Data analysis
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- Census of Population (13)
- Canadian Community Health Survey - Annual Component (7)
- Labour Force Survey (7)
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- Survey of Labour and Income Dynamics (3)
- Longitudinal Immigration Database (3)
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- Monthly Oil and Other Liquid Petroleum Products Pipeline Survey (2)
- Uniform Crime Reporting Survey (2)
- Census of Agriculture (2)
- Households and the Environment Survey (2)
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- Longitudinal Employment Analysis Program (2)
- Canada's International Transactions in Services (1)
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- National Tourism Indicators (1)
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- Annual Electricity Supply and Disposition Survey (1)
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- Vital Statistics - Death Database (1)
- Annual Demographic Estimates: Canada, Provinces and Territories (1)
- Homeowner Repair and Renovation Survey (1)
- Annual Income Estimates for Census Families and Individuals (T1 Family File) (1)
- Annual Survey of Research and Development in Canadian Industry (1)
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- General Social Survey - Victimization (1)
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- Culture Services Trade (1)
- Canadian Community Health Survey - Nutrition (1)
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- Longitudinal and International Study of Adults (1)
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- National Household Survey (1)
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- Survey of Safety in Public and Private Spaces (1)
- Canadian Housing Statistics Program (1)
- Study on International Money Transfers (1)
- Canadian Housing Survey (1)
- Survey on Early Learning and Child Care Arrangements (SELCCA) (1)
- Canadian Perspectives Survey Series (CPSS) (1)
- Canada Mortgage and Housing Corporation (1)
Results
All (289)
All (289) (40 to 50 of 289 results)
- Stats in brief: 89-20-00082021002Description: This video is part of the confidentiality vetting support series and presents examples of how to use SAS to create proportion output for researchers working with confidential data.Release date: 2022-04-27
- Stats in brief: 89-20-00082021003Description: This video is part of the confidentiality vetting support series and presents examples of how to use Stata to create proportion output for researchers working with confidential data.Release date: 2022-04-27
- 43. Confidentiality Vetting Support: Dominance and homogeneity using the tcensus function (Stata) ArchivedStats in brief: 89-20-00082021004Description: This video is part of the confidentiality vetting support series and presents examples of how to use Stata to perform the dominance and homogeneity test while using the Census.Release date: 2022-04-27
- Stats in brief: 89-20-00082021005Description: This video is part of the confidentiality vetting support series and presents examples of how to use R to create proportion output for researchers working with confidential data.Release date: 2022-04-27
- Stats in brief: 89-20-00082021006Description: This video is part of the confidentiality vetting support series and presents examples of how to use R to perform the dominance and homogeneity test while using the Census.Release date: 2022-04-27
- Stats in brief: 11-627-M2022016Description:
This infographic explains the steps involved in collecting data for all Statistics Canada household and business surveys. The responses are compiled, analyzed and used to make important decisions and are kept strictly confidential.
Release date: 2022-02-28 - Articles and reports: 11-522-X202100100029Description:
In line with the path taken by the European Statistical System, Istat is investing on innovative methods to harness Big Data sources and to use them for the production of new and enriched Official Statistics products. Big Data sources are not, in general, directly tractable with traditional statistical techniques, just think of specific data types such as images and texts that are examples of the Variety dimension of Big Data. This motivates and justifies the growing interest of National Statistical Institutes in data science techniques. Istat is currently using data science techniques, including machine learning techniques, in innovation projects and for the publication of experimental statistics. This paper will provide an overview of the main current projects by Istat and will focus on two specific Big Data-based production pipelines, related to the processing of respectively text sources and imagery sources. The paper will highlight the main challenges these two pipelines and the solutions put in place to solve them.
Key Words: Machine Learning; Text Processing; Image Processing; Big Data
Release date: 2021-11-05 - Articles and reports: 11-522-X202100100008Description:
Non-probability samples are being increasingly explored by National Statistical Offices as a complement to probability samples. We consider the scenario where the variable of interest and auxiliary variables are observed in both a probability and non-probability sample. Our objective is to use data from the non-probability sample to improve the efficiency of survey-weighted estimates obtained from the probability sample. Recently, Sakshaug, Wisniowski, Ruiz and Blom (2019) and Wisniowski, Sakshaug, Ruiz and Blom (2020) proposed a Bayesian approach to integrating data from both samples for the estimation of model parameters. In their approach, non-probability sample data are used to determine the prior distribution of model parameters, and the posterior distribution is obtained under the assumption that the probability sampling design is ignorable (or not informative). We extend this Bayesian approach to the prediction of finite population parameters under non-ignorable (or informative) sampling by conditioning on appropriate survey-weighted statistics. We illustrate the properties of our predictor through a simulation study.
Key Words: Bayesian prediction; Gibbs sampling; Non-ignorable sampling; Statistical data integration.
Release date: 2021-10-29 - Articles and reports: 11-522-X202100100009Description:
Use of auxiliary data to improve the efficiency of estimators of totals and means through model-assisted survey regression estimation has received considerable attention in recent years. Generalized regression (GREG) estimators, based on a working linear regression model, are currently used in establishment surveys at Statistics Canada and several other statistical agencies. GREG estimators use common survey weights for all study variables and calibrate to known population totals of auxiliary variables. Increasingly, many auxiliary variables are available, some of which may be extraneous. This leads to unstable GREG weights when all the available auxiliary variables, including interactions among categorical variables, are used in the working linear regression model. On the other hand, new machine learning methods, such as regression trees and lasso, automatically select significant auxiliary variables and lead to stable nonnegative weights and possible efficiency gains over GREG. In this paper, a simulation study, based on a real business survey sample data set treated as the target population, is conducted to study the relative performance of GREG, regression trees and lasso in terms of efficiency of the estimators.
Key Words: Model assisted inference; calibration estimation; model selection; generalized regression estimator.
Release date: 2021-10-29 - Articles and reports: 11-522-X202100100018Description: Statistics Finland started publishing nowcasts of the trend indicator of output (TIO), the monthly indicator of real economic activity, to answer users´ needs during the Covid-19 pandemic. The indicator was first published in April 2020, at the very beginning of the pandemic in Finland, and had a monthly release schedule until June 2021. The TIO nowcasts are produced using open-source data on truck traffic volumes at about 100 automatic measuring points in the Helsinki/Uusimaa -region and the Economic Sentiment Indicator for Finland. Estimation is done using a machine learning approach and the methodology is based on previous work done by Statistics Finland and ETLA Economic Research.
Key Words: nowcasting; flash estimates; machine learning; experimental statistics.
Release date: 2021-10-29
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Data (2)
Data (2) ((2 results))
- 1. 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
- 2. 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
Analysis (256)
Analysis (256) (0 to 10 of 256 results)
- 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
- Articles and reports: 12-001-X202500200004Description: The class of generalized linear models (GLM) is a flexible generalization of ordinary least squares regression that allows the linear model to be related to the response variable via a link function and assumes the magnitude of the variance of each measurement to be a function of its predicted value. Multicollinearity in GLMs can inflate variances of the estimated coefficients and cause poor prediction in certain regions of the regression space. It may also cause a nonsignificant Wald statistic even when the predictors are highly predictive in a model of the family of GLMs. Little previous research has closely investigated the diagnostics of multicollinearity in GLMs, especially when complex survey data are used. In this paper, we develop variance inflation factors (VIFs) that measure the amount that the variance of a parameter estimator is increased due to multicollinearity in GLMs. We also extend VIFs and condition indexes to apply to complex survey data, accounting for design features, e.g. weights, clusters, and strata. Illustrations of these methods are given using data from a household survey of health and nutrition.Release date: 2025-12-23
- Stats in brief: 89-20-00062025001Description: This video is designed to help you critically assess the data presented to you. No data is perfect. By understanding the strengths and limitations of the data, you can avoid being misled—and make smarter, more informed decisions.Release date: 2025-12-15
- Articles and reports: 11-522-X202500100010Description: Statistics Canada's Labour Force Survey (LFS) plays an essential role in the estimation of labour market conditions in Canada. Periodically, LFS revises its data to the most recent industry and occupational classification versions. Differences in versions can be extensive, including high-level and unit-group structural changes, creations, deletions, split-offs and combination of classification units (classes). Historically, to reconcile split-off classes - where one class splits into multiple classes - a sample of LFS split-off records would be manually recoded to the new classification version. Based on the split-off proportion observed in the recoded sample, a random allocation method would be applied on all data to reflect the changing Canadian labour market over time. This article proposes using machine learning (fastText), constrained to split-off proportions using linear programming, to revise industry and occupation classifications in LFS. The hybrid framework benefits from a text-based revision mechanism while adhering to traditional proportions driven estimates, thus ensuring a minimal impact on the comparability of published labour market indicators.Release date: 2025-09-08
- Articles and reports: 36-28-0001202500300002Description: Government programs are evaluated to measure their effectiveness. This article discusses the benefits of using Statistics Canada data combined with the data collected from the government program to provide a far more comprehensive evaluation than program data alone can offer. The article also summarizes a recent example of a program evaluation that benefited from Statistics Canada data and the expertise of Statistics Canada researchers in analyzing the data.Release date: 2025-03-26
- Articles and reports: 12-001-X202400200004Description: While we avoid specifying the parametric relationship between the study variable and covariates, we illustrate the advantage of including a spatial component to better account for the covariates in our models to make Bayesian predictive inference. We treat each unique covariate combination as an individual stratum, then we use small area estimation techniques to make inference about the finite population mean of the continuous response variable. The two spatial models used are the conditional autoregressive and simple conditional autoregressive models. We include the spatial effects by creating the adjacency matrix via the Mahalanobis distance between covariates. We also show how to incorporate survey weights into the spatial models when dealing with probability survey data. We compare the results of two non-spatial models including the Scott-Smith model and the Battese, Harter, and Fuller model to the spatial models. We illustrate the comparison between the aforementioned models with an application using BMI data from eight counties in California. Our goal is to have neighboring strata yield similar predictions, and to increase the difference between strata that are not neighbors. Ultimately, using the spatial models shows less global pooling compared to the non-spatial models, which was the desired outcome.Release date: 2024-12-20
- Articles and reports: 12-001-X202400200005Description: Adaptive survey designs (ASDs) tailor recruitment protocols to population subgroups that are relevant to a survey. In recent years, effective ASD optimization has been the topic of research and several applications. However, the performance of an optimized ASD over time is sensitive to time changes in response propensities. How adaptation strategies can adjust to such variation over time is not yet fully understood. In this paper, we propose a robust optimization approach in the context of sequential mixed-mode surveys employing Bayesian analysis. The approach is formulated as a mathematical programming problem that explicitly accounts for uncertainty due to time change. ASD decisions can then be made by considering time-dependent variation in conditional mode response propensities and between-mode correlations in response propensities. The approach is demonstrated using a case study: the 2014-2017 Dutch Health Survey. We evaluate the sensitivity of ASD performance to 1) the budget level and 2) the length of applicable historic time-series data. We find there is only a moderate dependence on the budget level and the dependence on historic data is moderated by the amount of seasonality during the year.Release date: 2024-12-20
- Stats in brief: 89-20-00062024003Description: This video is intended for professionals, policymakers, and researchers who are interested in understanding how data linkage can be used to gain deeper insights into various issues. It demonstrates how combining data from different sources can help address gaps in information, leading to better-informed policies and improved outcomes.Release date: 2024-11-25
- 10. Labour Force Survey initiatives under Statistics Canada’s Disaggregated Data Action Plan ArchivedArticles and reports: 11-522-X202200100004Description: In accordance with Statistics Canada’s long-term Disaggregated Data Action Plan (DDAP), several initiatives have been implemented into the Labour Force Survey (LFS). One of the more direct initiatives was a targeted increase in the size of the monthly LFS sample. Furthermore, a regular Supplement program was introduced, where an additional series of questions are asked to a subset of LFS respondents and analyzed in a monthly or quarterly production cycle. Finally, the production of modelled estimates based on Small Area Estimation (SAE) methodologies resumed for the LFS and will include a wider scope with more analytical value than what had existed in the past. This paper will give an overview of these three initiatives.Release date: 2024-03-25
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Reference (26)
Reference (26) (0 to 10 of 26 results)
- 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
- Surveys and statistical programs – Documentation: 11-633-X2024004Description: The Longitudinal Immigration Database (IMDB) is a comprehensive source of data that plays a key role in the understanding of the economic behaviour of immigrants. It is the only annual Canadian dataset that allows users to study the characteristics of immigrants to Canada at the time of admission and their economic outcomes and regional (inter-provincial) mobility over a time span of more than 40 years.Release date: 2024-12-09
- Surveys and statistical programs – Documentation: 11-633-X2024001Description: The Longitudinal Immigration Database (IMDB) is a comprehensive source of data that plays a key role in the understanding of the economic behaviour of immigrants. It is the only annual Canadian dataset that allows users to study the characteristics of immigrants to Canada at the time of admission and their economic outcomes and regional (inter-provincial) mobility over a time span of more than 35 years.Release date: 2024-01-22
- Surveys and statistical programs – Documentation: 32-26-0006Description: This report provides data quality information pertaining to the Agriculture–Population Linkage, such as sources of error, matching process, response rates, imputation rates, sampling, weighting, disclosure control methods and data quality indicators.Release date: 2023-08-25
- Surveys and statistical programs – Documentation: 98-20-00032021011Description: This video explains the key concepts of different levels of aggregation of income data such as household and family income; income concepts derived from key income variables such as adjusted income and equivalence scale; and statistics used for income data such as median and average income, quartiles, quintiles, deciles and percentiles.Release date: 2023-03-29
- Surveys and statistical programs – Documentation: 98-20-00032021012Description: This video builds on concepts introduced in the other videos on income. It explains key low-income concepts - Market Basket Measure (MBM), Low income measure (LIM) and Low-income cut-offs (LICO) and the indicators associated with these concepts such as the low-income gap and the low-income ratio. These concepts are used in analysis of the economic well-being of the population.Release date: 2023-03-29
- Surveys and statistical programs – Documentation: 11-633-X2022009Description: The Longitudinal Immigration Database (IMDB) is a comprehensive source of data that plays a key role in the understanding of the economic behaviour of immigrants. It is the only annual Canadian dataset that allows users to study the characteristics of immigrants to Canada at the time of admission and their economic outcomes and regional (inter-provincial) mobility over a time span of more than 35 years.
This report will discuss the IMDB data sources, concepts and variables, record linkage, data processing, dissemination, data evaluation and quality indicators, comparability with other immigration datasets, and the analyses possible with the IMDB.
Release date: 2022-12-05 - Notices and consultations: 98-26-0001Description:
This white paper presents Statistics Canada’s planned approach to the 2021 Census of Population and provides a clear explanation of the processes behind the census program, touching on historical, legal, operational and content aspects. Statistics Canada recognizes that it is important to not only successfully conduct the census, but also to be transparent and informative about the way in which those efforts are accomplished. Painting a Portrait of Canada: The 2021 Census of Population gives readers an exclusive, detailed look at how census data is collected, analyzed and given back to Canadians, in the form of high-quality statistical information, used to make evidence-based decisions in Canadian society.
Release date: 2020-07-20 - Surveys and statistical programs – Documentation: 91F0015M2016012Description:
This article provides information on using family-related variables from the microdata files of Canada’s Census of Population. These files exist internally at Statistics Canada, in the Research Data Centres (RDCs), and as public-use microdata files (PUMFs). This article explains certain technical aspects of all three versions, including the creation of multi-level variables for analytical purposes.
Release date: 2016-12-22 - 10. The Data Warehouse and analytical tools to facilitate the integration of the Canadian Macroeconomic Accounts ArchivedSurveys and statistical programs – Documentation: 11-522-X201700014710Description:
The Data Warehouse has modernized the way the Canadian System of Macroeconomic Accounts (MEA) are produced and analyzed today. Its continuing evolution facilitates the amounts and types of analytical work that is done within the MEA. It brings in the needed element of harmonization and confrontation as the macroeconomic accounts move toward full integration. The improvements in quality, transparency, and timeliness have strengthened the statistics that are being disseminated.
Release date: 2016-03-24