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1. Measuring Social Capital at the Neighbourhood Level: Experimental Estimates of Sense of Belonging to the Local Community Measured at the Census Tract Level
Articles and reports: 11-633-X2021007
Description:
Statistics Canada continues to use a variety of data sources to provide neighbourhood-level variables across an expanding set of domains, such as sociodemographic characteristics, income, services and amenities, crime, and the environment. Yet, despite these advances, information on the social aspects of neighbourhoods is still unavailable. In this paper, answers to the Canadian Community Health Survey on respondents’ sense of belonging to their local community were pooled over the four survey years from 2016 to 2019. Individual responses were aggregated up to the census tract (CT) level.
Release date: 2021-11-16
2. 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: 2021-11-16
3. Construction of a Northern Market Basket Measure of poverty for Yukon and the Northwest Territories
Articles and reports: 75F0002M2021007
Description:
This discussion paper describes the proposed methodology for a Northern Market Basket Measure (MBM-N) for Yukon and the Northwest Territories, as well as identifies research which could be conducted in preparation for the 2023 review. The paper presents initial MBM-N thresholds and provides preliminary poverty estimates for reference years 2018 and 2019. A review period will follow the release of this paper, during which time Statistics Canada and Employment and Social Development Canada will welcome feedback from interested parties and work with experts, stakeholders, indigenous organizations, federal, provincial and territorial officials to validate the results.
Release date: 2021-11-12
4. Income Research Paper Series
Journals and periodicals: 75F0002M
Description:
This series provides detailed documentation on income developments, including survey design issues, data quality evaluation and exploratory research.
Release date: 2021-11-12
5. 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: 2021-10-06
6. Distributions of Household Economic Accounts, estimates of asset, liability and net worth distributions, 2010 to 2020, technical methodology and quality report
Articles and reports: 13-604-M2021001
Description:
This documentation outlines the methodology used to develop the Distributions of household economic accounts published in September 2021 for the reference years 2010 to 2020. It describes the framework and the steps implemented to produce distributional information aligned with the National Balance Sheet Accounts and other national accounts concepts. It also includes a report on the quality of the estimated distributions.
Release date: 2021-09-07
7. Bayesian predictive inference of small area proportions under selection bias
Articles and reports: 12-001-X202100100001
Description:
In a previous paper, we developed a model to make inference about small area proportions under selection bias in which the binary responses and the selection probabilities are correlated. This is the homogeneous nonignorable selection model; nonignorable selection means that the selection probabilities and the binary responses are correlated. The homogeneous nonignorable selection model was shown to perform better than a baseline ignorable selection model. However, one limitation of the homogeneous nonignorable selection model is that the distributions of the selection probabilities are assumed to be identical across areas. Therefore, we introduce a more general model, the heterogeneous nonignorable selection model, in which the selection probabilities are not identically distributed over areas. We used Markov chain Monte Carlo methods to fit the three models. We illustrate our methodology and compare our models using an example on severe activity limitation of the U.S. National Health Interview Survey. We also perform a simulation study to demonstrate that our heterogeneous nonignorable selection model is needed when there is moderate to strong selection bias.
Release date: 2021-06-24
8. A method to find an efficient and robust sampling strategy under model uncertainty
Articles and reports: 12-001-X202100100002
Description:
We consider the problem of deciding on sampling strategy, in particular sampling design. We propose a risk measure, whose minimizing value guides the choice. The method makes use of a superpopulation model and takes into account uncertainty about its parameters through a prior distribution. The method is illustrated with a real dataset, yielding satisfactory results. As a baseline, we use the strategy that couples probability proportional-to-size sampling with the difference estimator, as it is known to be optimal when the superpopulation model is fully known. We show that, even under moderate misspecifications of the model, this strategy is not robust and can be outperformed by some alternatives.
Release date: 2021-06-24
9. Sample empirical likelihood approach under complex survey design with scrambled responses
Articles and reports: 12-001-X202100100003
Description:
One effective way to conduct statistical disclosure control is to use scrambled responses. Scrambled responses can be generated by using a controlled random device. In this paper, we propose using the sample empirical likelihood approach to conduct statistical inference under complex survey design with scrambled responses. Specifically, we propose using a Wilk-type confidence interval for statistical inference. Our proposed method can be used as a general tool for inference with confidential public use survey data files. Asymptotic properties are derived, and the limited simulation study verifies the validity of theory. We further apply the proposed method to some real applications.
Release date: 2021-06-24
10. Integration of data from probability surveys and big found data for finite population inference using mass imputation
Articles and reports: 12-001-X202100100004
Description:
Multiple data sources are becoming increasingly available for statistical analyses in the era of big data. As an important example in finite-population inference, we consider an imputation approach to combining data from a probability survey and big found data. We focus on the case when the study variable is observed in the big data only, but the other auxiliary variables are commonly observed in both data. Unlike the usual imputation for missing data analysis, we create imputed values for all units in the probability sample. Such mass imputation is attractive in the context of survey data integration (Kim and Rao, 2012). We extend mass imputation as a tool for data integration of survey data and big non-survey data. The mass imputation methods and their statistical properties are presented. The matching estimator of Rivers (2007) is also covered as a special case. Variance estimation with mass-imputed data is discussed. The simulation results demonstrate the proposed estimators outperform existing competitors in terms of robustness and efficiency.
Release date: 2021-06-24

