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- Surveys and statistical programs – Documentation: 68-514-XDescription:
Statistics Canada's approach to gathering and disseminating economic data has developed over several decades into a highly integrated system for collection and estimation that feeds the framework of the Canadian System of National Accounts.
The key to this approach was creation of the Unified Enterprise Survey, the goal of which was to improve the consistency, coherence, breadth and depth of business survey data.
The UES did so by bringing many of Statistics Canada's individual annual business surveys under a common framework. This framework included a single survey frame, a sample design framework, conceptual harmonization of survey content, means of using relevant administrative data, common data collection, processing and analysis tools, and a common data warehouse.
Release date: 2006-11-20 - 2. Targeted random walk designs ArchivedArticles and reports: 12-001-X20060019262Description:
Hidden human populations, the Internet, and other networked structures conceptualized mathematically as graphs are inherently hard to sample by conventional means, and the most effective study designs usually involve procedures that select the sample by adaptively following links from one node to another. Sample data obtained in such studies are generally not representative at face value of the larger population of interest. However, a number of design and model based methods are now available for effective inference from such samples. The design based methods have the advantage that they do not depend on an assumed population model, but do depend for their validity on the design being implemented in a controlled and known way, which can be difficult or impossible in practice. The model based methods allow greater flexibly in the design, but depend on modeling of the population using stochastic graph models and also depend on the design being ignorable or of known form so that it can be included in the likelihood or Bayes equations. For both the design and the model based methods, the weak point often is the lack of control in how the initial sample is obtained, from which link-tracing commences. The designs described in this paper offer a third way, in which the sample selection probabilities become step by step less dependent on the initial sample selection. A Markov chain "random walk" model idealizes the natural design tendencies of a link-tracing selection sequence through a graph. This paper introduces uniform and targeted walk designs in which the random walk is nudged at each step to produce a design with the desired stationary probabilities. A sample is thus obtained that in important respects is representative at face value of the larger population of interest, or that requires only simple weighting factors to make it so.
Release date: 2006-07-20 - Articles and reports: 88-003-X20060029240Geography: CanadaDescription:
This article summarizes the Canadian experience in collecting and accessing information on government expenditure (both federal and provincial) on nanotechnology R&D in Canada. The steps taken to measure activities in the private sector on nanotechnology illustrate the many challenges facing measurement of nanotechnology activities.
Release date: 2006-06-27 - Notices and consultations: 87-004-X20030039213Description:
The Culture Statistics Program (CSP) has been Statistic Canada's chief source for analysis of the culture sector since the program's inception in 1972 and this role will continue. However, the CSP is making substantial changes to the way it collects culture data and, in effect, the data themselves. This article is intended to inform users of these data, of the scope of these upcoming changes and how the CSP is managing the challenges presented by this transition.
Release date: 2006-06-12 - Journals and periodicals: 85-569-XGeography: CanadaDescription:
This feasibility report provides a blueprint for improving data on fraud in Canada through a survey of businesses and through amendments to the Uniform Crime Reporting (UCR) Survey. Presently, national information on fraud is based on official crime statistics reported by police services to the Uniform Crime Reporting Survey. These data, however, do not reflect the true nature and extent of fraud in Canada due to under-reporting of fraud by individuals and businesses, and due to inconsistencies in the way frauds are counted within the UCR Survey. This feasibility report concludes that a better measurement of fraud in Canada could be obtained through a survey of businesses. The report presents the information priorities of government departments, law enforcement and the private sector with respect to the issue of fraud and makes recommendations on how a survey of businesses could help fulfill these information needs.
To respond to information priorities, the study recommends surveying the following types of business establishments: banks, payment companies (i.e. credit card and debit card companies), selected retailers, property and casualty insurance carriers, health and disability insurance carriers and selected manufacturers. The report makes recommendations regarding survey methodology and questionnaire content, and provides estimates for timeframes and cost.
The report also recommends changes to the UCR Survey in order to improve the way in which incidents are counted and to render the data collected more relevant with respect to the information priorities raised by government, law enforcement and the private sector during the feasibility study.
Release date: 2006-04-11 - Articles and reports: 81-004-X20050059111Description:
This article presents an analysis of Census at School survey results that shows what students themselves had to say about their reading and associated daily habits. It was written by Statistics Canada analysts as an example to students of the type of detailed analysis that can be made using Canadian Census at School results. The article uses data that were collected from over 22,000 students across Canada during the 2004-2005 academic year. Census at School is an international classroom project that teaches students aged 8 to 18 about statistical enquiry and census-taking. Students anonymously fill in an online questionnaire about themselves - their height, time use, eating habits and much more - and then use their class results to learn statistical concepts, practice data analysis and explore social issues. Their responses also become part of national and international project databases that are used for teaching statistics.
