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- ESDC Employee Wellness Survey (1)
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Results
All (10,026)
All (10,026) (70 to 80 of 10,026 results)
- Stats in brief: 11-001-X202419423503Description: Release published in The Daily – Statistics Canada’s official release bulletinRelease date: 2024-07-12
- Stats in brief: 98-20-00032021031Description: This video is part of a series that is designed to give you a basic understanding of the Census of Population web pages. This video will provide an overview of the major links and products on the main page that are available to all users.Release date: 2024-07-10
- Stats in brief: 98-20-00032021032Description: This video is part of a series that is designed to give you a basic understanding of the Census of Population web pages. The purpose of this video is to explain where to find the most popular standard data product of the Census of Population, the 2021 Census Profile, and how to filter the data.Release date: 2024-07-10
- Stats in brief: 98-20-00032021033Description: This video is part of a series that is designed to give you a basic understanding of the Census of Population web pages. The purpose of this video is to explain how to add geographies in the 2021 Census Profile and to present the various downloading options to see the data.Release date: 2024-07-10
- Journals and periodicals: 98-20-0003Description: Once every five years, the Census of Population provides a detailed and comprehensive statistical portrait of Canada that is vital to our country. It is the primary source of sociodemographic data for specific population groups such as lone-parent families, Indigenous peoples, immigrants, seniors and language groups.
In order to help users of census products to better understand the various Census of Population concepts, Statistics Canada has developed, in the context of the activities of the 2021 Census and previous censuses, a collection of short videos. These videos are a reference source for users who are new to census concepts or those who have some experience with these concepts, but may need a refresher or would like to expand their knowledge.
Release date: 2024-07-10 - Stats in brief: 11-001-X202419138647Description: Release published in The Daily – Statistics Canada’s official release bulletinRelease date: 2024-07-09
- Articles and reports: 89-654-X2024002Description: Using data from the 2022 Canadian Survey on Disability (CSD), this factsheet examines the experiences of 2SLGBTQ+ persons with disabilities. It provides information on various sociodemographic and disability characteristics, such as age, disability type, severity of disability, and employment. It also includes comparisons to the non-2SLGBTQ+ persons with disabilities population by age group.Release date: 2024-07-08
- Journals and periodicals: 89-654-XDescription: The Canadian Survey on Disability (CSD) is a national survey of Canadians aged 15 and over whose everyday activities are limited because of a long-term condition or health-related problem.Release date: 2024-07-08
- Articles and reports: 51-004-X2024001Description: This report presents statistics on airline traffic such as the volume of passengers and cargo at Canadian airports.Release date: 2024-07-04
- 80. AviationTable: 51-004-XDescription: This bulletin presents the most up-to-date available information extracted from all of the Aviation Statistics Centre's surveys. Regular features include releases on principal statistics for Canada's major air carriers, airport data, fare basis statistics and traffic data for Canada's most important markets.Release date: 2024-07-04
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Stats in brief (2,683)
Stats in brief (2,683) (50 to 60 of 2,683 results)
- Stats in brief: 11-001-X202417616361Description: Release published in The Daily – Statistics Canada’s official release bulletinRelease date: 2024-06-24
- Stats in brief: 11-001-X20241764039Description: Release published in The Daily – Statistics Canada’s official release bulletinRelease date: 2024-06-24
- Stats in brief: 11-627-M2024027Description: 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
- Stats in brief: 11-001-X202417011703Description: Release published in The Daily – Statistics Canada’s official release bulletinRelease date: 2024-06-18
- Stats in brief: 11-001-X202417037886Description: Release published in The Daily – Statistics Canada’s official release bulletinRelease date: 2024-06-18
- Stats in brief: 11-001-X202416636525Description: Release published in The Daily – Statistics Canada’s official release bulletinRelease date: 2024-06-14
- Stats in brief: 11-001-X202416538104Description: Release published in The Daily – Statistics Canada’s official release bulletinRelease date: 2024-06-13
- Stats in brief: 11-001-X20241655421Description: Release published in The Daily – Statistics Canada’s official release bulletinRelease date: 2024-06-13
- Stats in brief: 11-001-X20241643339Description: Release published in The Daily – Statistics Canada’s official release bulletinRelease date: 2024-06-12
- Stats in brief: 11-001-X20241643631Description: Release published in The Daily – Statistics Canada’s official release bulletinRelease date: 2024-06-12
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Articles and reports (7,021)
Articles and reports (7,021) (30 to 40 of 7,021 results)
- Articles and reports: 12-001-X202400100004Description: 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
- Articles and reports: 12-001-X202400100005Description: 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