Stats in brief (59)

Stats in brief (59) (0 to 10 of 59 results)

1. Statistics 101: Exploring measures of central tendency
Stats in brief: 89-20-00062020002
Description:
This video is intended to teach viewers the differences between three fundamental statistical concepts. First, the mean, then the median and finally, the mode.
Release date: 2021-05-03
2. Statistics 101: Exploring measures of dispersion
Stats in brief: 89-20-00062020003
Description:
In this module, we will explore the concept of dispersion, also called variability. This concept includes: the range, the interquartile range, the standard deviation and the normal distribution.
Release date: 2021-05-03
3. Framework for Responsible Machine Learning Processes at Statistics Canada
Stats in brief: 89-20-00062021001
Description:
As Canada's national statistical organization, Statistics Canada is committed to sharing our knowledge and expertise to help all Canadians develop their data literacy skills. The goal is to provide learners with information on the basic concepts and skills with regard to a range of data literacy topics.
The training is aimed at those who are new to data or those who have some experience with data but may need a refresher or want to expand their knowledge. We invite you to check out our Learning catalogue to learn more about our offerings including a great collection of short videos. Be sure to check back regularly as we will be continuing to release new training.
Release date: 2021-05-03
4. Statistics 101: Correlation and Causality
Stats in brief: 89-20-00062021002
Description:
This video is intended for viewers who wish to gain a basic understanding of correlation and causality. As a prerequisite, before beginning this video, we highly recommend having already completed our videos titled “What is Data? An Introduction to Data Terminology and Concepts” and “Types of Data: Understanding and Exploring Data”.
Release date: 2021-05-03
5. Statistics 101: Proportions, ratios, and rates
Stats in brief: 89-20-00062021003
Description:
In this video, viewers will learn the differences between three types of measure: proportions, ratios, and rates. In addition, viewers by the end of this video will be able to determine how each measure is calculated and when it is best to use one measure rather than the other.

Release date: 2021-05-03
6. Machine learning: An introduction
Stats in brief: 89-20-00062021004
Description:
One important distinction we will make in this video is the differences between Data Science, Artificial Intelligence and Machine Learning. You'll learn what machine learning can be used for, how it works, and some different methods for doing it. And you'll also learn how to build and use machine learning processes responsibly.
This video is recommended for those who already have some familiarity with the concepts and techniques associated with computer programming and using algorithms to analyze data.
Release date: 2021-05-03
7. Telling the data story: How to create stories that matter
Stats in brief: 89-20-00062021005
Description:
By the end of this video, you should have a deeper understanding of the fundamentals of using data to tell a story. We will go over some the principle components of storytelling including the data, the narrative and visualization, and discuss how they can be used to construct concise, informative and interesting messages your audience can trust. And then, you will learn the importance of a well planned data story, which includes learning who your audience will be, what they should know and how to best deliver that information.
Release date: 2021-05-03
8. Data stewardship: An introduction to data standards and metadata
Stats in brief: 89-20-00062021006
Description:
In this video, you'll learn what we can do to data itself, to make it easier to work with. That's the role of data standards. And you'll learn what extra information we can provide to make data easier to use. That's the role of metadata.
Release date: 2021-05-03
9. Study: Measuring proximity to services and amenities
Stats in brief: 11-001-X202104628783
Description: Release published in The Daily – Statistics Canada’s official release bulletin
Release date: 2021-02-15
10. Agenda 2030 Sustainable Development Goals Report
Stats in brief: 11-637-X
Description:
On 1 January 2016, the world officially began implementation of the 2030 Agenda for Sustainable Development - the transformative plan of action based on 17 Sustainable Development Goals - to address urgent global challenges over the next 15 years. This report presents an overview of the 17 Goals using data currently available to report on the global indicator framework.
Release date: 2020-10-20

