Analysis

Skip to filters. View results.

What’s new on our website

View results

Filter results by

Search Help
Currently selected filters that can be removed

Keyword(s)

Subject

Year of publication

49 facets displayed. 0 facets selected.

Author(s)

26 facets displayed. 0 facets selected.

Survey or statistical program

497 facets displayed. 0 facets selected.

Content

1 facets displayed. 0 facets selected.
Sort Help
entries

Results

All (10,025)

All (10,025) (0 to 10 of 10,025 results)

  • 1. Wholesale trade June 2024
    Stats in brief: 11-001-X20242283647
    Description: Release published in The Daily – Statistics Canada’s official release bulletin
    Release date: 2024-08-15
  • 2. Nearly half of Canadians report that rising prices are greatly impacting their ability to meet day-to-day expenses
    Stats in brief: 11-001-X202422837971
    Description: Release published in The Daily – Statistics Canada’s official release bulletin
    Release date: 2024-08-15
  • 3. Overview of the Education and Labour Market Longitudinal Platform and Associated Datasets, 2024
    Articles and reports: 37-20-00012024002
    Description: This technical reference guide (updated to include the 2024 datasets) is intended for users of the Education and Labour Market Longitudinal Platform (ELMLP). The data for the products associated with this issue are derived from integrating Postsecondary Student Information System (PSIS) administrative data with other administrative data on earnings. Statistics Canada has derived a series of annual indicators on the labour market outcomes of public postsecondary graduates including median employment income by educational qualification, field of study, age group and gender for Canada, the provinces and the territories combined.
    Release date: 2024-08-15
  • 4. Indigenous Peoples Survey: First Nations children living off reserve, Métis children and Inuit children and their families 2022
    Stats in brief: 11-001-X202422737724
    Description: Release published in The Daily – Statistics Canada’s official release bulletin
    Release date: 2024-08-14
  • 5. Study: The evolving landscape of Canadian lending: Key trends in mortgage and non-mortgage loans
    Stats in brief: 11-001-X202422738588
    Description: Release published in The Daily – Statistics Canada’s official release bulletin
    Release date: 2024-08-14
  • 6. The evolving landscape of Canadian lending: Key trends in mortgage and non-mortgage loans
    Articles and reports: 11-621-M2024009
    Description: This analysis is divided into three sections. Section 1 looks at the principal components of non-mortgage loans, while section 2 looks at mortgage loans and the breakdown of insured and uninsured mortgages. Section 3 looks at both mortgage and non-mortgage loans and highlights indicators related to household indebtedness and financial stability.
    Release date: 2024-08-14
  • 7. Why Economists Are Sounding the Alarm on Productivity
    Stats in brief: 45-20-00032024005
    Description: Canada's workforce is among the most educated in the world. But when it comes to worker productivity, we've seen a real slump over the past few years. The quarterly data published by StatCan in June 2024 confirms Canadian workers are continuing to underperform compared to our neighbours to the south. This comes as no surprise to this episode's guest, Guy Gellatly, Chief Economic Advisor at StatCan. The latest quarterly numbers are a continuation of an ongoing decline in Canada's productivity that economists have been tracking for years. 

    But what factors influence worker productivity? And why does it matter if Canadians are less productive? As a matter of face, what even is productivity? In this episode, we asked Guy to help us understand how we got to this point and why it matters for Canadians.
    Release date: 2024-08-14
  • 8. First Nations children living off reserve, Métis children, and Inuit children and their families: Selected findings from the 2022 Indigenous Peoples Survey
    Articles and reports: 89-653-X2024001
    Description: This analytical report presents selected findings from the 2022 Indigenous Peoples Survey (formerly called the Aboriginal Peoples Survey). The 2022 IPS represents the sixth cycle of the survey and focuses on Indigenous children and their families. This report covers First Nations children living off reserve, Métis children and Inuit children aged 1 to 14, and includes topics such as sociodemographic characteristics, food security and basic needs, Indigenous languages and culture, child care and a number of health indicators. Disaggregated data by gender, age groups, and geography (provinces and territories, inside and ouside Inuit Nunangat, urban and rural) are presented when possible. The report also includes comparisons to the 2006 Aboriginal Children's Survey, the 2006 Aboriginal Peoples Survey, and the 2019 Canadian Health Survey of Children and Youth.
    Release date: 2024-08-14
  • 9. 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: 2024-08-14
  • 10. 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: 2024-08-14
Stats in brief (2,682)

Stats in brief (2,682) (0 to 10 of 2,682 results)

Articles and reports (7,021)

Articles and reports (7,021) (30 to 40 of 7,021 results)

  • 31. 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
  • 32. 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
  • 33. 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
  • 34. 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
  • 35. 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
  • 36. 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
  • 37. 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
  • 38. 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
  • 39. 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
  • 40. 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
Journals and periodicals (322)

Journals and periodicals (322) (40 to 50 of 322 results)