- Articles and reports: 12-001-X202400100006Description: 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
- Articles and reports: 12-001-X202400100007Description: 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
- Articles and reports: 12-001-X202400100008Description: 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
- Articles and reports: 12-001-X202400100009Description: 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
- Articles and reports: 12-001-X202400100010Description: 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
- Articles and reports: 12-001-X202400100011Description: 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
- Articles and reports: 12-001-X202400100012Description: 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
- Articles and reports: 12-001-X202400100013Description: 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
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Journals and periodicals (322)
Journals and periodicals (322) (0 to 10 of 322 results)
- Journals and periodicals: 11-621-MGeography: CanadaDescription: 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
- Journals and periodicals: 45-20-0003Description: 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
- Journals and periodicals: 89-653-XDescription: The Indigenous Peoples Survey (IPS) is a national survey on the social and economic conditions of First Nations people living off reserve, Métis and Inuit. The objectives of the IPS are to identify the needs of these Indigenous groups and to inform policy and programming activities aimed at improving the well-being of Indigenous peoples. The IPS aims to provide current and relevant data for a variety of stakeholders, including Indigenous organizations, communities, service providers, researchers, governments and the general public. The 2017 APS represents the fifth cycle of the survey and focuses on participation in the Canadian economy, transferable skills, practical training, use of information technology and Indigenous language attainment of First Nations people living off reserve, Métis and Inuit aged 15 years and over. The 2022 IPS represents the sixth cycle of the survey and focuses on families and children including child care, access to services, family stability, intergenerational trauma and discrimination, sense of belonging, and Indigenous languages and culture, of First Nations people living off reserve, Métis and Inuit aged 1 year and over.Release date: 2024-08-14
- Journals and periodicals: 89-657-XDescription: This thematic series groups different statistical products related to ethnicity, languages, and immigration. It features analytical documents of varying scopes, such as population profiles, reference materials, data products (including tables and factsheets), among other document types.Release date: 2024-08-07
- Journals and periodicals: 75-006-XGeography: CanadaDescription: This publication brings together and analyzes a wide range of data sources in order to provide information on various aspects of Canadian society, including labour, income, education, social, and demographic issues, that affect the lives of Canadians.Release date: 2024-08-06
- Journals and periodicals: 11-627-MDescription: 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: 2024-07-30 - Journals and periodicals: 14-28-0001Description: Statistics Canada's Quality of Employment in Canada publication is intended to provide Canadians and Canadian organizations with a better understanding of quality of employment using an internationally-supported statistical framework. Quality of employment is approached as a multidimensional concept, characterized by different elements, which relate to human needs in various ways. To cover all relevant aspects, the framework identified seven dimensions and twelve sub-dimensions of quality of employment.Release date: 2024-07-25
- Journals and periodicals: 11-631-XDescription: Statistics Canada regularly prepares presentations with statistical findings about the country’s economy, society and environment. These presentations may be intended for conferences, meetings with stakeholders, or other events held throughout the year to provide Statistics Canada with an opportunity to promote the role of official statistics and to better understand data users’ needs. This series provides online access to these presentations as well as new presentations created to help communicate research findings on a wide range of subjects to a broad audience.Release date: 2024-07-24
- Journals and periodicals: 36-28-0001Description: Economic and Social Reports includes in-depth research, brief analyses, and current economic updates on a variety of topics, such as labour, immigration, education and skills, income mobility, well-being, aging, firm dynamics, productivity, economic transitions, and economic geography. All the papers are institutionally reviewed and the research and analytical papers undergo peer review to ensure that they conform to Statistics Canada's mandate as a governmental statistical agency and adhere to generally accepted standards of good professional practice.Release date: 2024-07-24
- Journals and periodicals: 11-632-XDescription: The newsletter offers information aimed at three main groups, businesses (small to medium), communities and ethno-cultural groups/communities. Articles and outreach materials will assist their understanding of national and local data from the many relevant sources found on the Statistics Canada website.Release date: 2024-07-22
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