Articles and reports (1,594)

Articles and reports (1,594) (0 to 10 of 1,594 results)

1. Measuring Social Capital at the Neighbourhood Level: Experimental Estimates of Sense of Belonging to the Local Community Measured at the Census Tract Level
Articles and reports: 11-633-X2021007
Description:
Statistics Canada continues to use a variety of data sources to provide neighbourhood-level variables across an expanding set of domains, such as sociodemographic characteristics, income, services and amenities, crime, and the environment. Yet, despite these advances, information on the social aspects of neighbourhoods is still unavailable. In this paper, answers to the Canadian Community Health Survey on respondents’ sense of belonging to their local community were pooled over the four survey years from 2016 to 2019. Individual responses were aggregated up to the census tract (CT) level.
Release date: 2021-11-16
2. Construction of a Northern Market Basket Measure of poverty for Yukon and the Northwest Territories
Articles and reports: 75F0002M2021007
Description:
This discussion paper describes the proposed methodology for a Northern Market Basket Measure (MBM-N) for Yukon and the Northwest Territories, as well as identifies research which could be conducted in preparation for the 2023 review. The paper presents initial MBM-N thresholds and provides preliminary poverty estimates for reference years 2018 and 2019. A review period will follow the release of this paper, during which time Statistics Canada and Employment and Social Development Canada will welcome feedback from interested parties and work with experts, stakeholders, indigenous organizations, federal, provincial and territorial officials to validate the results.
Release date: 2021-11-12
3. Distributions of Household Economic Accounts, estimates of asset, liability and net worth distributions, 2010 to 2020, technical methodology and quality report
Articles and reports: 13-604-M2021001
Description:
This documentation outlines the methodology used to develop the Distributions of household economic accounts published in September 2021 for the reference years 2010 to 2020. It describes the framework and the steps implemented to produce distributional information aligned with the National Balance Sheet Accounts and other national accounts concepts. It also includes a report on the quality of the estimated distributions.
Release date: 2021-09-07
4. Bayesian predictive inference of small area proportions under selection bias
Articles and reports: 12-001-X202100100001
Description:
In a previous paper, we developed a model to make inference about small area proportions under selection bias in which the binary responses and the selection probabilities are correlated. This is the homogeneous nonignorable selection model; nonignorable selection means that the selection probabilities and the binary responses are correlated. The homogeneous nonignorable selection model was shown to perform better than a baseline ignorable selection model. However, one limitation of the homogeneous nonignorable selection model is that the distributions of the selection probabilities are assumed to be identical across areas. Therefore, we introduce a more general model, the heterogeneous nonignorable selection model, in which the selection probabilities are not identically distributed over areas. We used Markov chain Monte Carlo methods to fit the three models. We illustrate our methodology and compare our models using an example on severe activity limitation of the U.S. National Health Interview Survey. We also perform a simulation study to demonstrate that our heterogeneous nonignorable selection model is needed when there is moderate to strong selection bias.
Release date: 2021-06-24
5. A method to find an efficient and robust sampling strategy under model uncertainty
Articles and reports: 12-001-X202100100002
Description:
We consider the problem of deciding on sampling strategy, in particular sampling design. We propose a risk measure, whose minimizing value guides the choice. The method makes use of a superpopulation model and takes into account uncertainty about its parameters through a prior distribution. The method is illustrated with a real dataset, yielding satisfactory results. As a baseline, we use the strategy that couples probability proportional-to-size sampling with the difference estimator, as it is known to be optimal when the superpopulation model is fully known. We show that, even under moderate misspecifications of the model, this strategy is not robust and can be outperformed by some alternatives.
Release date: 2021-06-24
6. Sample empirical likelihood approach under complex survey design with scrambled responses
Articles and reports: 12-001-X202100100003
Description:
One effective way to conduct statistical disclosure control is to use scrambled responses. Scrambled responses can be generated by using a controlled random device. In this paper, we propose using the sample empirical likelihood approach to conduct statistical inference under complex survey design with scrambled responses. Specifically, we propose using a Wilk-type confidence interval for statistical inference. Our proposed method can be used as a general tool for inference with confidential public use survey data files. Asymptotic properties are derived, and the limited simulation study verifies the validity of theory. We further apply the proposed method to some real applications.
Release date: 2021-06-24
7. Integration of data from probability surveys and big found data for finite population inference using mass imputation
Articles and reports: 12-001-X202100100004
