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Results
All (14)
All (14) (0 to 10 of 14 results)
- Articles and reports: 75-006-X202200100012Description:
Based on data from the Canadian Community Health Survey (CCHS) and the Canadian Census Health and Environment Cohort (CanCHEC), this study provides an understanding of suicide-related behaviours, namely suicide ideation and completed suicides, among Canadian immigrants.
Release date: 2022-12-01 - Stats in brief: 89-20-00062022004Description:
Gathering, exploring, analyzing and interpreting data are essential steps in producing information that benefits society, the economy and the environment. In this video, we will discuss the importance of considering data ethics throughout the process of producing statistical information.
As a pre-requisite to this video, make sure to watch the video titled “Data Ethics: An introduction” also available in Statistics Canada’s data literacy training catalogue.
Release date: 2022-10-17 - Stats in brief: 89-20-00062022005Description:
In this video, you will learn the answers to the following questions: What are the different types of error? What are the types of error that lead to statistical bias? Where during the data journey statistical bias can occur?
Release date: 2022-10-17 - Articles and reports: 62F0014M2022010Description: In 2021, Canada recorded its highest annual increase in the Consumer Price Index (CPI) since 1991, as global supply-chains felt the repercussions of the COVID-19 pandemic, transportation and supply disruptions, and rebounding energy prices – all alongside the effects of the climate crisis.
This analysis uses price data from the Industrial Product Price Index (IPPI), the Wholesale Services Price Index (WSPI), the Retail Services Price Index (RSPI), and the CPI to detail how manufacturers price movement works it way through the supply-chain to ultimately inform the price consumers pay for beef.
Release date: 2022-09-02 - Stats in brief: 45-28-0001202200100007Description:
This article uses administrative data from the Canada Emergency Response Benefit (CERB) program linked to the 2016 long-form Census to examine socio-economic characteristics of Indigenous workers who received the benefit between March and September 2020. Proportions of workers who received payment are presented by age group, sex, province or region, industry of employment, income and size of business as well as for First Nations, Métis and Inuit workers separately.
Release date: 2022-08-03 - Articles and reports: 12-001-X202200100002Description:
We consider an intercept only linear random effects model for analysis of data from a two stage cluster sampling design. At the first stage a simple random sample of clusters is drawn, and at the second stage a simple random sample of elementary units is taken within each selected cluster. The response variable is assumed to consist of a cluster-level random effect plus an independent error term with known variance. The objects of inference are the mean of the outcome variable and the random effect variance. With a more complex two stage sampling design, the use of an approach based on an estimated pairwise composite likelihood function has appealing properties. Our purpose is to use our simpler context to compare the results of likelihood inference with inference based on a pairwise composite likelihood function that is treated as an approximate likelihood, in particular treated as the likelihood component in Bayesian inference. In order to provide credible intervals having frequentist coverage close to nominal values, the pairwise composite likelihood function and corresponding posterior density need modification, such as a curvature adjustment. Through simulation studies, we investigate the performance of an adjustment proposed in the literature, and find that it works well for the mean but provides credible intervals for the random effect variance that suffer from under-coverage. We propose possible future directions including extensions to the case of a complex design.
Release date: 2022-06-21 - Articles and reports: 12-001-X202200100006Description:
In the last two decades, survey response rates have been steadily falling. In that context, it has become increasingly important for statistical agencies to develop and use methods that reduce the adverse effects of non-response on the accuracy of survey estimates. Follow-up of non-respondents may be an effective, albeit time and resource-intensive, remedy for non-response bias. We conducted a simulation study using real business survey data to shed some light on several questions about non-response follow-up. For instance, assuming a fixed non-response follow-up budget, what is the best way to select non-responding units to be followed up? How much effort should be dedicated to repeatedly following up non-respondents until a response is received? Should they all be followed up or a sample of them? If a sample is followed up, how should it be selected? We compared Monte Carlo relative biases and relative root mean square errors under different follow-up sampling designs, sample sizes and non-response scenarios. We also determined an expression for the minimum follow-up sample size required to expend the budget, on average, and showed that it maximizes the expected response rate. A main conclusion of our simulation experiment is that this sample size also appears to approximately minimize the bias and mean square error of the estimates.
