Statistical methods
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Selected geographical area:Canada
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Results
All (2,480)
All (2,480) (0 to 10 of 2,480 results)
- Surveys and statistical programs – Documentation: 19-20-0001Description: Documents in this series provide insight into the statistical methods used by Statistics Canada to produce official statistics. They include introductory material, in-depth descriptions of techniques and methods, best practices, and guidelines. All documents have undergone review to ensure that they conform to Statistics Canada's mandate and adhere to generally accepted methodological standards and practices.Release date: 2026-06-08
- Surveys and statistical programs – Documentation: 19-20-00012026002Description: This reference document provides answers on selected topics related to the use, interpretation, and calculation of trend-cycle estimates for seasonally adjusted data. It is designed to complement more technical discussions of seasonal adjustment and trend-cycle estimation found in Statistics Canada publications and reference manuals.Release date: 2026-06-08
- Articles and reports: 36-28-0001202600500003Description: This spotlight article outlines practical methods for assessing the economic impacts of public programs delivered by federal agencies and Crown corporations. It summarizes key steps in conducting quantitative impact analysis, including data linkage, cohort construction and implementation of quasi causal estimators.Release date: 2026-05-27
- 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: 2026-05-27
- Journals and periodicals: 75F0002MDescription: This series provides detailed documentation on income developments, including survey design issues, data quality evaluation and exploratory research.Release date: 2026-05-20
- Surveys and statistical programs – Documentation: 19-20-00012026001Description: This reference document provides nontechnical answers on selected topics related to the use and interpretation of seasonally adjusted data. It is designed to complement more technical discussions of seasonal adjustment found in Statistics Canada publications and reference manuals.Release date: 2026-05-11
- Notices and consultations: 13-605-XDescription: This product contains articles related to the latest methodological, conceptual developments in the Canadian System of Macroeconomic Accounts as well as the analysis of the Canadian economy. It includes articles detailing new methods, concepts and statistical techniques used to compile the Canadian System of Macroeconomic Accounts. It also includes information related to new or expanded data products, provides updates and supplements to information found in various guides and analytical articles touching upon a broad range of topics related to the Canadian economy.Release date: 2026-05-04
- Surveys and statistical programs – Documentation: 11-633-X2026002Description: Recent changes in Canada’s immigration levels have heightened interest in understanding how immigration affects housing demand. This article develops a methodological framework for projecting housing use associated with permanent residents (PRs) and non-permanent residents (NPRs) under alternative immigration scenarios. The framework applies observed per capita housing use rates from the Census of Population to estimate incremental housing use by tenure over time.Release date: 2026-04-24
- Surveys and statistical programs – Documentation: 11-633-X2026001Description: This report defines key concepts related to area-level analysis and introduces area-level measures developed and utilized at Statistics Canada for health analysis. It also provides a decision-making framework and practical recommendations to help researchers select appropriate methods. The goal is to guide readers on when area-level analysis is appropriate and what type of area-level measure is suitable to achieve research objectives.Release date: 2026-03-05
- Public use microdata: 89F0002XDescription: The SPSD/M is a static microsimulation model designed to analyse financial interactions between governments and individuals in Canada. It can compute taxes paid to and cash transfers received from government. It is comprised of a database, a series of tax/transfer algorithms and models, analytical software and user documentation.Release date: 2026-02-12
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Data (10)
Data (10) ((10 results))
- Public use microdata: 89F0002XDescription: The SPSD/M is a static microsimulation model designed to analyse financial interactions between governments and individuals in Canada. It can compute taxes paid to and cash transfers received from government. It is comprised of a database, a series of tax/transfer algorithms and models, analytical software and user documentation.Release date: 2026-02-12
- Profile of a community or region: 46-26-0002Description: The National Address Register (NAR) is a list of commercial and residential addresses in Canada that are extracted from Statistics Canada's Building Register and deemed non-confidential.Release date: 2025-12-19
- Table: 89-26-0006Description: PASSAGES is an open-source dynamic microsimulation model aimed at supporting policy analysis and research relating to Canadian retirement income system outcomes at the individual and family level. The publicly available version includes a synthetic starting database, a model, and documentation. A confidential starting database is also available.Release date: 2025-03-12
- 4. Canadian Statistical Geospatial Explorer Hub ArchivedData Visualization: 71-607-X2020010Description: The Canadian Statistical Geospatial Explorer empowers users to discover geo enabled data holdings of Statistics Canada at various levels of geography including at the neighbourhood level. Users are able to visualize, thematically map, spatially explore and analyze, export and consume data in various formats. Users can also view the data superimposed on satellite imagery, topographic and street layers.Release date: 2024-08-21
- 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 - 6. Housing Data Viewer ArchivedData Visualization: 71-607-X2019010Description: The Housing Data Viewer is a visualization tool that allows users to explore Statistics Canada data on a map. Users can use the tool to navigate, compare and export data.Release date: 2019-10-30
- Table: 53-500-XDescription:
This report presents the results of a pilot survey conducted by Statistics Canada to measure the fuel consumption of on-road motor vehicles registered in Canada. This study was carried out in connection with the Canadian Vehicle Survey (CVS) which collects information on road activity such as distance traveled, number of passengers and trip purpose.
