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All (162) (20 to 30 of 162 results)

  • 21. Machine Learning Classifier Evaluation Criteria: application to price statistics Archived
    Articles and reports: 11-522-X202100100012
    Description: The modernization of price statistics by National Statistical Offices (NSO) such as Statistics Canada focuses on the adoption of alternative data sources that include the near-universe of all products sold in the country, a scale that requires machine learning classification of the data. The process of evaluating classifiers to select appropriate ones for production, as well as monitoring classifiers once in production, needs to be based on robust metrics to measure misclassification. As commonly utilized metrics, such as the Fß-score may not take into account key aspects applicable to prices statistics in all cases, such as unequal importance of categories, a careful consideration of the metric space is necessary to select appropriate methods to evaluate classifiers. This working paper provides insight on the metric space applicable to price statistics and proposes an operational framework to evaluate and monitor classifiers, focusing specifically on the needs of the Canadian Consumer Prices Index and demonstrating discussed metrics using a publicly available dataset.

    Key Words: Consumer price index; supervised classification; evaluation metrics; taxonomy

    Release date: 2021-11-05
  • 22. Need for Speed: Using fastText (Machine Learning) to Code the Labour Force Survey Archived
    Articles and reports: 11-522-X202100100013
    Description: Statistics Canada’s Labour Force Survey (LFS) plays a fundamental role in the mandate of Statistics Canada. The labour market information provided by the LFS is among the most timely and important measures of the Canadian economy’s overall performance. An integral part of the LFS monthly data processing is the coding of respondent’s industry according to the North American Industrial Classification System (NAICS), occupation according to the National Occupational Classification System (NOC) and the Primary Class of Workers (PCOW). Each month, up to 20,000 records are coded manually. In 2020, Statistics Canada worked on developing Machine Learning models using fastText to code responses to the LFS questionnaire according to the three classifications mentioned previously. This article will provide an overview on the methodology developed and results obtained from a potential application of the use of fastText into the LFS coding process. 

    Key Words: Machine Learning; Labour Force Survey; Text classification; fastText.

    Release date: 2021-11-05
  • 23. 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
  • 24. 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
  • 25. 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
  • 26. 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
  • 27. 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
  • 28. Theory and Application of Raking for Time Series
    19-22-0007
    Description:

    Course Duration: 2 days

    Course Cost: There is no cost for Statistics Canada employees. The cost for external participants is $200 per day.

    Course Language: Offered in English and in French

    Pre-requisites: Knowledge of SAS is highly recommended. Knowledge equivalent to the SAS 9 Programming 1: Essentials course is a minimum.

    To familiarize participants with raking methods and software. Raking deals with the problem of restoring cross-sectional aggregation constraints in time series systems. Optionally, temporal constraints can also be preserved. We also use the words reconciliation and balancing.

    Benefits to Participants: Upon completion of the course, the participants will be able to understand some of the raking techniques in use at Statistics Canada. They will acquire the technical knowledge to run PROC TSRAKING, aSAS procedure developed at Statistics Canada. The course is practical, technical and theoretical.

    Course outline: Introduction; One and two dimensional raking with or without annual constraints; Alterability coefficients; Pro-rating and proportional iterative raking methods; Raking method implemented in PROC TSRAKING: numerical optimization approach with alterability coefficients; Time series system with multiple raking rules; Movement preservation.

    Release date: 2021-10-13
  • 29. 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
  • 30. 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
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 (102)

Analysis (102) (0 to 10 of 102 results)

  • 1. A case study of using Splink: Census duplicate matching Archived
    Articles and reports: 11-522-X202200100002
    Description: The authors used the Splink probabilistic linkage package developed by the UK Ministry of Justice, to link census data from England and Wales to itself to find duplicate census responses. A large gold standard of confirmed census duplicates was available meaning that the results of the Splink implementation could be quality assured. This paper describes the implementation and features of Splink, gives details of the settings and parameters that we used to tune Splink for our particular project, and gives the results that we obtained.
    Release date: 2024-03-25
  • 2. Record linkage techniques to identify 2021 Canadian Census dwellings in the new Statistical Building Register Archived
    Articles and reports: 11-522-X202200100020
    Description: The reconciliation of 2021 census dwellings with the new Statistical Building Register (SBgR) presented linkage challenges. The Census of Population collected information from various dwelling types. For a large proportion of the population, mailing addresses were at the centre: they were used for reaching out to people and collected as contact info. In parallel, the register environment has been evolving. The agency is transitioning from the Address Register (AR) to the SBgR holding both mailing and location addresses, while also covering non-residential buildings. The reconciliation was conducted using a combination of systems, notably the new Register Matching Engine (RME) for difficult cases. The RME holds an interesting range of sophisticated string comparators. A deterministic linkage approach was used, while incorporating some data knowledge like the entropy. Through metadata, the matching expert could also reduce the amounts of false positives and false negatives.
    Release date: 2024-03-25
  • 3. 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: 2024-03-25
  • 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: 2024-01-03
  • 5. Are “immortals” an issue for survival estimates derived from Canadian Cancer Registry data?
    Articles and reports: 82-003-X202301200002
    Description: The validity of survival estimates from cancer registry data depends, in part, on the identification of the deaths of deceased cancer patients. People whose deaths are missed seemingly live on forever and are informally referred to as “immortals”, and their presence in registry data can result in inflated survival estimates. This study assesses the issue of immortals in the Canadian Cancer Registry (CCR) using a recently proposed method that compares the survival of long-term survivors of cancers for which “statistical” cure has been reported with that of similar people from the general population.
    Release date: 2023-12-20
  • 6. 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
  • 7. 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
  • 8. 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
  • 9. 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
  • 10. 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
Reference (54)

Reference (54) (50 to 60 of 54 results)

  • 51. Age, Sex, Marital Status and Common-law Status (Reference Products : Technical Reports : 1996 Census of Population) Archived
    Surveys and statistical programs – Documentation: 92-353-X
    Description:

    This report deals with age, sex, marital status and common-law status. It is aimed at informing users about the complexity of the data and any difficulties that could affect their use. It explains the theoretical framework and definitions used to gather the data, and describes unusual circumstances that could affect data quality. Moreover, the report touches upon data capture, edit and imputation, and deals with the historical comparability of the data.

    Release date: 1999-04-16
  • 52. General Aspects of the Survey of Labour and Income Dynamics (SLID) Archived
    Surveys and statistical programs – Documentation: 75F0002M1998005
    Description:

    This article gives an overview of the main goals of the Survey of Labour and Income Dynamics (SLID) and the methodology used.

    Release date: 1998-12-30
  • 53. Data Inventory Project
    Surveys and statistical programs – Documentation: 5190
    Description: The Data Inventory Project is a government-wide stock-taking of federal data holdings within departments that are part of the Policy Research Data Group to determine the broad range of data holdings that could address the medium to longer-term priorities. The inventory is comprised of the metadata on datasets held within the various departments and will be linked, when possible, to specific key policy issues.
  • 54. Respondent Selection Study for the General Social Survey
    Surveys and statistical programs – Documentation: 8014
    Description: This study will be used to determine which method would be the most effective to select households in Canada for any given survey that is conducted by Statistics Canada.
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