Release date: 2022-06-21 - Articles and reports: 62F0014M2022006Description:
This article presents the data sources and methodology for the Machinery and Equipment Price Index (MEPI). The MEPI is an input price index that measures the quarterly change in the price of machinery and equipment purchased by industries in Canada. The MEPI is an important indicator of economic activity in all industries undertaking capital investment, serving as a tool for performance evaluation, cost monitoring, contract assessment and benchmark comparisons. It also provides supplemental information to the Canadian System of Macroeconomic Accounts to calculate gross domestic product and measure changes in productivity.
Release date: 2022-05-16 - Articles and reports: 82-003-X202200400001Description:
Canadians have been gravely impacted by the COVID-19 pandemic, and adults living with children may have been disproportionately impacted. The objective of this study was to describe changes in chronic disease risk factors and current exercise habits among adults living with and without a child younger than 18 years old.
Release date: 2022-04-20 - Articles and reports: 82-003-X202200300002Description:
This study presents detailed tumour-based cancer prevalence estimates in Canada by sex, age group, cancer type and prevalence duration as of January 1, 2018.
Release date: 2022-03-16
Stats in brief (4)
Stats in brief (4) ((4 results))
- Stats in brief: 89-20-00062022004Description:
Gathering, exploring, analyzing and interpreting data are essential steps in producing information that benefits society, the economy and the environment. In this video, we will discuss the importance of considering data ethics throughout the process of producing statistical information.
As a pre-requisite to this video, make sure to watch the video titled “Data Ethics: An introduction” also available in Statistics Canada’s data literacy training catalogue.
Release date: 2022-10-17 - Stats in brief: 89-20-00062022005Description:
In this video, you will learn the answers to the following questions: What are the different types of error? What are the types of error that lead to statistical bias? Where during the data journey statistical bias can occur?
Release date: 2022-10-17 - Stats in brief: 45-28-0001202200100007Description:
This article uses administrative data from the Canada Emergency Response Benefit (CERB) program linked to the 2016 long-form Census to examine socio-economic characteristics of Indigenous workers who received the benefit between March and September 2020. Proportions of workers who received payment are presented by age group, sex, province or region, industry of employment, income and size of business as well as for First Nations, Métis and Inuit workers separately.
Release date: 2022-08-03 - Stats in brief: 45-28-0001202200100001Description:
This article examines some of the effects of COVID-19 on rural businesses in Canada, with comparison to urban counterparts by industry for contextual support. Topics include business obstacles, expectations for the next year, workforce changes and other subjects from the Canadian Survey on Business Conditions, fourth quarter of 2021.
Release date: 2022-01-12
Articles and reports (10)
Articles and reports (10) ((10 results))
- Articles and reports: 75-006-X202200100012Description:
Based on data from the Canadian Community Health Survey (CCHS) and the Canadian Census Health and Environment Cohort (CanCHEC), this study provides an understanding of suicide-related behaviours, namely suicide ideation and completed suicides, among Canadian immigrants.
Release date: 2022-12-01 - Articles and reports: 62F0014M2022010Description: In 2021, Canada recorded its highest annual increase in the Consumer Price Index (CPI) since 1991, as global supply-chains felt the repercussions of the COVID-19 pandemic, transportation and supply disruptions, and rebounding energy prices – all alongside the effects of the climate crisis.
This analysis uses price data from the Industrial Product Price Index (IPPI), the Wholesale Services Price Index (WSPI), the Retail Services Price Index (RSPI), and the CPI to detail how manufacturers price movement works it way through the supply-chain to ultimately inform the price consumers pay for beef.
Release date: 2022-09-02 - Articles and reports: 12-001-X202200100002Description:
We consider an intercept only linear random effects model for analysis of data from a two stage cluster sampling design. At the first stage a simple random sample of clusters is drawn, and at the second stage a simple random sample of elementary units is taken within each selected cluster. The response variable is assumed to consist of a cluster-level random effect plus an independent error term with known variance. The objects of inference are the mean of the outcome variable and the random effect variance. With a more complex two stage sampling design, the use of an approach based on an estimated pairwise composite likelihood function has appealing properties. Our purpose is to use our simpler context to compare the results of likelihood inference with inference based on a pairwise composite likelihood function that is treated as an approximate likelihood, in particular treated as the likelihood component in Bayesian inference. In order to provide credible intervals having frequentist coverage close to nominal values, the pairwise composite likelihood function and corresponding posterior density need modification, such as a curvature adjustment. Through simulation studies, we investigate the performance of an adjustment proposed in the literature, and find that it works well for the mean but provides credible intervals for the random effect variance that suffer from under-coverage. We propose possible future directions including extensions to the case of a complex design.
