Keyword search
Filter results by
Search HelpKeyword(s)
Survey or statistical program
- Consumer Price Index (4)
- Census of Population (4)
- Canadian Community Health Survey - Annual Component (3)
- Longitudinal and International Study of Adults (3)
- Survey of Household Spending (2)
- Labour Force Survey (2)
- Canadian Health Measures Survey (2)
- Gross Domestic Product by Industry - National (Monthly) (1)
- Waste Management Industry Survey: Government Sector (1)
- National Balance Sheet Accounts (1)
- National Gross Domestic Product by Income and by Expenditure Accounts (1)
- Biennial Waste Management Survey (1)
- Monthly Oil and Other Liquid Petroleum Products Pipeline Survey (1)
- Monthly Electricity Supply and Disposition Survey (1)
- Monthly New Motor Vehicle Sales Survey (1)
- Uniform Crime Reporting Survey (1)
- Census of Agriculture (1)
- Annual Demographic Estimates: Canada, Provinces and Territories (1)
- Households and the Environment Survey (1)
- Annual Income Estimates for Census Families and Individuals (T1 Family File) (1)
- Annual Survey of Research and Development in Canadian Industry (1)
- Research and Development of Canadian Private Non-Profit Organizations (1)
- General Social Survey - Time Use (1)
- General Social Survey - Social Identity (1)
- Canadian System of Environmental-Economic Accounts - Physical Flow Accounts (1)
- Biennial Drinking Water Plants Survey (1)
- Government Finance Statistics (1)
- Canadian Income Survey (1)
- Study on International Money Transfers (1)
- Longitudinal Employment Analysis Program (1)
Results
All (42)
All (42) (0 to 10 of 42 results)
- Articles and reports: 12-001-X202000200001Description:
This paper constructs a probability-proportional-to-size (PPS) ranked-set sample from a stratified population. A PPS-ranked-set sample partitions the units in a PPS sample into groups of similar observations. The construction of similar groups relies on relative positions (ranks) of units in small comparison sets. Hence, the ranks induce more structure (stratification) in the sample in addition to the data structure created by unequal selection probabilities in a PPS sample. This added data structure makes the PPS-ranked-set sample more informative then a PPS-sample. The stratified PPS-ranked-set sample is constructed by selecting a PPS-ranked-set sample from each stratum population. The paper constructs unbiased estimators for the population mean, total and their variances. The new sampling design is applied to apple production data to estimate the total apple production in Turkey.
Release date: 2020-12-15 - Articles and reports: 12-001-X202000200002Description:
In many large-scale surveys, estimates are produced for numerous small domains defined by cross-classifications of demographic, geographic and other variables. Even though the overall sample size of such surveys might be very large, samples sizes for domains are sometimes too small for reliable estimation. We propose an improved estimation approach that is applicable when “natural” or qualitative relationships (such as orderings or other inequality constraints) can be formulated for the domain means at the population level. We stay within a design-based inferential framework but impose constraints representing these relationships on the sample-based estimates. The resulting constrained domain estimator is shown to be design consistent and asymptotically normally distributed as long as the constraints are asymptotically satisfied at the population level. The estimator and its associated variance estimator are readily implemented in practice. The applicability of the method is illustrated on data from the 2015 U.S. National Survey of College Graduates.
Release date: 2020-12-15 - Articles and reports: 12-001-X202000200003Description:
We combine weighting and Bayesian prediction in a unified approach to survey inference. The general principles of Bayesian analysis imply that models for survey outcomes should be conditional on all variables that affect the probability of inclusion. We incorporate all the variables that are used in the weighting adjustment under the framework of multilevel regression and poststratification, as a byproduct generating model-based weights after smoothing. We improve small area estimation by dealing with different complex issues caused by real-life applications to obtain robust inference at finer levels for subdomains of interest. We investigate deep interactions and introduce structured prior distributions for smoothing and stability of estimates. The computation is done via Stan and is implemented in the open-source R package rstanarm and available for public use. We evaluate the design-based properties of the Bayesian procedure. Simulation studies illustrate how the model-based prediction and weighting inference can outperform classical weighting. We apply the method to the New York Longitudinal Study of Wellbeing. The new approach generates smoothed weights and increases efficiency for robust finite population inference, especially for subsets of the population.
