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All (2,478) (10 to 20 of 2,478 results)

  • 11. Estimating monthly indicators for Consumer Confidence using Structural Time Series Models
    Articles and reports: 12-001-X202500200003
    Description: In this paper a model-based inference procedure based on a multivariate structural time series model is developed for the production of monthly figures about consumer confidence. The input for the model are five series of direct estimates for the indices that measure consumer confidence, which are derived from the Dutch Consumer Survey. The model improves the accuracy of the direct estimates, since it provides a better separation of measurement errors and sampling errors from estimated target parameters. The standard errors for the month-to-month changes are clearly smaller under the time series model. A second problem addressed in this paper is related to the transition to a new survey process in 2017. Structural time series models in combination with a parallel run are applied to estimate discontinuities induced by the redesign. An algorithm designed for the consumer confidence variables is developed to construct uninterrupted input series for the aforementioned structural time series model. This inference method facilitated a smooth transition to a new survey design and resulted in uninterrupted series about consumer confidence that date back to 1986. The method is implemented for the production of official monthly figures on consumer confidence in the Netherlands.
    Release date: 2025-12-23
  • 12. Collinearity diagnostics in generalized linear models fitted with survey data
    Articles and reports: 12-001-X202500200004
    Description: 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
  • 13. Quantile balancing inverse probability weighting for non-probability samples
    Articles and reports: 12-001-X202500200005
    Description: The use of non-probability data sources for statistical purposes and for official statistics has become increasingly popular in recent years. However, statistical inference based on non-probability samples is made more difficult by nature of their biasedness and lack of representativity. In this paper we propose quantile balancing inverse probability weighting estimator (QBIPW) for non-probability samples. We apply the idea of Harms and Duchesne (2006) allowing the use of quantile information in the estimation process to reproduce known totals and the distribution of auxiliary variables. We discuss the estimation of the QBIPW probabilities and its variance. Our simulation study has demonstrated that the proposed estimators are robust against model mis-specification and, as a result, help to reduce bias and mean squared error. Finally, we applied the proposed methods to estimate the share of job vacancies aimed at Ukrainian workers in Poland using an integrated set of administrative and survey data about job vacancies.
    Release date: 2025-12-23
  • 14. Quality indicators for representation in administrative data
    Articles and reports: 12-001-X202500200006
    Description: National Statistical Institutes (NSIs) are directing resources into advancing the use of administrative data in official statistics. Administrative data, however, are not developed for the purpose of producing statistics rather as a result of an event or transaction relating to administrative procedures of organizations, public administrations and government agencies. Therefore, it is essential to check the quality of the administrative data with respect to sources of error, particularly representativeness to the target population. In this paper, we utilize the strength of probability-based reference samples or censuses that can be used to detect the lack of representativeness in administrative data and introduce quality indicators based on distance metrics and representativity indicators (R-indicators). We demonstrate their application with a simulation study and discuss a real application applied on a UK Office for National Statistics (ONS) administrative dataset.
    Release date: 2025-12-23
  • 15. Integrating probability and non-probability samples through deep learning-based mass imputation
    Articles and reports: 12-001-X202500200007
    Description: 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
  • 16. Generalized regression estimation under misspecified sample design
    Articles and reports: 12-001-X202500200008