Release date: 2022-06-21 - Articles and reports: 12-001-X202200100006Description:
In the last two decades, survey response rates have been steadily falling. In that context, it has become increasingly important for statistical agencies to develop and use methods that reduce the adverse effects of non-response on the accuracy of survey estimates. Follow-up of non-respondents may be an effective, albeit time and resource-intensive, remedy for non-response bias. We conducted a simulation study using real business survey data to shed some light on several questions about non-response follow-up. For instance, assuming a fixed non-response follow-up budget, what is the best way to select non-responding units to be followed up? How much effort should be dedicated to repeatedly following up non-respondents until a response is received? Should they all be followed up or a sample of them? If a sample is followed up, how should it be selected? We compared Monte Carlo relative biases and relative root mean square errors under different follow-up sampling designs, sample sizes and non-response scenarios. We also determined an expression for the minimum follow-up sample size required to expend the budget, on average, and showed that it maximizes the expected response rate. A main conclusion of our simulation experiment is that this sample size also appears to approximately minimize the bias and mean square error of the estimates.
Release date: 2022-06-21 - Articles and reports: 62F0014M2022006Description:
This article presents the data sources and methodology for the Machinery and Equipment Price Index (MEPI). The MEPI is an input price index that measures the quarterly change in the price of machinery and equipment purchased by industries in Canada. The MEPI is an important indicator of economic activity in all industries undertaking capital investment, serving as a tool for performance evaluation, cost monitoring, contract assessment and benchmark comparisons. It also provides supplemental information to the Canadian System of Macroeconomic Accounts to calculate gross domestic product and measure changes in productivity.
Release date: 2022-05-16 - Articles and reports: 82-003-X202200400001Description:
Canadians have been gravely impacted by the COVID-19 pandemic, and adults living with children may have been disproportionately impacted. The objective of this study was to describe changes in chronic disease risk factors and current exercise habits among adults living with and without a child younger than 18 years old.
Release date: 2022-04-20 - Articles and reports: 82-003-X202200300002Description:
This study presents detailed tumour-based cancer prevalence estimates in Canada by sex, age group, cancer type and prevalence duration as of January 1, 2018.
Release date: 2022-03-16 - Articles and reports: 62F0014M2021017Description:
Decisions by economic agents, such as firms and consumers, depend on their views about inflation. Consumers’ views of inflation, are systematically higher than inflation measured by the Consumer Price Index (CPI), and more so for certain demographic groups. While measurement factors can explain part of this gap, behavioral factors appear to play a larger role. This article examines these factors to explain the gap between CPI’s inflation and inflation perceptions in Canada.
Release date: 2022-01-19 - Articles and reports: 12-001-X202100200007Description:
In this paper, we consider the Fay-Herriot model for small area estimation. In particular, we are interested in the impact of sampling variance smoothing and modeling on the model-based estimates. We present methods of smoothing and modeling for the sampling variances and apply the proposed models to a real data analysis. Our results indicate that sampling variance smoothing can improve the efficiency and accuracy of the model-based estimator. For sampling variance modeling, the HB models of You (2016) and Sugasawa, Tamae and Kubokawa (2017) perform equally well to improve the direct survey estimates.
Release date: 2022-01-06 - Articles and reports: 46-28-0001202200100001Description:
When a survey publishes statistics with a quality indicator, it is usually derived from measures based on sampling theory. The production of quality indicators is a significant challenge when statistics are produced using alternative sources for which no sampling is done. This paper describes a new method used to create a quality indicator that combines indicators obtained at different stages of data processing. An example of the application of the method in the Canadian Housing Statistics Program is provided in the Appendix.
Release date: 2022-01-06
Journals and periodicals (0)
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