The Census at School statistical literacy project is not an official Statistics Canada survey conducted under the Statistics Act. Schools; students participate on a voluntary basis and the data collected are not representative of Canada's student population. This is clearly stated with the summary Canadian results that are made available on the project website for the benefit of participating students.
Release date: 2006-02-28 - Articles and reports: 11F0019M2006274Geography: CanadaDescription:
We present new evidence on levels and trends in after-tax income inequality in Canada between 1980 and 2000. We argue that existing data sources may miss changes in the tails of the income distribution, and that much of the changes in the income distribution have been in the tails. Our data are constructed from Census files, which are augmented with predicted taxes based on information available from administrative tax data. After validating our approach in predicting taxes on the Census files, we document differences in the levels and trends in after-tax inequality between the newly constructed data source and the more commonly used survey data. We find that after-tax inequality levels are substantially higher based on the new data, primarily because income levels are lower at the bottom than in survey data. The new data show larger long-term increases in after-tax income inequality and far more variability over the economic cycle. This raises interesting questions about the role of the tax and transfer system in mitigating both trends and fluctuations in market income inequality.
Release date: 2006-02-27 - Articles and reports: 12-001-X20050029049Description:
Nonresponse weight adjustment is commonly used to compensate for unit nonresponse in surveys. Often, a nonresponse model is postulated and design weights are adjusted by the inverse of estimated response probabilities. Typical nonresponse models are conditional on a vector of fixed auxiliary variables that are observed for every sample unit, such as variables used to construct the sampling design. In this note, we consider using data collection process variables as potential auxiliary variables. An example is the number of attempts to contact a sample unit. In our treatment, these auxiliary variables are taken to be random, even after conditioning on the selected sample, since they could change if the data collection process were repeated for a given sample. We show that this randomness introduces no bias and no additional variance component in the estimates of population totals when the nonresponse model is properly specified. Moreover, when nonresponse depends on the variables of interest, we argue that the use of data collection process variables is likely to reduce the nonresponse bias if they provide information about the variables of interest not already included in the nonresponse model and if they are associated with nonresponse. As a result, data collection process variables may well be beneficial to handle unit nonresponse. This is briefly illustrated using the Canadian Labour Force Survey.
Release date: 2006-02-17
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- 1. Targeted random walk designs ArchivedArticles and reports: 12-001-X20060019262Description:
Hidden human populations, the Internet, and other networked structures conceptualized mathematically as graphs are inherently hard to sample by conventional means, and the most effective study designs usually involve procedures that select the sample by adaptively following links from one node to another. Sample data obtained in such studies are generally not representative at face value of the larger population of interest. However, a number of design and model based methods are now available for effective inference from such samples. The design based methods have the advantage that they do not depend on an assumed population model, but do depend for their validity on the design being implemented in a controlled and known way, which can be difficult or impossible in practice. The model based methods allow greater flexibly in the design, but depend on modeling of the population using stochastic graph models and also depend on the design being ignorable or of known form so that it can be included in the likelihood or Bayes equations. For both the design and the model based methods, the weak point often is the lack of control in how the initial sample is obtained, from which link-tracing commences. The designs described in this paper offer a third way, in which the sample selection probabilities become step by step less dependent on the initial sample selection. A Markov chain "random walk" model idealizes the natural design tendencies of a link-tracing selection sequence through a graph. This paper introduces uniform and targeted walk designs in which the random walk is nudged at each step to produce a design with the desired stationary probabilities. A sample is thus obtained that in important respects is representative at face value of the larger population of interest, or that requires only simple weighting factors to make it so.
Release date: 2006-07-20 - Articles and reports: 88-003-X20060029240Geography: CanadaDescription:
This article summarizes the Canadian experience in collecting and accessing information on government expenditure (both federal and provincial) on nanotechnology R&D in Canada. The steps taken to measure activities in the private sector on nanotechnology illustrate the many challenges facing measurement of nanotechnology activities.
Release date: 2006-06-27 - Journals and periodicals: 85-569-XGeography: CanadaDescription:
This feasibility report provides a blueprint for improving data on fraud in Canada through a survey of businesses and through amendments to the Uniform Crime Reporting (UCR) Survey. Presently, national information on fraud is based on official crime statistics reported by police services to the Uniform Crime Reporting Survey. These data, however, do not reflect the true nature and extent of fraud in Canada due to under-reporting of fraud by individuals and businesses, and due to inconsistencies in the way frauds are counted within the UCR Survey. This feasibility report concludes that a better measurement of fraud in Canada could be obtained through a survey of businesses. The report presents the information priorities of government departments, law enforcement and the private sector with respect to the issue of fraud and makes recommendations on how a survey of businesses could help fulfill these information needs.