    Description: 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
  • 17. Improved small area inference from data integration using global-local priors
    Articles and reports: 12-001-X202500200009
    Description: We present and apply methodology to improve inference for small area parameters by using data from several sources. This work extends Cahoy and Sedransk (2023) who showed how to integrate summary statistics from several sources. Our methodology uses hierarchical global-local prior distributions to make inferences for the proportion of individuals in Florida’s counties who do not have health insurance. Results from an extensive simulation study show that this methodology will provide improved inference by using several data sources. Among the five model variants evaluated the ones using horseshoe priors for all variances have better performance than the ones using lasso priors for the local variances.
    Release date: 2025-12-23
  • 18. Performance of hierarchical Bayes small area estimators using noninformative and informative priors with an application to the Canadian Labor Force Survey
    Articles and reports: 12-001-X202500200010
    Description: In this paper, we study the performance of hierarchical Bayes (HB) small area estimators using noninformative and informative priors. We apply the Bayesian models of You and Chapman (2006) and You (2021) to the Canadian Labor Force Survey (LFS) data and evaluate the impact of the priors on the HB estimators. A Bayesian model comparison and simulation study are also conducted. Our results indicate that a correct informative prior can lead to very good results, and noninformative priors can also perform very well. Incorrect informative priors can lead to poor results in terms of large bias and large coefficient of variation (CV). Noninformative priors are recommended in practice for HB small area estimation unless correctly specified informative priors are available. Informative priors are particularly useful when the number of small areas is relatively small.
    Release date: 2025-12-23
  • 19. Approximate hierarchical Bayes small area estimation using Natural Exponential Family with Quadratic Variance Function and poststratification
    Articles and reports: 12-001-X202500200011
    Description: We propose an approximate hierarchical Bayes approach that uses the Natural Exponential Family with Quadratic Variance Function (NEF-QVF) in combining information from multiple sources to improve traditional survey estimates of finite population means for small areas. Unlike other Bayesian approaches in finite population sampling, we do not assume a model for all units of the finite population and do not require linking sampled units to the finite population frame. We assume a model only for the finite population units in which the outcome variable is observed; because, for these units, the assumed model can be checked using existing statistical tools. We do not posit an elaborate model on the true means for unobserved units. Instead, we assume that population means of cells with the same combination of factor levels are identical across small areas, and that the population mean for a cell is identical to the mean of the observed units in that cell. We apply our proposed methodology to a real-life survey, linking information from multiple disparate data sources. We also provide practical ways of model selection that can be applied to a wider class of models under similar setting but for a diverse range of scientific problems.
    Release date: 2025-12-23
  • 20. Modified observed best prediction strategies for small area estimation with unit-level data
    Articles and reports: 12-001-X202500200012
    Description: The observed best prediction (OBP) under a nested-error regression (NER) model was previously proposed using a design-based mean squared prediction error (MSPE) as a tool to derive the best predictive estimator (BPE). A recent study showed the OBP under the NER model may suffer from numerical instability when computing the BPE. We propose several modifications of the OBP under the NER model, including ones using a model-based MSPE to derive the BPE, to improve the numerical stability and predictive performance. We compare the performance of the modified OBP strategies with the existing methods in a simulation study. A real-data example is discussed.
    Release date: 2025-12-23
Data (10)