Release date: 2004-10-21 - Table: 13-220-XDescription: In the 1997 edition, new and revised benchmarks were introduced for 1992 and 1988. The indicators are used to monitor supply, demand and employment for tourism in Canada on a timely basis. The annual tables are derived using the National Income and Expenditure Accounts (NIEA) and various industry and travel surveys. Tables providing actual data and percentage changes, for seasonally adjusted current and constant price estimates are included. In addition, an analytical section provides graphs, and time series of first differences, percentage changes, and seasonal factors for selected indicators. Data are published from 1987 and the publication will be available on the day of release. New data are included in the demand tables for non-tourism commodities produced by non-tourism industries and in the employment tables covering direct tourism employment generated by non-tourism industries. This product was commissioned by the Canadian Tourism Commission to provide annual updates for the Tourism Satellite Account.Release date: 2003-01-08
- 9. Historical Statistics of Canada ArchivedTable: 11-516-XDescription:
The second edition of Historical statistics of Canada was jointly produced by the Social Science Federation of Canada and Statistics Canada in 1983. This volume contains about 1,088 statistical tables on the social, economic and institutional conditions of Canada from the start of Confederation in 1867 to the mid-1970s. The tables are arranged in sections with an introduction explaining the content of each section, the principal sources of data for each table, and general explanatory notes regarding the statistics. In most cases, there is sufficient description of the individual series to enable the reader to use them without consulting the numerous basic sources referenced in the publication.
The electronic version of this historical publication is accessible on the Internet site of Statistics Canada as a free downloadable document: text as HTML pages and all tables as individual spreadsheets in a comma delimited format (CSV) (which allows online viewing or downloading).
Release date: 1999-07-29 - 10. National Population Health Survey Overview ArchivedTable: 82-567-XDescription:
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 (2,037)
Analysis (2,037) (60 to 70 of 2,037 results)
- Journals and periodicals: 11-522-XDescription: Since 1984, an annual international symposium on methodological issues has been sponsored by Statistics Canada. Proceedings have been available since 1987.Release date: 2025-09-08
- Articles and reports: 12-001-X202500100001Description: Geoffrey J.C. Hole (or Geoff, as he likes to be called) was born on January 24, 1940 at Shardeloes, Amersham, Buckinghamshire, England, to Charles William Hole and Sybil Winifred Hole, formerly Morge. He completed a BSc Honours in Mathematics in 1961, and a Postgraduate Diploma in Statistics at Manchester University the following year. He started his career as a mathematical statistician in London, England, working successively for the National Coal Board (1962-63), the Central Electricity Generating Board (1963-66), and the Electricity Council (1966-67), where his title was Economist. He moved to Canada in 1967 to join the Dominion Bureau of Statistics (DBS) as a survey methodologist. In 1971-72, he was Chief of Census Operations, Methodology and Quality Control Section, and Assistant Coordinator, Socio-Economic Survey Methods Section. He then took a one-year leave of absence to complete an MSc (Econ) in Statistics at the London School of Economics. In 1973, Geoff returned to the DBS, which had become Statistics Canada, as Chief, Methodology Group V, Business Survey Methods Division. In 1974, he was appointed Director, Institutions and Agriculture Survey Methods Division, and, as of 1986, Director, Business Survey Methods Division. His career culminated when he became Director, Social Survey Methods Division, in 1987. He held that position until his retirement, on September 29, 2004. In addition to his long-term involvement at Statistics Canada, including as a member of the Editorial Board of Survey Methodology between 1983 and 1987, Geoff was very active in the Statistical Society of Canada (SSC), serving among others as Chair of the Program Committee for the 1986 Annual Meeting at the Banff Centre, in Alberta, and President of the SSC in 1989-90. He was also Program Chair for a joint conference of the International Association of Survey Statisticians and the International Association for Official Statistics which was held in Aguascalientes, Mexico, in 1998.Release date: 2025-06-30
- Articles and reports: 12-001-X202500100002Description: Ivan Fellegi is an expert in statistical science and a public servant who was the Chief Statistician of Canada from 1985 to 2008. This article briefly recounts his early life, long-spanning career and influential research contributions. It includes an interview conducted in February 2017 to mark the 60th year of service of Ivan Fellegi’s career at Statistics Canada.Release date: 2025-06-30