  • 41. Demographic Documents
    Journals and periodicals: 91F0015M
    Geography: Canada
    Description: Demographic documentsis a series of texts intended for scholars and researchers, published occasionally by the Demography Division of Statistics Canada for their methodological, analytical or descriptive interest in the population field.
    Release date: 2024-02-02
  • 42. Analytical Studies: Methods and References
    Journals and periodicals: 11-633-X
    Description: Papers in this series provide background discussions of the methods used to develop data for economic, health, and social analytical studies at Statistics Canada. They are intended to provide readers with information on the statistical methods, standards and definitions used to develop databases for research purposes. All papers in this series have undergone peer and institutional review to ensure that they conform to Statistics Canada's mandate and adhere to generally accepted standards of good professional practice.
    Release date: 2024-01-22
  • 43. Sexual Misconduct in the Canadian Armed Forces
    Journals and periodicals: 85-603-X
    Description: This article presents results from the first Survey of Sexual Misconduct in the Canadian Armed Forces. Namely, the prevalence of general sexualized behaviour in the workplace; discrimination on the basis of sex, sexual orientation, or gender identity; personal experiences of discrimination or sexualized behaviour; the prevalence of sexual assault; and knowledge of policies on sexual misconduct and perceptions of responses to sexual misconduct are examined. Where possible, results are analyzed by sex, environmental command, type of service, age, rank, and number of years of service.
    Release date: 2023-12-05
  • 44. Juristat Bulletin—Quick Fact
    Journals and periodicals: 85-005-X
    Geography: Canada
    Description: This publication features short, informative articles focusing on specific justice-related issues. For more in-depth articles on justice in Canada, see also Juristat, Catalogue no. 85-002-X.
    Release date: 2023-12-04
  • 45. VISTA on the Agri-food Industry and the Farm Community
    Journals and periodicals: 21-004-X
    Geography: Canada
    Description:

    Each issue contains a short article highlighting statistical insights on themes relating to agriculture, food and rural issues.

    Release date: 2023-11-30
  • 46. Report on Energy Supply and Demand in Canada: Explanatory Information
    Table: 57-003-X
    Description: This publication presents energy balance sheets in natural units and heat equivalents in primary and secondary forms, by province. Each balance sheet shows data on production, trade, interprovincial movements, conversion and consumption by sector. Analytical tables and details on non-energy products are also included. It includes explanatory notes, a historical energy summary table and data analysis. The publication also presents data on natural gas liquids, electricity generated from fossil fuels, solid wood waste and spent pulping liquor.
    Release date: 2023-11-20
  • 47. Statistics Canada: Departmental Sustainable Development Strategy, 2023 to 2027
    Journals and periodicals: 45-26-0001
    Description: The Departmental Sustainable Development Strategy (DSDS) outlines departmental actions, with measurable performance indicators, that support the implementation strategies of the 2022-2026 Federal Sustainable Development Strategy. The DSDS further outlines Statistics Canada’s sustainable development vision to produce data to help track whether Canada is moving toward a more sustainable future and highlights projects with links to supporting sustainable development goals.
    Release date: 2023-11-14
  • 48. Household Expenditures Research Paper Series
    Journals and periodicals: 62F0026M
    Description: This series provides detailed documentation on the issues, concepts, methodology, data quality and other relevant research related to household expenditures from the Survey of Household Spending, the Homeowner Repair and Renovation Survey and the Food Expenditure Survey.
    Release date: 2023-10-18
  • 49. Statistical Methodology Research and Development Program Achievements
    Journals and periodicals: 12-206-X
    Description: This report summarizes the annual achievements of the Methodology Research and Development Program (MRDP) sponsored by the Modern Statistical Methods and Data Science Branch at Statistics Canada. This program covers research and development activities in statistical methods with potentially broad application in the agency’s statistical programs; these activities would otherwise be less likely to be carried out during the provision of regular methodology services to those programs. The MRDP also includes activities that provide support in the application of past successful developments in order to promote the use of the results of research and development work. Selected prospective research activities are also presented.
    Release date: 2023-10-11
  • 50. Environment Accounts and Statistics Analytical and Technical Paper Series
    Journals and periodicals: 16-001-M
    Description: The series covers environment accounts and indicators, environmental surveys, spatial environmental information and other research related to environmental statistics. The technical paper series is intended to stimulate discussion on a range of environmental topics.
    Release date: 2023-09-13
Report a problem or mistake on this page
Date modified:

Search help

How do I use the filters and the search box?

  • You can initiate a search by entering keywords or selecting filters (e.g., under Subject, Geography, etc.) on the left side of the page.

  • Filters can be used together or in any combination. Each time you select a filter, the results on the page are updated.

  • To start a new search, click on the Clear all button above the search box or deselect each filter.

  • Specified keywords and filters appear above the search box. You can deselect any or all the tags to refine or clear your search.

How do I refine my search?

  • You can enter keywords in the search box. It is not necessary to use “+” or “, “ or “AND”.

  • You can delete some or all of the keywords from your search string.

  • Quotes around keywords limit the search to a specific phrase.

    • For example, “labour force survey” will limit results to products containing this phrase.

  • Use “or” between keywords to obtain results that contain one or more search terms.

    • For example, labour or­ force or survey will limit results to products containing any or all of these words.

How does the search work?

  • This search looks for results that have your query term in the title, description, subject, geography, product number or other information about the product.

    • For example, when searching the term ‘Diseases’, all results with the word ‘Diseases’ in the title, description or tagged the subject appear.

  • The text within an article or publication are not searched. For full text searching of articles, use the site search.

Sort help

How are the results ordered?

The Sort by feature provides three options:

  • By date – sorts results by product release date starting with the most recent.
  • By relevance is only needed or beneficial when you use the search box and submit a search query. Results are ordered by a combination of where the search term appears in the content, e.g., if it appears in the title, it likely means the product is more relevant to your search; how up to date or new the content in the product is; how popular the product is; and other factors.
  • By title – sorts results alphabetically.

Sort help

How are the results ordered?

The Sort by feature provides four options:

  • By date – sorts results by product release date starting with the most recent.
  • By most popular – gives priority to results that are most frequently selected by users, followed by the most recent.
  • By relevance is only needed or beneficial when you use the search box and submit a search query. Results are ordered by a combination of where the search term appears in the content, e.g., if it appears in the title, it likely means the product is more relevant to your search; how up to date or new the content in the product is; how popular the product is; and other factors.
  • By title – sorts results alphabetically.