Release date: 2020-12-15 - Articles and reports: 12-001-X202000200004Description:
This article proposes a weight scaling method for Firth’s penalized likelihood for proportional hazards regression models. The method derives a relationship between the penalized likelihood that uses scaled weights and the penalized likelihood that uses unscaled weights, and it shows that the penalized likelihood that uses scaled weights have some desirable properties. A simulation study indicates that the penalized likelihood using scaled weights produces smaller biases in point estimates and standard errors than the biases produced by the penalized likelihood using unscaled weights. The weighted penalized likelihood is applied to estimate hazard rates for heart attacks by using a public-use data set from the National Health and Epidemiology Followup Study (NHEFS). SAS® statements to estimate hazard rates using data from complex surveys are given in the appendix.
Release date: 2020-12-15 - Articles and reports: 12-001-X202000200005Description:
In surveys, text answers from open-ended questions are important because they allow respondents to provide more information without constraints. When classifying open-ended questions automatically using supervised learning, often the accuracy is not high enough. Alternatively, a semi-automated classification strategy can be considered: answers in the easy-to-classify group are classified automatically, answers in the hard-to-classify group are classified manually. This paper presents a semi-automated classification method for multi-label open-ended questions where text answers may be associated with multiple classes simultaneously. The proposed method effectively combines multiple probabilistic classifier chains while avoiding prohibitive computational costs. The performance evaluation on three different data sets demonstrates the effectiveness of the proposed method.
Release date: 2020-12-15 - 6. Validation of the Food Security Module in the 2018 Longitudinal and International Study of AdultsArticles and reports: 89-648-X2020004Description:
This technical report is intended to validate the Longitudinal and International Study of Adults (LISA) Wave 4 (2018) Food Security (FSC) module and provide recommendations for analytical use. Section 2 of this report provides an overview of the LISA data. Section 3 provides some background information of food security measures in national surveys and why it is significant in today's literature. Section 4 analyzes FSC data by presenting key descriptive statistics and logic checks using LISA methodology as well as outside researcher information. In section 5, certification validation was done by comparing other Canadian national surveys that have used the FSC module to the one used by LISA. Finally in section 6, key findings and their implications with regard to LISA are outlined.
Release date: 2020-11-02 - Articles and reports: 11-633-X2020004Description:
Recent advances in artificial intelligence have rekindled ancient fears that robots will replace humans in the economy. Previous waves of automation changed but did not reduce labour’s role, but robots’ human-like flexibility could make this time different. Whether or not it will is an empirical question that has lacked suitable data to answer. This paper describes the creation of a dataset to fill the evidence gap in Canada. Robots! is firm-level panel data on robot adoption created using Canadian import data. The data identify a substantial amount of the robot investment in the Canadian economy from 1996 to 2017. Although many robots are imported by robotics wholesalers or programmers for resale, the majority of them can be attributed to their final (direct) adopting firm. The data can be used to study the impact of robot adoption at the economic region, industry or firm-level.