- Articles and reports: 12-001-X202500100003Description: In recent years, there has been a significant interest in machine learning in national statistical offices. Thanks to their flexibility, these methods may prove useful at the nonresponse treatment stage. In this article, we conduct an empirical investigation in order to compare several machine learning procedures in terms of bias and efficiency. In addition to the classical machine learning procedures, we assess the performance of ensemble approaches that make use of different machine learning procedures to produce a set of weights adjusted for nonresponse.Release date: 2025-06-30
- Articles and reports: 12-001-X202500100004Description: Survey data collection often is plagued by unit and item nonresponse. To reduce reliance on strong assumptions about the missingness mechanisms, statisticians can use information about population marginal distributions known, for example, from censuses or administrative databases. One approach that does so is the Missing Data with Auxiliary Margins, or MD-AM, framework, which uses multiple imputation for both unit and item nonresponse so that survey-weighted estimates accord with the known marginal distributions. However, this framework relies on specifying and estimating a joint distribution for the survey data and nonresponse indicators, which can be computationally and practically daunting in data with many variables of mixed types. We propose two adaptations to the MD-AM framework to simplify the imputation task. First, rather than specifying a joint model for unit respondents’ data, we use random hot deck imputation while still leveraging the known marginal distributions. Second, instead of sampling from conditional distributions implied by the joint model for the missing data due to item nonresponse, we apply multiple imputation by chained equations for item nonresponse before imputation for unit nonresponse. Using simulation studies with nonignorable missingness mechanisms, we demonstrate that the proposed approach can provide more accurate point and interval estimates than models that do not leverage the auxiliary information. We illustrate the approach using data on voter turnout from the U.S. Current Population Survey.Release date: 2025-06-30
- Articles and reports: 12-001-X202500100005Description: In this paper, we derive a second-order unbiased (or nearly unbiased) mean squared prediction error (MSPE) estimator of the empirical best linear unbiased predictor (EBLUP) of a small area mean for a semi-parametric extension to the well-known Fay-Herriot model. Specifically, we derive our MSPE estimator essentially assuming certain moment conditions on both the sampling errors and random effects distributions. The normality-based Prasad-Rao MSPE estimator has a surprising robustness property in that it remains second-order unbiased under the non-normality of random effects when a simple Prasad-Rao method-of-moments estimator is used for the variance component and the sampling error distribution is normal. We show that the normality-based MSPE estimator is no longer second-order unbiased when the sampling error distribution has non-zero kurtosis or when the Fay-Herriot moment method is used to estimate the variance component, even when the sampling error distribution is normal. Interestingly, when the simple method-of moments estimator is used for the variance component, our proposed MSPE estimator does not require the estimation of kurtosis of the random effects. Results of a simulation study on the accuracy of the proposed MSPE estimator, under non-normality of both sampling and random effects distributions, are also presented.Release date: 2025-06-30
- Articles and reports: 12-001-X202500100006Description: Survey practitioners have increasingly embraced the benefits of modern machine learning techniques, including classification and regression tree algorithms, in the development of nonresponse adjustments. These methods, which do not require a predefined functional relationship between outcomes and predictors, offer a practical means of conducting variable selection and deriving interpretable structures that link response propensity with explanatory variables. However, when applying these algorithms to survey data, it is common to overlook crucial factors like sampling weights, as well as sample design features such as stratification and clustering. To bridge this shortcoming, we propose an extension of the Chi-square Automatic Interaction Detector (CHAID) approach, and we describe the design-based asymptotic properties of the resulting “survey CHAID” (sCHAID) method. To facilitate the practical use of sCHAID, we incorporate a Rao-Scott correction into the splitting criterion, accounting for the survey design. Using data from the U.S. American Community Survey, we illustrate the use of the method and evaluate its performance through comparisons with existing weighted and unweighted algorithms.Release date: 2025-06-30
