Other content related to Statistical methods

Skip to filters. View results.

Results

All (166)

All (166) (10 to 20 of 166 results)

11. 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: 2023-10-11
12. 2021 Census Geography and Tools: GeoSuite and GeoSearch
19-22-0011
Description: An introduction to the role geography plays in Statistics Canada data. Viewers will learn about the different geographic levels Statistics Canada uses and how they are related, as well as two products - GeoSuite and GeoSearch - that the public can use to find detailed information for any place in Canada. Two case studies will be shown to demonstrate applications of these two products.
https://www.statcan.gc.ca/en/services/webinars/19220011
Release date: 2023-09-12
13. Construction of a Northern Market Basket Measure of poverty for Nunavut
Articles and reports: 75F0002M2022003
Description: This discussion paper describes the proposed methodology for a Northern Market Basket Measure (MBM-N) for Nunavut, 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 to 2021. 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: 2023-06-21
14. Technical paper for the Northern Market Basket Measure of poverty for Yukon and the Northwest Territories
Articles and reports: 75F0002M2022004
Description:
This technical paper describes the results of the review period, including small adjustments to the disposable income amounts used in the discussion paper Construction of a Northern Market Basket Measure (MBM-N) of poverty for Yukon and the Northwest Territories. It also marks the end of the review period for the MBM-N for Yukon and the Northwest Territories by presenting the latest poverty estimates for reference year 2020.

Release date: 2022-11-03
15. The System of National Quality-Of-Life Statistics: Conceptual Framework
Articles and reports: 11-633-X2022002
Description:
This paper provides a description of the conceptual framework of the modernized system of national quality-of-life statistics that Statistics Canada is planning to implement within the next 5 to 10 years. Consistent with 50 years of dialogue on the improvement of social statistics, the conceptual framework proposes the adoption of a micro-level approach to describe how society operates and help create a cohesive and integrated system of quality-of-life statistics.

Release date: 2022-06-01
16. Guide to the Census of Agriculture
Surveys and statistical programs – Documentation: 32-26-0002
Description:
This reference guide may be useful to both new and experienced users who wish to familiarize themselves with and find specific information about the Census of Agriculture.

It provides an overview of the Census of Agriculture communications, content determination, collection, processing, data quality evaluation and dissemination activities. It also summarizes the key changes to the census and other useful information.

Release date: 2022-04-14
17. Created Equal
Stats in brief: 45-20-00032022002
Description:
Canada’s diversity and rich cultural heritage have been shaped by the people who have come from all over the world to call it home. But even in our multicultural society, eliminating all forms of discrimination remains a challenge. In this episode, we turn a critical eye to the ways that cognitive bias risks perpetuating systemic racism. Statistics are supposed to accurately reflect the world around us, but are all data created equal? Join our guests, Sarah Messou-Ghelazzi, Communications Officer, Filsan Hujaleh, Analyst with the Centre for Social Data Insights and Innovation, and Jeff Latimer, Director General - Accountable for Health, Justice, Diversity and Populations at Statistics Canada as we explore the role data can play to make Canada a more equal society for all.

Release date: 2022-03-16
18. The System of National Quality-of-life Statistics: Future Directions
Articles and reports: 11-633-X2021006
Description:
This paper describes the current thinking at Statistics Canada about future directions in social statistics. It describes how the system of statistics on social statistics (which would be renamed quality of life statistics) will look like in the next 5 to 10 years if Statistics Canada adopts the transformative methodologies and dissemination products that are needed to meet the growing demand for more disaggregated, timely, granular, accessible and more responsive statistics on quality of life.