Release date: 2020-11-02 - Articles and reports: 11-637-X202000100001Description: As the first goal outlined in the 2030 Agenda for Sustainable Development, Canada and other UN member states have committed to end poverty in all its forms everywhere by 2030. This 2020 infographic provides an overview of indicators underlying the first Sustainable Development Goal in support of eradicating poverty, and the statistics and data sources used to monitor and report on this goal in Canada.Release date: 2020-10-20
- Articles and reports: 11-637-X202000100002Description: As the second goal outlined in the 2030 Agenda for Sustainable Development, Canada and other UN member states have committed to end hunger, achieve food security and improved nutrition, and promote sustainable agriculture by 2030. This 2020 infographic provides an overview of indicators underlying the second Sustainable Development Goal in support of ending hunger, and the statistics and data sources used to monitor and report on this goal in Canada.Release date: 2020-10-20
- Articles and reports: 11-637-X202000100003Description: As the third goal outlined in the 2030 Agenda for Sustainable Development, Canada and other UN member states have committed to ensure healthy lives and promote well-being for all at all ages by 2030. This 2020 infographic provides an overview of indicators underlying the third Sustainable Development Goal in support of Good Health and Well-being, and the statistics and data sources used to monitor and report on this goal in Canada.Release date: 2020-10-20
Data (0)
Data (0) (0 results)
No content available at this time.
Analysis (42)
Analysis (42) (0 to 10 of 42 results)
- Articles and reports: 12-001-X202000200001Description:
This paper constructs a probability-proportional-to-size (PPS) ranked-set sample from a stratified population. A PPS-ranked-set sample partitions the units in a PPS sample into groups of similar observations. The construction of similar groups relies on relative positions (ranks) of units in small comparison sets. Hence, the ranks induce more structure (stratification) in the sample in addition to the data structure created by unequal selection probabilities in a PPS sample. This added data structure makes the PPS-ranked-set sample more informative then a PPS-sample. The stratified PPS-ranked-set sample is constructed by selecting a PPS-ranked-set sample from each stratum population. The paper constructs unbiased estimators for the population mean, total and their variances. The new sampling design is applied to apple production data to estimate the total apple production in Turkey.
Release date: 2020-12-15 - Articles and reports: 12-001-X202000200002Description:
In many large-scale surveys, estimates are produced for numerous small domains defined by cross-classifications of demographic, geographic and other variables. Even though the overall sample size of such surveys might be very large, samples sizes for domains are sometimes too small for reliable estimation. We propose an improved estimation approach that is applicable when “natural” or qualitative relationships (such as orderings or other inequality constraints) can be formulated for the domain means at the population level. We stay within a design-based inferential framework but impose constraints representing these relationships on the sample-based estimates. The resulting constrained domain estimator is shown to be design consistent and asymptotically normally distributed as long as the constraints are asymptotically satisfied at the population level. The estimator and its associated variance estimator are readily implemented in practice. The applicability of the method is illustrated on data from the 2015 U.S. National Survey of College Graduates.
Release date: 2020-12-15 - Articles and reports: 12-001-X202000200003Description:
We combine weighting and Bayesian prediction in a unified approach to survey inference. The general principles of Bayesian analysis imply that models for survey outcomes should be conditional on all variables that affect the probability of inclusion. We incorporate all the variables that are used in the weighting adjustment under the framework of multilevel regression and poststratification, as a byproduct generating model-based weights after smoothing. We improve small area estimation by dealing with different complex issues caused by real-life applications to obtain robust inference at finer levels for subdomains of interest. We investigate deep interactions and introduce structured prior distributions for smoothing and stability of estimates. The computation is done via Stan and is implemented in the open-source R package rstanarm and available for public use. We evaluate the design-based properties of the Bayesian procedure. Simulation studies illustrate how the model-based prediction and weighting inference can outperform classical weighting. We apply the method to the New York Longitudinal Study of Wellbeing. The new approach generates smoothed weights and increases efficiency for robust finite population inference, especially for subsets of the population.
Release date: 2020-12-15 - Articles and reports: 12-001-X202000200004Description:
This article proposes a weight scaling method for Firth’s penalized likelihood for proportional hazards regression models. The method derives a relationship between the penalized likelihood that uses scaled weights and the penalized likelihood that uses unscaled weights, and it shows that the penalized likelihood that uses scaled weights have some desirable properties. A simulation study indicates that the penalized likelihood using scaled weights produces smaller biases in point estimates and standard errors than the biases produced by the penalized likelihood using unscaled weights. The weighted penalized likelihood is applied to estimate hazard rates for heart attacks by using a public-use data set from the National Health and Epidemiology Followup Study (NHEFS). SAS® statements to estimate hazard rates using data from complex surveys are given in the appendix.