- Articles and reports: 12-001-X202500100007Description: We introduce a novel approach to model-assisted calibration estimation in survey sampling using generalized entropy. The method builds upon recent work by Kwon, Kim and Qiu (2024) and extends it to a model-assisted framework. Unlike traditional calibration techniques, this approach employs a generalized entropy function as the objective for optimization and incorporates a debiasing calibration constraint to ensure design consistency. The proposed estimator is shown to be asymptotically equivalent to an augmented generalized regression (GREG) estimator. It allows for unequal model variance, potentially improving efficiency when the sampling design is informative. The paper presents both design-based and model-based justifications for the method, along with asymptotic properties and variance estimation techniques. Computational aspects are discussed, including an unconstrained optimization approach that facilitates implementation, especially for high-dimensional auxiliary variables. The method’s performance is evaluated through a simulation study, demonstrating its effectiveness in improving estimation efficiency, particularly when the sampling design is informative.Release date: 2025-06-30
- Articles and reports: 12-001-X202500100008Description: Tightened budgets, continuing decrease of response rates in traditional probability surveys and increasing pressure by users for more timely data, has stimulated research on the use of nonprobability sample data, such as administrative records, web scraping, mobile phone data and voluntary internet surveys, for inference on finite population parameters like means and totals. These data are often easier, faster and cheaper to collect than traditional probability samples. However, a major concern with the use of this kind of data for official statistics is their nonrepresentativeness due to possible selection bias, which if not accounted for properly, could bias the inference. In this article, we review and discuss methods considered in the literature to deal with this problem and propose new methods, distinguishing between methods based on integration of the nonprobability sample with an appropriate probability sample, and methods that base the inference solely on the nonprobability sample. Empirical illustrations, based on simulated data are provided.Release date: 2025-06-30
- Articles and reports: 12-001-X202500100009Description: BigData users and the BigData research community are expanding rapidly, while statisticians at large are seemingly becoming divided between those who are enthusiastic and those who are concerned, if not downright hostile. Is BigData also a big step ahead, truly advancing our ability to extract meaningful information and actual knowledge from data? Is BigData underplaying traditional statistical inference as we know it, supplanting survey methodology as a low-cost futuristic option? In this paper I will attempt to unravel the multifaceted relationship bridging BigData to sampling methodology. Starting by reasoning why it should be interesting to look at BigData from a sampling statistician’s perspective, I will delve deeper into the somewhat ambiguous definition of BigData and share some very personal considerations and views on the matter. In the process, several open questions will arise while discussing a personal selection of insights that are traceable through the vast body of statistical literature around BigData and sampling methodology. The discussion will take various angles explored across nine key points, and it will conclude with a forward-looking perspective on a main challenge for future research: addressing the strong assumptions needed to manage deviations from purely randomized data collection.Release date: 2025-06-30
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Reference (381)
Reference (381) (70 to 80 of 381 results)
- Notices and consultations: 12-002-XDescription:
The Research Data Centres (RDCs) Information and Technical Bulletin (ITB) is a forum by which Statistics Canada analysts and the research community can inform each other on survey data uses and methodological techniques. Articles in the ITB focus on data analysis and modelling, data management, and best or ineffective statistical, computational, and scientific practices. Further, ITB topics will include essays on data content, implications of questionnaire wording, comparisons of datasets, reviews on methodologies and their application, data peculiarities, problematic data and solutions, and explanations of innovative tools using RDC surveys and relevant software. All of these essays may provide advice and detailed examples outlining commands, habits, tricks and strategies used to make problem-solving easier for the RDC user.
The main aims of the ITB are:
- the advancement and dissemination of knowledge surrounding Statistics Canada's data; - the exchange of ideas among the RDC-user community;- the support of new users; - the co-operation with subject matter experts and divisions within Statistics Canada.
The ITB is interested in quality articles that are worth publicizing throughout the research community, and that will add value to the quality of research produced at Statistics Canada's RDCs.
Release date: 2015-03-25 - Surveys and statistical programs – Documentation: 99-002-X2011001Description:
This report describes sampling and weighting procedures used in the 2011 National Household Survey. It provides operational and theoretical justifications for them, and presents the results of the evaluation studies of these procedures.