To respond to information priorities, the study recommends surveying the following types of business establishments: banks, payment companies (i.e. credit card and debit card companies), selected retailers, property and casualty insurance carriers, health and disability insurance carriers and selected manufacturers. The report makes recommendations regarding survey methodology and questionnaire content, and provides estimates for timeframes and cost.
The report also recommends changes to the UCR Survey in order to improve the way in which incidents are counted and to render the data collected more relevant with respect to the information priorities raised by government, law enforcement and the private sector during the feasibility study.
Release date: 2006-04-11 - Articles and reports: 81-004-X20050059111Description:
This article presents an analysis of Census at School survey results that shows what students themselves had to say about their reading and associated daily habits. It was written by Statistics Canada analysts as an example to students of the type of detailed analysis that can be made using Canadian Census at School results. The article uses data that were collected from over 22,000 students across Canada during the 2004-2005 academic year. Census at School is an international classroom project that teaches students aged 8 to 18 about statistical enquiry and census-taking. Students anonymously fill in an online questionnaire about themselves - their height, time use, eating habits and much more - and then use their class results to learn statistical concepts, practice data analysis and explore social issues. Their responses also become part of national and international project databases that are used for teaching statistics.
The Census at School statistical literacy project is not an official Statistics Canada survey conducted under the Statistics Act. Schools; students participate on a voluntary basis and the data collected are not representative of Canada's student population. This is clearly stated with the summary Canadian results that are made available on the project website for the benefit of participating students.
Release date: 2006-02-28 - Articles and reports: 11F0019M2006274Geography: CanadaDescription:
We present new evidence on levels and trends in after-tax income inequality in Canada between 1980 and 2000. We argue that existing data sources may miss changes in the tails of the income distribution, and that much of the changes in the income distribution have been in the tails. Our data are constructed from Census files, which are augmented with predicted taxes based on information available from administrative tax data. After validating our approach in predicting taxes on the Census files, we document differences in the levels and trends in after-tax inequality between the newly constructed data source and the more commonly used survey data. We find that after-tax inequality levels are substantially higher based on the new data, primarily because income levels are lower at the bottom than in survey data. The new data show larger long-term increases in after-tax income inequality and far more variability over the economic cycle. This raises interesting questions about the role of the tax and transfer system in mitigating both trends and fluctuations in market income inequality.
Release date: 2006-02-27 - Articles and reports: 12-001-X20050029049Description:
Nonresponse weight adjustment is commonly used to compensate for unit nonresponse in surveys. Often, a nonresponse model is postulated and design weights are adjusted by the inverse of estimated response probabilities. Typical nonresponse models are conditional on a vector of fixed auxiliary variables that are observed for every sample unit, such as variables used to construct the sampling design. In this note, we consider using data collection process variables as potential auxiliary variables. An example is the number of attempts to contact a sample unit. In our treatment, these auxiliary variables are taken to be random, even after conditioning on the selected sample, since they could change if the data collection process were repeated for a given sample. We show that this randomness introduces no bias and no additional variance component in the estimates of population totals when the nonresponse model is properly specified. Moreover, when nonresponse depends on the variables of interest, we argue that the use of data collection process variables is likely to reduce the nonresponse bias if they provide information about the variables of interest not already included in the nonresponse model and if they are associated with nonresponse. As a result, data collection process variables may well be beneficial to handle unit nonresponse. This is briefly illustrated using the Canadian Labour Force Survey.
Release date: 2006-02-17
Reference (2)
Reference (2) ((2 results))
- Surveys and statistical programs – Documentation: 68-514-XDescription:
Statistics Canada's approach to gathering and disseminating economic data has developed over several decades into a highly integrated system for collection and estimation that feeds the framework of the Canadian System of National Accounts.
The key to this approach was creation of the Unified Enterprise Survey, the goal of which was to improve the consistency, coherence, breadth and depth of business survey data.
The UES did so by bringing many of Statistics Canada's individual annual business surveys under a common framework. This framework included a single survey frame, a sample design framework, conceptual harmonization of survey content, means of using relevant administrative data, common data collection, processing and analysis tools, and a common data warehouse.
Release date: 2006-11-20 - Notices and consultations: 87-004-X20030039213Description:
The Culture Statistics Program (CSP) has been Statistic Canada's chief source for analysis of the culture sector since the program's inception in 1972 and this role will continue. However, the CSP is making substantial changes to the way it collects culture data and, in effect, the data themselves. This article is intended to inform users of these data, of the scope of these upcoming changes and how the CSP is managing the challenges presented by this transition.
Release date: 2006-06-12
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