Release date: 2022-01-31
19. The Importance of Data Quality
19-22-0009
Description:
Join us as Statistics Canada’s Quality Secretariat will give a presentation on the importance of data quality. We are living in an exciting time for data: sources are more abundant, they are being generated in innovative ways, and they are available quicker than ever. However, a data source is not only worthless if it does not meet basic quality standards – it can be misleading, and worse than having no data at all! Statistics Canada’s Quality Secretariat has a mandate to promote good quality practices within the agency, across the Government of Canada, and internationally. For quality to truly be present, it must be incorporated into each process (from design to analysis) and into the product itself – whether that product is a microdata file or estimates derived from it. We will address why data quality is important and how one can evaluate it in practice. We will cover some basic concepts in data quality (quality assurance vs. control, metadata, etc.), and present data quality as a multidimensional concept. Finally, we will show data quality in action by evaluating a data source together. All data quality literacy levels are welcome. After all, everybody plays a part in quality!

https://www.statcan.gc.ca/en/services/webinars/19220009

Release date: 2022-01-26
20. Intermediate Session on Considerations for Displaying Data - A Case Study
19-22-0008
Description:
Data visualizations are a powerful tool to explore and present ideas. In response to feedback from information session participants, this session uses a case study approach to illustrate how to explore your data and decide which visualizations help tell your audience a data story. Designed for a beginner to intermediate audience, the session focuses on one of the hardest parts of designing graphs and charts: knowing where to start.

https://www.statcan.gc.ca/en/services/information/19220008

Release date: 2021-12-10

Data (1)

Data (1) ((1 result))

1. National Population Health Survey Overview Archived
Table: 82-567-X
Description:
The National Population Health Survey (NPHS) is designed to enhance the understanding of the processes affecting health. The survey collects cross-sectional as well as longitudinal data. In 1994/95 the survey interviewed a panel of 17,276 individuals, then returned to interview them a second time in 1996/97. The response rate for these individuals was 96% in 1996/97. Data collection from the panel will continue for up to two decades. For cross-sectional purposes, data were collected for a total of 81,000 household residents in all provinces (except people on Indian reserves or on Canadian Forces bases) in 1996/97.
This overview illustrates the variety of information available by presenting data on perceived health, chronic conditions, injuries, repetitive strains, depression, smoking, alcohol consumption, physical activity, consultations with medical professionals, use of medications and use of alternative medicine.
Release date: 1998-07-29

Analysis (105)

Analysis (105) (20 to 30 of 105 results)

21. Collaborative Data Science within Government of Canada: Development of R libraries for common tasks with Open Canada data Archived
Articles and reports: 11-522-X202100100028
Description:
Many Government of Canada groups are developing codes to process and visualize various kinds data, often duplicating each other’s efforts, with sub-optimal efficiency and limited level of code quality reviewing. This paper informally presents a working-level approach to addressing this technical problem. The idea is to collaboratively build a common repository of code and knowledgebase for use by anyone in the public sector to perform many common data science tasks, and, in doing that, help each other to master both the data science coding skills and the industry standard collaborative practices. The paper explains why R language is used as the language of choice for collaborative data science code development. It summaries R advantages and addresses its limitations, establishes the taxonomy of discussion topics of highest interested to the GC data scientists working with R, provides an overview of used collaborative platforms, and presents the results obtained to date. Even though the code knowledgebase is developed mainly in R, it is meant to be valuable also for data scientists coding in Python and other development environments. Key Words: Collaboration; Data science; Data Engineering; R; Open Government; Open Data; Open Science

Release date: 2021-10-29
22. Survey Data Integration for Regression Analysis Using Model Calibration Archived
Articles and reports: 11-522-X202100100001
Description:
We consider regression analysis in the context of data integration. To combine partial information from external sources, we employ the idea of model calibration which introduces a “working” reduced model based on the observed covariates. The working reduced model is not necessarily correctly specified but can be a useful device to incorporate the partial information from the external data. The actual implementation is based on a novel application of the empirical likelihood method. The proposed method is particularly attractive for combining information from several sources with different missing patterns. The proposed method is applied to a real data example combining survey data from Korean National Health and Nutrition Examination Survey and big data from National Health Insurance Sharing Service in Korea.
Key Words: Big data; Empirical likelihood; Measurement error models; Missing covariates.

Release date: 2021-10-15
23. Responsible use of machine learning at Statistics Canada Archived
Articles and reports: 11-522-X202100100002
Description:
A framework for the responsible use of machine learning processes has been developed at Statistics Canada. The framework includes guidelines for the responsible use of machine learning and a checklist, which are organized into four themes: respect for people, respect for data, sound methods, and sound application. All four themes work together to ensure the ethical use of both the algorithms and results of machine learning. The framework is anchored in a vision that seeks to create a modern workplace and provide direction and support to those who use machine learning techniques. It applies to all statistical programs and projects conducted by Statistics Canada that use machine learning algorithms. This includes supervised and unsupervised learning algorithms. The framework and associated guidelines will be presented first. The process of reviewing projects that use machine learning, i.e., how the framework is applied to Statistics Canada projects, will then be explained. Finally, future work to improve the framework will be described.
Keywords: Responsible machine learning, explainability, ethics

Release date: 2021-10-15
24. Predicting transitions into and out of poverty using machine learning Archived
Articles and reports: 11-522-X202100100003
Description:
The increasing size and richness of digital data allow for modeling more complex relationships and interactions, which is the strongpoint of machine learning. Here we applied gradient boosting to the Dutch system of social statistical datasets to estimate transition probabilities into and out of poverty. Individual estimates are reasonable, but the main advantages of the approach in combination with SHAP and global surrogate models are the simultaneous ranking of hundreds of features by their importance, detailed insight into their relationship with the transition probabilities, and the data-driven identification of subpopulations with relatively high and low transition probabilities. In addition, we decompose the difference in feature importance between general and subpopulation into a frequency and a feature effect. We caution for misinterpretation and discuss future directions.
Key Words: Classification; Explainability; Gradient boosting; Life event; Risk factors; SHAP decomposition.

Release date: 2021-10-15
25. Modernizing Construction Indicators Through Machine Learning and Satellite Imagery Archived
Articles and reports: 11-522-X202100100019
Description: Official statistical agencies must continually seek new methods and techniques that can increase both program efficiency and product relevance. The U.S. Census Bureau’s measurement of construction activity is currently a resource-intensive endeavor, relying heavily on monthly survey response via questionnaires and extensive field data collection. While our data users continually require more timely and granular data products, the traditional survey approach and associated collection cost and respondent burden limits our ability to meet that need. In 2019, we began research on whether the application of machine learning techniques to satellite imagery could accurately estimate housing starts and completions while meeting existing monthly indicator timelines at a cost equal to or less than existing methods. Using historical Census construction survey data in combination with targeted satellite imagery, the team trained, tested, and validated convolutional neural networks capable of classifying images by their stage of construction demonstrating the viability of a data science-based approach to producing official measures of construction activity.
Key Words: Official Statistics; Housing Starts, Machine Learning, Satellite Imagery
Release date: 2021-10-15
26. Statistics 101: Exploring measures of central tendency Archived
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
27. Statistics 101: Exploring measures of dispersion Archived
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
28. Study: Measuring proximity to services and amenities Archived
Stats in brief: 11-001-X202104628783
Description: Release published in The Daily – Statistics Canada’s official release bulletin
Release date: 2021-02-15
29. Measuring proximity to services and amenities: An experimental set of indicators for neighbourhoods and localities
Articles and reports: 18-001-X2020001
Description:
This paper presents the methodology used to generate the first nationwide database of proximity measures and the results obtained with a first set of ten measures. The computational methods are presented as a generalizable model due to the fact that it is now possible to apply similar methods to a multitude of other services or amenities, in a variety of alternative specifications.
Release date: 2021-02-15
30. Statistical standards: A national research and development standard Archived
Stats in brief: 11-627-M2020072
Description:
This infographic provides an overview of the Canadian Research and Development Classification (CRDC), a national standard jointly developed by the Canada Foundation for Innovation (CFI), the Canadian Institutes of Health Research (CIHR), the Natural Sciences and Engineering Research Council of Canada (NSERC), the Social Sciences and Humanities Research Council of Canada (SSHRC), and Statistics Canada.
Release date: 2020-10-05

Reference (55)

Reference (55) (0 to 10 of 55 results)

1. Concepts and Methods Guide for the Indigenous Peoples Survey and Indigenous Peoples Survey – Nunavut Inuit Supplement, 2022
Surveys and statistical programs – Documentation: 89-653-X2024002
Description: This guide is intended to provide a detailed review of both the 2022 IPS and IPS–NIS with respect to subject matter and methodological approaches. It is designed to help data users by serving as a guide to the concepts and measures of the survey as well as the technical details of the survey’s design, field work and data processing. This guide is meant to provide users with helpful information on how to use and interpret survey results. The discussion on data quality also allows users to review the strengths and limitations of the data for their particular needs.
Chapter 1 of this guide provides an overview of the 2022 IPS and IPS–NIS by introducing the survey background and objectives. Chapter 2 outlines the survey’s themes and explains the key concepts and definitions used for the survey. Chapters 3 to 6 cover important aspects of the survey methodology, sampling design, data collection and processing. Chapters 7 and 8 review issues of data quality and caution users about comparing 2022 IPS or IPS–NIS data with data from other sources. Chapter 9 outlines the survey products available to the public, including data tables, analytical articles and reference material. The appendices provide a comprehensive list of survey indicators, extra coding categories and standard classifications used on both the IPS and the IPS–NIS. Lastly, a glossary of survey terms and information on confidence intervals is also provided.
Release date: 2024-08-14
2. Guide to the Job Vacancy and Wage Survey
Surveys and statistical programs – Documentation: 75-514-G
Description: The Guide to the Job Vacancy and Wage Survey contains a dictionary of concepts and definitions, and covers topics such as survey methodology, data collection, processing, and data quality. The guide covers both components of the survey: the job vacancy component, which is quarterly, and the wage component, which is annual.
Release date: 2024-06-18
3. Guide to the Census of Agriculture
Surveys and statistical programs – Documentation: 32-26-0002
Description:
This reference guide may be useful to both new and experienced users who wish to familiarize themselves with and find specific information about the Census of Agriculture.

It provides an overview of the Census of Agriculture communications, content determination, collection, processing, data quality evaluation and dissemination activities. It also summarizes the key changes to the census and other useful information.

Release date: 2022-04-14
4. Multi-year Consolidated Plan for Research, Modelling and Data Development, 2021 to 2023 Archived
Surveys and statistical programs – Documentation: 11-633-X2021005
Description:
The Analytical Studies and Modelling Branch (ASMB) is the research arm of Statistics Canada mandated to provide high-quality, relevant and timely information on economic, health and social issues that are important to Canadians. The branch strategically makes use of expert knowledge and a broad range of data sources and modelling techniques to address the information needs of a broad range of government, academic and public sector partners and stakeholders through analysis and research, modeling and predictive analytics, and data development. The branch strives to deliver relevant, high-quality, timely, comprehensive, horizontal and integrated research and to enable the use of its research through capacity building and strategic dissemination to meet the user needs of policy makers, academics and the general public.
This Multi-year Consolidated Plan for Research, Modelling and Data Development outlines the priorities for the branch over the next two years.
Release date: 2021-08-12
5. Statistics Canada Data Strategy
Surveys and statistical programs – Documentation: 89-26-0003
Description:
Statistics Canada Data Strategy (SCDS) provides a course of action for managing and leveraging the agency’s data assets to ensure their optimal use and value while maintaining public trust. As Statistics Canada is the nation’s trusted provider of high-quality data and information to support evidence-based policy and decision making, the SCDS also naturally includes the agency’s plan for providing support and data expertise to other government organizations (federal, provincial and territorial), non-governmental organizations, the private sector, academia, and other national and international communities).
The SCDS provides a roadmap for how Statistics Canada will continue to govern and manage its valuable data assets as part of its modernization agenda and in alignment with and response to other federal government strategies and initiatives. These federal strategies include the Data Strategy for the Federal Public Service, Canada’s 2018-2020 National Action Plan on Open Government, and the Treasury Board Secretariat Digital Operations Strategic Plan: 2018-2022.
Release date: 2020-04-30
6. National Household Survey: Aboriginal Peoples
Surveys and statistical programs – Documentation: 99-011-X
Description:
This topic presents data on the Aboriginal peoples of Canada and their demographic characteristics. Depending on the application, estimates using any of the following concepts may be appropriate for the Aboriginal population: (1) Aboriginal identity, (2) Aboriginal ancestry, (3) Registered or Treaty Indian status and (4) Membership in a First Nation or Indian band. Data from the 2011 National Household Survey are available for the geographical locations where these populations reside, including 'on reserve' census subdivisions and Inuit communities of Inuit Nunangat as well as other geographic areas such as the national (Canada), provincial and territorial levels.
Analytical products
The analytical document provides analysis on the key findings and trends in the data, and is complimented with the short articles found in NHS in Brief and the NHS Focus on Geography Series.
Data products
The NHS Profile is one data product that provides a statistical overview of user selected geographic areas based on several detailed variables and/or groups of variables. Other data products include data tables which represent a series of cross tabulations ranging in complexity and are available for various levels of geography.
Release date: 2019-10-29
7. Classifying Cannabis in the Canadian Statistical System Archived
Surveys and statistical programs – Documentation: 11-621-M2018105
Description:
Statistics Canada needs to respond to the legalization of cannabis for non-medical use by measuring various aspects of the introduction of cannabis in the Canadian economy and society. An important part of measuring the economy and society is using statistical classifications. It is common practice with classifications that they are updated and revised as new industries, products, occupations and educational programs are introduced into the Canadian economy and society. This paper describes the changes to the various statistical classifications used by Statistics Canada in order to measure the introduction of legal non-medical cannabis.
Release date: 2019-07-24
8. Analytical Studies Branch Annual Consolidated Plan for Research, Data Development and Modelling, 2019/2020 Archived
Surveys and statistical programs – Documentation: 11-633-X2019001
Description:
The mandate of the Analytical Studies Branch (ASB) is to provide high-quality, relevant and timely information on economic, health and social issues that are important to Canadians. The branch strategically makes use of expert knowledge and a large range of statistical sources to describe, draw inferences from, and make objective and scientifically supported deductions about the evolving nature of the Canadian economy and society. Research questions are addressed by applying leading-edge methods, including microsimulation and predictive analytics using a range of linked and integrated administrative and survey data. In supporting greater access to data, ASB linked data are made available to external researchers and policy makers to support evidence-based decision making. Research results are disseminated by the branch using a range of mediums (i.e., research papers, studies, infographics, videos, and blogs) to meet user needs. The branch also provides analytical support and training, feedback, and quality assurance to the wide range of programs within and outside Statistics Canada.
Release date: 2019-05-29
9. Transition of Labour Force Survey Data Processing to the Social Survey Processing Environment (SSPE) Archived
Surveys and statistical programs – Documentation: 75-005-M2019001
Description:
The production of statistics from the Labour Force Survey (LFS) involves many activities, one of which is data processing. This step involves the verification and correction of survey data when required in order to produce microdata files. Beginning in January 2019, LFS processing will be transitioned to a new system, the Social Survey Processing Environment. This document describes the development and testing that preceded the implementation of the new system, and demonstrates that the transition is expected to have minimal impact on LFS estimates and be transparent to users of LFS data.
Release date: 2019-02-08
10. Methodology of the Canadian Labour Force Survey
Surveys and statistical programs – Documentation: 71-526-X
Description:
The Canadian Labour Force Survey (LFS) is the official source of monthly estimates of total employment and unemployment. Following the 2011 census, the LFS underwent a sample redesign to account for the evolution of the population and labour market characteristics, to adjust to changes in the information needs and to update the geographical information used to carry out the survey. The redesign program following the 2011 census culminated with the introduction of a new sample at the beginning of 2015. This report is a reference on the methodological aspects of the LFS, covering stratification, sampling, collection, processing, weighting, estimation, variance estimation and data quality.
Release date: 2017-12-21

Report a problem or mistake on this page

Date modified:: 2024-09-20

Other content related to Statistical methods

Filter results by

Keyword(s)

Type

Geography

Survey or statistical program

Content

Results

All (166) (10 to 20 of 166 results)

Data (1) ((1 result))

Analysis (105) (20 to 30 of 105 results)

Reference (55) (0 to 10 of 55 results)

Other content related to Statistical methods

Filter results by

Keyword(s)

Type

Geography

Survey or statistical program

Content

Results

All (166) (10 to 20 of 166 results)

Data (1) ((1 result))

Analysis (105) (20 to 30 of 105 results)

Reference (55) (0 to 10 of 55 results)

How do I use the filters and the search box?

How do I refine my search?

How does the search work?

How are the results ordered?

How are the results ordered?