Release date: 2015-01-28 - Surveys and statistical programs – Documentation: 99-002-XDescription: This report describes sampling and weighting procedures used in the 2011 National Household Survey. It provides operational and theoretical justifications for them, and presents the results of the evaluation studies of these procedures.Release date: 2015-01-28
- Surveys and statistical programs – Documentation: 62F0026M2015001Description:
This report describes the quality indicators produced for the 2013 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.
Release date: 2015-01-22 - Notices and consultations: 75-513-X2014001Description:
Starting with the 2012 reference year, annual individual and family income data is produced by the Canadian Income Survey (CIS). The CIS is a cross-sectional survey developed to provide information on the income and income sources of Canadians, along with their individual and household characteristics. The CIS reports on many of the same statistics as the Survey of Labour and Income Dynamics (SLID), which last reported on income for the 2011 reference year. This note describes the CIS methodology, as well as the main differences in survey objectives, methodology and questionnaires between CIS and SLID.
Release date: 2014-12-10 - 76. Newsletter for Communities ArchivedNotices and consultations: 11-016-XDescription: Statistics Canada's Newsletter for Communities offers information to those working for municipal and community organizations about Statistics Canada's data and services. The newsletter also offers links to data releases of the Census and National Household Survey, videos, tutorials, media advisories, learning sessions and presentations.Release date: 2014-11-20
- 77. Newsletter for Small and Medium-sized Businesses ArchivedNotices and consultations: 11-017-XDescription: Statistics Canada's Newsletter for Small and Medium-sized Businesses offers information to the business community about Statistics Canada's data and services. The newsletter also offers links to data releases of the Census and National Household Survey, videos, tutorials, media advisories, learning sessions and presentations.Release date: 2014-11-20
- Surveys and statistical programs – Documentation: 13-605-X201400514088Description:
An overview of the Canadian Government Finance Statistics (CGFS) framework; how it relates to other government statistics such as the Canadian System of Macroeconomic Accounts and the Public Accounts; and the new GFS data products available to users
Release date: 2014-11-07 - Notices and consultations: 13-605-X201400414107Description:
Beginning in November 2014, International Trade in goods data will be provided on a Balance of Payments (BOP) basis for additional country detail. In publishing this data, BOP-based exports to and imports from 27 countries, referred to as Canada’s Principal Trading Partners (PTPs), will be highlighted for the first time. BOP-based trade in goods data will be available for countries such as China and Mexico, Brazil and India, South Korea, and our largest European Union trading partners, in response to substantial demand for information on these countries in recent years. Until now, Canada’s geographical trading patterns have been examined almost exclusively through analysis of Customs-based trade data. Moreover, BOP trade in goods data for these countries will be available alongside the now quarterly Trade in Services data as well as annual Foreign Direct Investment data for many of these Principal Trading Partners, facilitating country-level international trade and investment analysis using fully comparable data. The objective of this article is to introduce these new measures. This note will first walk users through the key BOP concepts, most importantly the concept of change in ownership. This will serve to familiarize analysts with the Balance of Payments framework for analyzing country-level data, in contrast to Customs-based trade data. Second, some preliminary analysis will be reviewed to illustrate the concepts, with provisional estimates for BOP-based trade with China serving as the principal example. Lastly, we will outline the expansion of quarterly trade in services to generate new estimates of trade for the PTPs and discuss future work in trade statistics.
Release date: 2014-11-04 - 80. Challenges and Lessons Learned with the Implementation of Car Chips in the Fuel Consumption Survey ArchivedSurveys and statistical programs – Documentation: 11-522-X201300014258Description:
The National Fuel Consumption Survey (FCS) was created in 2013 and is a quarterly survey that is designed to analyze distance driven and fuel consumption for passenger cars and other vehicles weighing less than 4,500 kilograms. The sampling frame consists of vehicles extracted from the vehicle registration files, which are maintained by provincial ministries. For collection, FCS uses car chips for a part of the sampled units to collect information about the trips and the fuel consumed. There are numerous advantages to using this new technology, for example, reduction in response burden, collection costs and effects on data quality. For the quarters in 2013, the sampled units were surveyed 95% via paper questionnaires and 5% with car chips, and in Q1 2014, 40% of sampled units were surveyed with car chips. This study outlines the methodology of the survey process, examines the advantages and challenges in processing and imputation for the two collection modes, presents some initial results and concludes with a summary of the lessons learned.
Release date: 2014-10-31
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