Data (10) ((10 results))

  • 1. Social Policy Simulation Database and Model (SPSD/M)
    Public use microdata: 89F0002X
    Description: 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
  • 2. National Address Register
    Profile of a community or region: 46-26-0002
    Description: 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
  • 3. PASSAGES microsimulation model
    Table: 89-26-0006
    Description: 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 Archived
    Data Visualization: 71-607-X2020010
    Description: 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
  • 5. Income divergence index (D-index) by census tract
    Table: 11-10-0074-01
    Geography: Census tract
    Frequency: Occasional
    Description:

    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 Archived
    Data Visualization: 71-607-X2019010
    Description: 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
  • 7. Findings of the Canadian Vehicle Fuel Pilot Survey Archived
    Table: 53-500-X
    Description:

    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
  • 8. National Tourism Indicators, Historical Estimates Archived
    Table: 13-220-X
    Description: 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 Archived
    Table: 11-516-X
    Description:

    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 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 (2,036)

Analysis (2,036) (0 to 10 of 2,036 results)

  • 1. Statistical Methods Reference Documents
    Surveys and statistical programs – Documentation: 19-20-0001
    Description: 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: 2026-05-11
  • 2. Analytical Studies: Methods and References
    Journals and periodicals: 11-633-X
    Description: 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
  • 3. Distributions of Household Economic Accounts, estimates of asset, liability and net worth distributions, 2010 to 2025, technical methodology and quality report
    Articles and reports: 13-604-M2026001
    Description: This documentation outlines the methodology used to develop the Distributions of household economic accounts published in January 2026 for the reference years 2010 to 2025. It describes the framework and the steps implemented to produce distributional information aligned with the National Balance Sheet Accounts and other national accounts concepts. It also includes a report on the quality of the estimated distributions.
    Release date: 2026-01-29
  • 4. Effects of model misspecification on small area estimators
    Articles and reports: 12-001-X202500200001
    Description: 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
  • 5. Who’s asking? Interviewer effects on unit nonresponse in the Household Finance and Consumption Survey
    Articles and reports: 12-001-X202500200002
    Description: This study examines interviewer effects on household nonresponse in three waves of the Household Finance and Consumption Survey (HFCS) in Austria using a multilevel model. Addressing nonresponse at its source is crucial for maintaining survey data quality and representativeness. Our findings indicate that the variation in response behavior explained by interviewer effects decreased from about one-third in the first wave to 7% in the third wave. Effective interviewers tend to have a university degree, be married, homeowners, and have a larger workload. Additionally, higher mean wages in the household’s municipality negatively affect survey participation. These insights suggest targeted interviewer selection and training strategies to improve response rates.
    Release date: 2025-12-23
  • 6. Estimating monthly indicators for Consumer Confidence using Structural Time Series Models
    Articles and reports: 12-001-X202500200003
    Description: In this paper a model-based inference procedure based on a multivariate structural time series model is developed for the production of monthly figures about consumer confidence. The input for the model are five series of direct estimates for the indices that measure consumer confidence, which are derived from the Dutch Consumer Survey. The model improves the accuracy of the direct estimates, since it provides a better separation of measurement errors and sampling errors from estimated target parameters. The standard errors for the month-to-month changes are clearly smaller under the time series model. A second problem addressed in this paper is related to the transition to a new survey process in 2017. Structural time series models in combination with a parallel run are applied to estimate discontinuities induced by the redesign. An algorithm designed for the consumer confidence variables is developed to construct uninterrupted input series for the aforementioned structural time series model. This inference method facilitated a smooth transition to a new survey design and resulted in uninterrupted series about consumer confidence that date back to 1986. The method is implemented for the production of official monthly figures on consumer confidence in the Netherlands.
    Release date: 2025-12-23
  • 7. Collinearity diagnostics in generalized linear models fitted with survey data
    Articles and reports: 12-001-X202500200004
    Description: 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
  • 8. Quantile balancing inverse probability weighting for non-probability samples
    Articles and reports: 12-001-X202500200005
    Description: The use of non-probability data sources for statistical purposes and for official statistics has become increasingly popular in recent years. However, statistical inference based on non-probability samples is made more difficult by nature of their biasedness and lack of representativity. In this paper we propose quantile balancing inverse probability weighting estimator (QBIPW) for non-probability samples. We apply the idea of Harms and Duchesne (2006) allowing the use of quantile information in the estimation process to reproduce known totals and the distribution of auxiliary variables. We discuss the estimation of the QBIPW probabilities and its variance. Our simulation study has demonstrated that the proposed estimators are robust against model mis-specification and, as a result, help to reduce bias and mean squared error. Finally, we applied the proposed methods to estimate the share of job vacancies aimed at Ukrainian workers in Poland using an integrated set of administrative and survey data about job vacancies.
    Release date: 2025-12-23
  • 9. Quality indicators for representation in administrative data
    Articles and reports: 12-001-X202500200006
    Description: National Statistical Institutes (NSIs) are directing resources into advancing the use of administrative data in official statistics. Administrative data, however, are not developed for the purpose of producing statistics rather as a result of an event or transaction relating to administrative procedures of organizations, public administrations and government agencies. Therefore, it is essential to check the quality of the administrative data with respect to sources of error, particularly representativeness to the target population. In this paper, we utilize the strength of probability-based reference samples or censuses that can be used to detect the lack of representativeness in administrative data and introduce quality indicators based on distance metrics and representativity indicators (R-indicators). We demonstrate their application with a simulation study and discuss a real application applied on a UK Office for National Statistics (ONS) administrative dataset.
    Release date: 2025-12-23
  • 10. Integrating probability and non-probability samples through deep learning-based mass imputation
    Articles and reports: 12-001-X202500200007
    Description: 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
Reference (380)

Reference (380) (10 to 20 of 380 results)

  • 11. Survey on the Official Language Minority Population: User guide, 2022 Archived
    Surveys and statistical programs – Documentation: 89-657-X2024009
    Description: The Survey on the Official Language Minority Population (SOLMP) user guide contains a description of the survey, along with survey concepts and definitions and an overview of the content development. The target and survey populations, the sample design and sample size are described in the Methodology section. Finally, in the Data Collection module, the collection period and instrument, modes of collection, collection and communications strategies and response rates are provided.
    Release date: 2024-12-16
  • 12. Longitudinal Immigration Database (IMDB) Technical Report, 2023
    Surveys and statistical programs – Documentation: 11-633-X2024004
    Description: 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
  • 13. Multi-year Consolidated Plan for Research, Modelling and Data Development, 2024 to 2026
    Surveys and statistical programs – Documentation: 11-633-X2024005
    Description: The Analytical Studies and Modelling Branch is the research (ASMB), modelling, training and access hub of Statistics Canada. It focuses on leveraging the agency’s vast data holdings to generate in-depth insights that support evidence-based policy making and to enable others to do so through analytical training and data access. The ASMB, like other program areas in the agency, works to support Statistics Canada’s overall mission of delivering insights through data for a better Canada.
    Release date: 2024-12-06
  • 14. Census Technical Report: Coverage
    Surveys and statistical programs – Documentation: 98-303-X
    Description: The Coverage Technical Report will present the errors included in census data that result from persons who are either missed (not enumerated) or enumerated more than once. The population coverage error is one of the most important types of errors because it affects the accuracy of not only population counts, but also all the census data results that describe the characteristics of the population universe.
    Release date: 2024-10-23
  • 15. Concepts and Methods Guide for the Indigenous Peoples Survey and Indigenous Peoples Survey – Nunavut Inuit Supplement, 2022
    Surveys and statistical programs – Documentation: 89-653-X2024002
    Description: This guide is intended to provide a detailed review of both the 2022 IPS and IPS–NIS with respect to subject matter and methodological approaches. It is designed to help data users by serving as a guide to the concepts and measures of the survey as well as the technical details of the survey’s design, field work and data processing. This guide is meant to provide users with helpful information on how to use and interpret survey results. The discussion on data quality also allows users to review the strengths and limitations of the data for their particular needs.

    Chapter 1 of this guide provides an overview of the 2022 IPS and IPS–NIS by introducing the survey background and objectives. Chapter 2 outlines the survey’s themes and explains the key concepts and definitions used for the survey. Chapters 3 to 6 cover important aspects of the survey methodology, sampling design, data collection and processing. Chapters 7 and 8 review issues of data quality and caution users about comparing 2022 IPS or IPS–NIS data with data from other sources. Chapter 9 outlines the survey products available to the public, including data tables, analytical articles and reference material. The appendices provide a comprehensive list of survey indicators, extra coding categories and standard classifications used on both the IPS and the IPS–NIS. Lastly, a glossary of survey terms and information on confidence intervals is also provided.
    Release date: 2024-08-14
  • 16. Guide to the Job Vacancy and Wage Survey
    Surveys and statistical programs – Documentation: 75-514-G
    Description: The Guide to the Job Vacancy and Wage Survey contains a dictionary of concepts and definitions, and covers topics such as survey methodology, data collection, processing, and data quality. The guide covers both components of the survey: the job vacancy component, which is quarterly, and the wage component, which is annual.
    Release date: 2024-06-18
  • 17. Census of Agriculture: Data quality report
    Surveys and statistical programs – Documentation: 32-26-0007
    Description: Census of Agriculture data provide statistical information on farms and farm operators at fine geographic levels and for small subpopulations. Quality evaluation activities are essential to ensure that census data are reliable and that they meet user needs.

    This report provides data quality information pertaining to the Census of Agriculture, such as sources of error, error detection, disclosure control methods, data quality indicators, response rates and collection rates.
    Release date: 2024-02-06
  • 18. Longitudinal Immigration Database (IMDB) Technical Report, 2022
    Surveys and statistical programs – Documentation: 11-633-X2024001
    Description: 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
  • 19. Labour Force Survey Response Rates, September 2023 Archived
    Surveys and statistical programs – Documentation: 75-005-M2023001
    Description: This document provides information on the evolution of response rates for the Labour Force Survey (LFS) and a discussion of the evaluation of two aspects of data quality that ensure the LFS estimates continue providing an accurate portrait of the Canadian labour market.
    Release date: 2023-10-30
  • 20. Sampling and Weighting Technical Report, Census of Population
    Surveys and statistical programs – Documentation: 98-306-X
    Description:

    This report describes sampling, weighting and estimation procedures used in the Census of Population. It provides operational and theoretical justifications for them, and presents the results of the evaluations of these procedures.

    Release date: 2023-10-04

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  • Use “or” between keywords to obtain results that contain one or more search terms.

    • For example, labour or­ force or survey will limit results to products containing any or all of these words.

How does the search work?

  • This search looks for results that have your query term in the title, description, subject, geography, product number or other information about the product.

    • For example, when searching the term ‘Diseases’, all results with the word ‘Diseases’ in the title, description or tagged the subject appear.

  • The text within an article or publication are not searched. For full text searching of articles, use the site search.

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