Description:
Multiple data sources are becoming increasingly available for statistical analyses in the era of big data. As an important example in finite-population inference, we consider an imputation approach to combining data from a probability survey and big found data. We focus on the case when the study variable is observed in the big data only, but the other auxiliary variables are commonly observed in both data. Unlike the usual imputation for missing data analysis, we create imputed values for all units in the probability sample. Such mass imputation is attractive in the context of survey data integration (Kim and Rao, 2012). We extend mass imputation as a tool for data integration of survey data and big non-survey data. The mass imputation methods and their statistical properties are presented. The matching estimator of Rivers (2007) is also covered as a special case. Variance estimation with mass-imputed data is discussed. The simulation results demonstrate the proposed estimators outperform existing competitors in terms of robustness and efficiency.
Release date: 2021-06-24
8. Bayesian pooling for analyzing categorical data from small areas
Articles and reports: 12-001-X202100100005
Description:
Bayesian pooling strategies are used to solve precision problems related to statistical analyses of data from small areas. In such cases, the subpopulation samples are usually small, even though the population might not be. As an alternative, similar data can be pooled in order to reduce the number of parameters in the model. Many surveys consist of categorical data on each area, collected into a contingency table. We consider hierarchical Bayesian pooling models with a Dirichlet process prior for analyzing categorical data based on small areas. However, the prior used to pool such data frequently results in an overshrinkage problem. To mitigate for this problem, the parameters are separated into global and local effects. This study focuses on data pooling using a Dirichlet process prior. We compare the pooling models using bone mineral density (BMD) data taken from the Third National Health and Nutrition Examination Survey for the period 1988 to 1994 in the United States. Our analyses of the BMD data are performed using a Gibbs sampler and slice sampling to carry out the posterior computations.
Release date: 2021-06-24
9. Science and survey management
Articles and reports: 12-001-X202100100006
Description:
It is now possible to manage surveys using statistical models and other tools that can be applied in real time. This paper focuses on three developments that reflect the attempt to take a more scientific approach to the management of survey field work: 1) the use of responsive and adaptive designs to reduce nonresponse bias, other sources of error, or costs; 2) optimal routing of interviewer travel to reduce costs; and 3) rapid feedback to interviewers to reduce measurement error. The article begins by reviewing experiments and simulation studies examining the effectiveness of responsive and adaptive designs. These studies suggest that these designs can produce modest gains in the representativeness of survey samples or modest cost savings, but can also backfire. The next section of the paper examines efforts to provide interviewers with a recommended route for their next trip to the field. The aim is to bring interviewers’ field work into closer alignment with research priorities while reducing travel time. However, a study testing this strategy found that interviewers often ignore such instructions. Then, the paper describes attempts to give rapid feedback to interviewers, based on automated recordings of their interviews. Interviewers often read questions in ways that affect respondents’ answers; correcting these problems quickly yielded marked improvements in data quality. All of the methods are efforts to replace the judgment of interviewers, field supervisors, and survey managers with statistical models and scientific findings.
Release date: 2021-06-24
10. Small area benchmarked estimation under the basic unit level model when the sampling rates are non-negligible
Articles and reports: 12-001-X202100100007
Description:
We consider the estimation of a small area mean under the basic unit-level model. The sum of the resulting model-dependent estimators may not add up to estimates obtained with a direct survey estimator that is deemed to be accurate for the union of these small areas. Benchmarking forces the model-based estimators to agree with the direct estimator at the aggregated area level. The generalized regression estimator is the direct estimator that we benchmark to. In this paper we compare small area benchmarked estimators based on four procedures. The first procedure produces benchmarked estimators by ratio adjustment. The second procedure is based on the empirical best linear unbiased estimator obtained under the unit-level model augmented with a suitable variable that ensures benchmarking. The third procedure uses pseudo-empirical estimators constructed with suitably chosen sampling weights so that, when aggregated, they agree with the reliable direct estimator for the larger area. The fourth procedure produces benchmarked estimators that are the result of a minimization problem subject to the constraint given by the benchmark condition. These benchmark procedures are applied to the small area estimators when the sampling rates are non-negligible. The resulting benchmarked estimators are compared in terms of relative bias and mean squared error using both a design-based simulation study as well as an example with real survey data.
Release date: 2021-06-24

