasonally adjusted and expressed in current dollars.
Release date: 2024-06-25
18. Consumer Price Index Data Visualization Tool
Data Visualization: 71-607-X2018016
Description: This interactive dashboard provides access to current and historical Consumer Price Index (CPI) data in a dynamic and customizable format. Key indicators such as the 12-month and 1-month inflation rates and price trends are presented in interactive charts, allowing users to compare and analyze price changes of all the goods and services in the CPI basket over time as well as across geography (national, provincial and territorial levels).
Other CPI indicators available in this tool include the Bank of Canada’s core measures of inflation, seasonally adjusted inflation rates, and CPI basket weights.

This web-based application is updated monthly, as soon as the data for the latest reference month is released in The Daily.
Release date: 2024-06-25
19. Gender diversity of Canadian postsecondary students, 2021/2022
Stats in brief: 11-627-M2024026
Description: Using data from the Postsecondary Student Information System (PSIS) and the Census of Population, 2021, this infographic provides information on enrolment in Canadian public postsecondary institutions for transgender and non-binary people.
Release date: 2024-06-25
20. 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

Data (12,024)

Data (12,024) (20 to 30 of 12,024 results)

21. Consumer Price Index, major components and special aggregates, Canada, seasonally adjusted, percentage change
Table: 18-10-0006-02
Geography: Canada
Frequency: Monthly
Description:
Monthly indexes and percentage changes for major components and special aggregates of the Consumer Price Index (CPI), seasonally adjusted, for Canada. Data are presented for the corresponding month of the previous year, the previous month and the current month. The base year for the index is 2002=100.
Release date: 2024-06-25
22. Consumer Price Index (CPI) statistics, measures of core inflation and other related statistics - Bank of Canada definitions
Table: 18-10-0256-01
Geography: Canada
Frequency: Monthly
Description: This table contains 11 series, with data from 1949 (not all combinations necessarily have data for all years). Data are presented for the current month and previous four months. Users can select other time periods that are of interest to them.
Release date: 2024-06-25
23. Consumer Price Index (CPI) statistics, measures of core inflation - Bank of Canada definitions, year-over-year percent change
Table: 18-10-0256-02
Geography: Canada
Frequency: Monthly
Description: Consumer Price Index (CPI) statistics, measures of core inflation, Bank of Canada definitions, year-over-year percent change.
Release date: 2024-06-25
24. Monthly survey of food services and drinking places
Table: 21-10-0019-01
Geography: Canada, Province or territory
Frequency: Monthly
Description: Seasonally adjusted receipts of monthly survey of food services and drinking places, by North American Industry Classification System (NAICS), monthly, for five months of data.
Release date: 2024-06-25
25. Screened passenger traffic at the largest airports in Canada
Table: 23-10-0312-01
Frequency: Monthly
Description: Monthly screened passenger traffic at the eight largest airports in Canada. Data are derived from the Canadian Air Transport Security Authority (CATSA) Boarding Pass Security System and include screened traffic at pre-board screening checkpoints at the airports.
Release date: 2024-06-25
26. Daily average foreign exchange rates in Canadian dollars, Bank of Canada
Table: 33-10-0036-01
Geography: Canada
Frequency: Daily
Description:
This table contains 27 series, with data starting from 1981 (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); Type of currency (27 items: Australian dollar, daily average; Brazilian real, daily average; Chinese renminbi, daily average; European euro, daily average; ...).
Release date: 2024-06-25
27. Canadian monthly natural gas transmission, Canada and provinces
Table: 25-10-0058-01
Geography: Canada, Province or territory
Frequency: Monthly
Description: Natural gas received from gas fields and processing plants, imports and exports, deliveries to industrial consumers and pipeline fuel in gigajoules and cubic metres, monthly, January 2016 to present.
Release date: 2024-06-24
28. Population Projections for Canada, Provinces and Territories: Interactive Dashboard
Data Visualization: 71-607-X2022015
Description: This data visualization product provides interactive insights on the most recent population projections for Canada, provinces and territories.
Release date: 2024-06-24
29. Population Projections for Canada, Provinces and Territories
Table: 91-520-X
Description: This report presents the results of the population projections by age group and sex for Canada, the provinces and territories. These projections are based on assumptions that take into account the most recent trends relating to components of population growth, particularly fertility, mortality, immigration, emigration and interprovincial migration.

The detailed data tables are available in CODR: tables 1710005701 and 1710005801.
Release date: 2024-06-24
30. Bank of Canada, assets and liabilities, Wednesdays
Table: 10-10-0136-01
Geography: Canada
Frequency: Weekly
Description: This table contains 29 series, with data starting from 1953 (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), Assets and liabilities (29 items: Total assets; Total, Government of Canada, direct and guaranteed securities; Government of Canada, Treasury Bills; Total, Government of Canada, bonds; ...).
Release date: 2024-06-24

Analysis (9,984)

Analysis (9,984) (0 to 10 of 9,984 results)

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

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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Date modified:: 2024-06-26

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