Release date: 2020-12-15 - Articles and reports: 12-001-X202000200005Description:
In surveys, text answers from open-ended questions are important because they allow respondents to provide more information without constraints. When classifying open-ended questions automatically using supervised learning, often the accuracy is not high enough. Alternatively, a semi-automated classification strategy can be considered: answers in the easy-to-classify group are classified automatically, answers in the hard-to-classify group are classified manually. This paper presents a semi-automated classification method for multi-label open-ended questions where text answers may be associated with multiple classes simultaneously. The proposed method effectively combines multiple probabilistic classifier chains while avoiding prohibitive computational costs. The performance evaluation on three different data sets demonstrates the effectiveness of the proposed method.
Release date: 2020-12-15 - 6. Validation of the Food Security Module in the 2018 Longitudinal and International Study of AdultsArticles and reports: 89-648-X2020004Description:
This technical report is intended to validate the Longitudinal and International Study of Adults (LISA) Wave 4 (2018) Food Security (FSC) module and provide recommendations for analytical use. Section 2 of this report provides an overview of the LISA data. Section 3 provides some background information of food security measures in national surveys and why it is significant in today's literature. Section 4 analyzes FSC data by presenting key descriptive statistics and logic checks using LISA methodology as well as outside researcher information. In section 5, certification validation was done by comparing other Canadian national surveys that have used the FSC module to the one used by LISA. Finally in section 6, key findings and their implications with regard to LISA are outlined.
Release date: 2020-11-02 - Articles and reports: 11-633-X2020004Description:
Recent advances in artificial intelligence have rekindled ancient fears that robots will replace humans in the economy. Previous waves of automation changed but did not reduce labour’s role, but robots’ human-like flexibility could make this time different. Whether or not it will is an empirical question that has lacked suitable data to answer. This paper describes the creation of a dataset to fill the evidence gap in Canada. Robots! is firm-level panel data on robot adoption created using Canadian import data. The data identify a substantial amount of the robot investment in the Canadian economy from 1996 to 2017. Although many robots are imported by robotics wholesalers or programmers for resale, the majority of them can be attributed to their final (direct) adopting firm. The data can be used to study the impact of robot adoption at the economic region, industry or firm-level.
Release date: 2020-11-02 - Articles and reports: 11-637-X202000100001Description: As the first goal outlined in the 2030 Agenda for Sustainable Development, Canada and other UN member states have committed to end poverty in all its forms everywhere by 2030. This 2020 infographic provides an overview of indicators underlying the first Sustainable Development Goal in support of eradicating poverty, and the statistics and data sources used to monitor and report on this goal in Canada.Release date: 2020-10-20
- Articles and reports: 11-637-X202000100002Description: As the second goal outlined in the 2030 Agenda for Sustainable Development, Canada and other UN member states have committed to end hunger, achieve food security and improved nutrition, and promote sustainable agriculture by 2030. This 2020 infographic provides an overview of indicators underlying the second Sustainable Development Goal in support of ending hunger, and the statistics and data sources used to monitor and report on this goal in Canada.Release date: 2020-10-20
- Articles and reports: 11-637-X202000100003Description: As the third goal outlined in the 2030 Agenda for Sustainable Development, Canada and other UN member states have committed to ensure healthy lives and promote well-being for all at all ages by 2030. This 2020 infographic provides an overview of indicators underlying the third Sustainable Development Goal in support of Good Health and Well-being, and the statistics and data sources used to monitor and report on this goal in Canada.Release date: 2020-10-20
Reference (0)
Reference (0) (0 results)
No content available at this time.
- Date modified: