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Results
All (207)
All (207) (0 to 10 of 207 results)
- Journals and periodicals: 11-633-XDescription: 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: 2026-04-24
- Articles and reports: 12-001-X202500200001Description: Nested error regression models are commonly used to incorporate unit specific auxiliary variables to improve small area estimates. When the mean structure of the model is misspecified, the design-based mean squared prediction error (MSPE) of Empirical Best Linear Unbiased Predictors (EBLUP) generally increases. The Observed Best Prediction (OBP) method has been proposed with the intent to improve on the design-based MSPE over EBLUP. In this paper, we conduct a Monte Carlo simulation experiments to understand the effect of misspsecification of mean structures on different small area estimators. Our findings suggest that the OBP using unit-level auxiliary variables does not outperform the EBLUP in terms of design-based MSPE, unless the number of small areas m is extremely large. Conversely, the performance of OBP significantly improves when area-level auxiliary variables are employed. This paper includes both analytical and numerical evidence to demonstrate these observations, providing practical insights for addressing model misspecification in small area estimation (SAE).Release date: 2025-12-23
- Articles and reports: 12-001-X202500200007Description: Although probability samples have been regarded as the gold standard to collect information for population-based study, non-probability samples have been used frequently in practice due to low cost, convenience, and the lack of the sampling frame for the survey. Naïve estimates based on non-probability samples without any adjustments may be misleading due to selection bias. Recently, a valid data integration approach that includes mass imputation, propensity score weighting, and calibration has been used to improve the representativeness of non-probability samples. The effectiveness of the mass imputation approach depends on the underlying model assumptions. In this paper, we propose using deep learning for the mass imputation in the combining of probability and non-probability samples and compare it with several modern machine learning-based mass imputation approaches, including generalized additive modeling, regression tree, random forest, and XG-boosting. In the simulation study, deep learning-based approaches have been shown to be more robust and effective than other mass imputation approaches against the failure of underlying model assumptions under non-linearity scenarios.Release date: 2025-12-23
- Articles and reports: 12-001-X202500200008Description: Classical design-based survey estimation relies on a properly specified sampling design for valid inference. We consider the properties of regression estimation under a misspecified sample design, in which the nominal and true inclusion probabilities do not necessarily match. This general misspecified sample design setting encompasses many challenges in the modern survey environment. Under this setting, an asymptotic analysis of the regression estimator, an expression of the bias, and an expression of the variance are presented. Further, a consistent variance estimator is derived and an expression which estimates the bias in-part or in-whole is discussed. This later expression may be used as an indicator of the presence of bias due to misspecification by a practitioner. A simulation study is conducted to support the presented theory.Release date: 2025-12-23
- Articles and reports: 18-001-X2025001Description: This paper brings the analysis of business cluster to a more granular geographic scale by developing a methodology for identifying business clusters at the neighborhood level. The proposed method identifies clusters of businesses at the DB level, which is one of the most granular spatial units of analysis defined by Statistics Canada. The method is developed with an application to four census metropolitan areas (CMAs) of different sizes and for different industry cluster specifications, including simple 2-digit North American Industry Classification System (NAICS) groups as well as industry clusters resulting from groupings of NAICS codes, as defined by Delgado et al. (2014).Release date: 2025-10-10
- Articles and reports: 11-522-X202500100019Description: Accurate and efficient record linkage is crucial for maintaining a comprehensive and current Statistical Business Register (SBR) at Statistics Canada. Linking external business lists to the SBR by name presents computational and methodological challenges, especially as data volumes grow. This paper describes a scalable methodology that employs blocking techniques to constrain the computational search space and integrates multiple similarity measures—from edit distances and n-gram overlaps to embedding-based methods using Sentence-BERT (SBERT)—to identify likely matches. By combining simple character-level comparisons with more advanced semantic embedding methods, the approach can adapt to various naming conventions and complexities. While it does not guarantee superior accuracy in all circumstances, it offers a pragmatic balance between computational feasibility and linkage quality.Release date: 2025-09-08
- Articles and reports: 11-522-X202500100020Description: At Statistics Canada, many data sets are linked with quasi-identifiers such as the first name, last name, or address. In such cases, linkage errors are a potential concern and must be measured. In that regard, previous studies have shown that the evaluation may be based on modeling the number of links from a given record while accounting for all the interactions among the linkage variables and dispensing with clerical reviews, so long as the decision to link two records does not involve other records. In this communication, the methodology is adapted for a class of practical strategies, which violate this constraint by linking the records in consecutive waves, where a given wave links a subset of the records that are not linked in previous waves. In particular, the linkage may be based on a deterministic wave followed by a probabilistic one.Release date: 2025-09-08
- Articles and reports: 11-522-X202500100021Description: Optimal threshold selection is a critical challenge in probabilistic linkage, with significant implications for the accuracy and reliability of linked datasets. This paper analyzes the performance of the neighbour model, a recently proposed error model which models linkage errors by the number of links from each record. Three threshold selection algorithms utilizing the neighbour model were assessed, highlighting the strengths and limitations of each. Their performance was assessed through simulation studies, which demonstrated that methods using the neighbour model achieved lower relative bias compared to two established methods for threshold selection. Additionally, the practical utility was validated through goodness-of-fit tests conducted on four agricultural datasets, showing the potential of the model for use in real-world applications.Release date: 2025-09-08
- Articles and reports: 11-522-X202500100022Description: In Canada, T1 Tax forms are used to report personal income, whether earned as an employee or through self-employment. Income from self-employment, or "T1 Business Income" is reported by sole proprietorships or partnerships. A T1 partnership involves two or more legal entities jointly filing for a shared business. T1 business data is received as individual filings, meaning partnerships are received separately for each partner. Internal record linkage within the T1 business database is performed to identify partnerships and prevent overcoverage within the final population of T1 businesses. This new T1 partnership identification process takes advantage of newer algorithms, such as DBSCAN numerical clustering fuzzy matching, to identify internal linkages. Graph theory is used to construct the list of partnerships from the row-pairs identified in the linkage process.Release date: 2025-09-08
- Articles and reports: 11-522-X202500100023Description: The latest Canadian Census Health and Environment Cohort (CanCHEC) continues a series of population-based microdata linkages focused on population health research by demographic, social and economic characteristics. The 2021 CanCHEC consists of 95.5% of the 2021 Census long-form sample survey records. The records of survey respondents that could not be linked to the Derived Record Depository and those presumed to be duplicates account for the remaining 4.5%. Linkage-adjusted main and replicate weights allow researchers to estimate and evaluate the variance of summary measures about population health in the presence of missed linked pairs to better understand the experiences of diverse population groups.Release date: 2025-09-08
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Data (1)
Data (1) ((1 result))
- Table: 11-10-0074-01Geography: Census tractFrequency: OccasionalDescription:
The divergence index (D-index) describes the degree that families with different income levels are mixing together in neighbourhoods. It compares neighbourhood (census tract, CT) discrete income distributions to a base distribution, which is the income quintiles of the neighbourhood’s census metropolitan area (CMA).
Release date: 2020-06-22
Analysis (199)
Analysis (199) (60 to 70 of 199 results)
- Stats in brief: 89-20-00062021006Description:
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 - 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 - 63. Sustainable Development Goals: Goal 1, No Poverty ArchivedArticles 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
- 64. Sustainable Development Goals: Goal 2, Zero Hunger ArchivedArticles 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
- Articles and reports: 11-637-X202000100004Description: As the fourth goal outlined in the 2030 Agenda for Sustainable Development, Canada and other UN member states have committed to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all by 2030. This 2020 infographic provides an overview of indicators underlying the fourth Sustainable Development Goal in support of Quality Education, 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-X202000100005Description: As the fifth goal outlined in the 2030 Agenda for Sustainable Development, Canada and other UN member states have committed to achieve gender equality and empower all women and girls by 2030. This 2020 infographic provides an overview of indicators underlying the fifth Sustainable Development Goal in support of Gender Equality, 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-X202000100006Description: As the sixth goal outlined in the 2030 Agenda for Sustainable Development, Canada and other UN member states have committed to ensure availability and sustainable management of water and sanitation for all by 2030. This 2020 infographic provides an overview of indicators underlying the sixth Sustainable Development Goal in support of clean water and sanitation, 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-X202000100007Geography: CanadaDescription: As the seventh goal outlined in the 2030 Agenda for Sustainable Development, Canada and other UN member states have committed to ensure access to affordable, reliable, sustainable and modern energy for all by 2030. This 2020 infographic provides an overview of indicators underlying the seventh Sustainable Development Goal in support of affordable and clean energy, 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-X202000100008Description: As the eighth goal outlined in the 2030 Agenda for Sustainable Development, Canada and other UN member states have committed to promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all by 2030. This 2020 infographic provides an overview of indicators underlying the eighth Sustainable Development Goal in support of decent work and economic growth, and the statistics and data sources used to monitor and report on this goal in Canada.Release date: 2020-10-20
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Reference (7)
Reference (7) ((7 results))
- Surveys and statistical programs – Documentation: 84-538-XGeography: CanadaDescription: This electronic publication presents the methodology underlying the production of the life tables for Canada, provinces and territories.Release date: 2023-08-28
- Surveys and statistical programs – Documentation: 82-225-X200701010508Description:
The Record Linkage Overview describes the process used in annual internal record linkage of the Canadian Cancer Registry. The steps include: preparation; pre-processing; record linkage; post-processing; analysis and resolution; resolution entry; and, resolution processing.
Release date: 2008-01-18 - Surveys and statistical programs – Documentation: 11-522-X20050019476Description:
The paper will show how, using data published by Statistics Canada and available from member libraries of the CREPUQ, a linkage approach using postal codes makes it possible to link the data from the outcomes file to a set of contextual variables. These variables could then contribute to producing, on an exploratory basis, a better index to explain the varied outcomes of students from schools. In terms of the impact, the proposed index could show more effectively the limitations of ranking students and schools when this information is not given sufficient weight.
Release date: 2007-03-02 - Surveys and statistical programs – Documentation: 68-514-XDescription:
Statistics Canada's approach to gathering and disseminating economic data has developed over several decades into a highly integrated system for collection and estimation that feeds the framework of the Canadian System of National Accounts.
The key to this approach was creation of the Unified Enterprise Survey, the goal of which was to improve the consistency, coherence, breadth and depth of business survey data.
The UES did so by bringing many of Statistics Canada's individual annual business surveys under a common framework. This framework included a single survey frame, a sample design framework, conceptual harmonization of survey content, means of using relevant administrative data, common data collection, processing and analysis tools, and a common data warehouse.
Release date: 2006-11-20 - Surveys and statistical programs – Documentation: 89-612-XDescription:
This paper describes the structure and linkage of two databases: the Longitudinal Administrative Databank (LAD), and the Longitudinal Immigration Database (IMDB). The combined data associate landed immigrant taxfilers on the LAD with their key characteristics upon immigration. The paper highlights how the combined information, referred to here as the LAD_IMDB, enhances and complements the existing separate databases. The paper compares the full IMDB file with the sample of immigrants to assess the representativeness of the sample file.
Release date: 2004-01-05 - Surveys and statistical programs – Documentation: 81-595-M2003005Geography: CanadaDescription:
This paper develops technical procedures that may enable ministries of education to link provincial tests with national and international tests in order to compare standards and report results on a common scale.
Release date: 2003-05-29 - Surveys and statistical programs – Documentation: 85-602-XDescription:
The purpose of this report is to provide an overview of existing methods and techniques making use of personal identifiers to support record linkage. Record linkage can be loosely defined as a methodology for manipulating and / or transforming personal identifiers from individual data records from one or more operational databases and subsequently attempting to match these personal identifiers to create a composite record about an individual. Record linkage is not intended to uniquely identify individuals for operational purposes; however, it does provide probabilistic matches of varying degrees of reliability for use in statistical reporting. Techniques employed in record linkage may also be of use for investigative purposes to help narrow the field of search against existing databases when some form of personal identification information exists.
Release date: 2000-12-05