Journals and periodicals (25)

Journals and periodicals (25) (0 to 10 of 25 results)

1. 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: 2021-11-16
2. Income Research Paper Series
Journals and periodicals: 75F0002M
Description:
This series provides detailed documentation on income developments, including survey design issues, data quality evaluation and exploratory research.
Release date: 2021-11-12
3. 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: 2021-10-06
4. Survey Methodology
Journals and periodicals: 12-001-X
Geography: Canada
Description:
The journal publishes articles dealing with various aspects of statistical development relevant to a statistical agency, such as design issues in the context of practical constraints, use of different data sources and collection techniques, total survey error, survey evaluation, research in survey methodology, time series analysis, seasonal adjustment, demographic studies, data integration, estimation and data analysis methods, and general survey systems development. The emphasis is placed on the development and evaluation of specific methodologies as applied to data collection or the data themselves.
Release date: 2021-06-24
5. Geography Working Paper Series
Journals and periodicals: 92F0138M
Description:
The Geography working paper series is intended to stimulate discussion on a variety of topics covering conceptual, methodological or technical work to support the development and dissemination of the division's data, products and services. Readers of the series are encouraged to contact the Geography Division with comments and suggestions.
Release date: 2019-11-13
6. Standing on the shoulders of giants History of Statistics Canada: 1970 to 2008
Journals and periodicals: 89-20-0001
Description:
Historical works allow readers to peer into the past, not only to satisfy our curiosity about “the way things were,” but also to see how far we’ve come, and to learn from the past. For Statistics Canada, such works are also opportunities to commemorate the agency’s contributions to Canada and its people, and serve as a reminder that an institution such as this continues to evolve each and every day.
On the occasion of Statistics Canada’s 100th anniversary in 2018, Standing on the shoulders of giants: History of Statistics Canada: 1970 to 2008, builds on the work of two significant publications on the history of the agency, picking up the story in 1970 and carrying it through the next 36 years, until 2008. To that end, when enough time has passed to allow for sufficient objectivity, it will again be time to document the agency’s next chapter as it continues to tell Canada’s story in numbers.
Release date: 2018-12-03
7. Record Linkage Project Process Model
Journals and periodicals: 12-605-X
Description:
The Record Linkage Project Process Model (RLPPM) was developed by Statistics Canada to identify the processes and activities involved in record linkage. The RLPPM applies to linkage projects conducted at the individual and enterprise level using diverse data sources to create new data sources to meet analytical and operational needs.
Release date: 2017-06-05
8. Demosim: An Overview of Methods and Data Sources Archived
Journals and periodicals: 91-621-X
Description:
This document briefly describes Demosim, the microsimulation population projection model, how it works as well as its methods and data sources. It is a methodological complement to the analytical products produced using Demosim.
Release date: 2017-01-25
9. Compendium of Management Practices for Statistical Organizations from Statistics Canada's International Statistical Fellowship Program
Journals and periodicals: 11-634-X
Description:
This publication is a catalogue of strategies and mechanisms that a statistical organization should consider adopting, according to its particular context. This compendium is based on lessons learned and best practices of leadership and management of statistical agencies within the scope of Statistics Canada’s International Statistical Fellowship Program (ISFP). It contains four broad sections including, characteristics of an effective national statistical system; core management practices; improving, modernizing and finding efficiencies; and, strategies to better inform and engage key stakeholders.
Release date: 2016-07-06
10. Statistics Canada International Symposium Series: Proceedings
Journals and periodicals: 11-522-X
Description:
Since 1984, an annual international symposium on methodological issues has been sponsored by Statistics Canada. Proceedings have been available since 1987.
Release date: 2016-03-24

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