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All (358)

All (358) (0 to 10 of 358 results)

1. Exploring a skewness conjecture: Expanding Cochran’s rule to a proportion estimated from a complex sample
Articles and reports: 12-001-X202400200001
Description: Cochran’s rule states that a standard (Wald) two-sided 95% confidence interval around a sample mean drawn from a population with positive skewness is reasonable when the sample size is greater than 25 times the square of the skewness coefficient of the population. We investigate whether a variant of this crude rule applies for a proportion estimated from a stratified simple random sample.
Release date: 2024-12-20
2. Daily rhythm of data quality: Evidence from the Survey of Unemployed Workers in New Jersey
Articles and reports: 12-001-X202400200002
Description: This paper investigates whether survey data quality fluctuates over the day. After laying out the argument theoretically, panel data from the Survey of Unemployed Workers in New Jersey are analyzed. Several indirect indicators of response error are investigated, including item nonresponse, interview completion time, rounding, and measures of the quality of time diary data. The evidence that we assemble for a time of day of interview effect is weak or nonexistent. Item nonresponse and the probability that interview completion time is among the 5% shortest appear to increase in the evening, but a more thorough assessment requires instrumental variables.
Release date: 2024-12-20
3. Recursive Neyman algorithm for optimum sample allocation under box constraints on sample sizes in strata
Articles and reports: 12-001-X202400200003
Description: The optimum sample allocation in stratified sampling is one of the basic issues of survey methodology. It is a procedure of dividing the overall sample size into strata sample sizes in such a way that for given sampling designs in strata the variance of the stratified \pi estimator of the population total (or mean) for a given study variable assumes its minimum. In this work, we consider the optimum allocation of a sample, under lower and upper bounds imposed jointly on sample sizes in strata. We are concerned with the variance function of some generic form that, in particular, covers the case of the simple random sampling without replacement in strata. The goal of this paper is twofold. First, we establish (using the Karush-Kuhn-Tucker conditions) a generic form of the optimal solution, the so-called optimality conditions. Second, based on the established optimality conditions, we derive an efficient recursive algorithm, named RNABOX, which solves the allocation problem under study. The RNABOX can be viewed as a generalization of the classical recursive Neyman allocation algorithm, a popular tool for optimum allocation when only upper bounds are imposed on sample strata-sizes. We implement RNABOX in R as a part of our package stratallo which is available from the Comprehensive R Archive Network (CRAN) repository.
Release date: 2024-12-20
4. Bayesian predictive inference of a finite population mean without specifying the relation between the study variable and the covariates
Articles and reports: 12-001-X202400200004
Description: 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
5. Robust adaptive survey design for time changes in mixed-mode response propensities
Articles and reports: 12-001-X202400200005
Description: 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
6. Investigating mode effects in interviewer variances using two representative multi-mode surveys
Articles and reports: 12-001-X202400200006
Description: As mixed-mode designs become increasingly popular, their effects on data quality have attracted much scholarly attention. Most studies focused on the bias properties of mixed-mode designs; few of them have investigated whether mixed-mode designs have heterogeneous variance structures across modes. While many characteristics of mixed-mode designs, such as varied interviewer usage, systematic differences in respondents, varying levels of social desirability bias, among others, may lead to heterogeneous variances in mode-specific point estimates of population means, this study specifically investigates whether interviewer variances remain consistent across different modes in mixed-mode studies. To address this research question, we utilize data collected from two distinct study designs. In the first design, when interviewers are responsible for either face-to-face or telephone mode, we examine whether there are mode differences in interviewer variances for 1) sensitive political questions, 2) international items, 3) and item missing indicators on international items, using the Arab Barometer wave 6 Jordan data. In the second design, we draw on Health and Retirement Study (HRS) 2016 core survey data to examine the question on three topics when interviewers are responsible for both modes. The topics cover 1) the CESD depression scale, 2) interviewer observations, and 3) the physical activity scale. To account for the lack of interpenetrated designs in both data sources, we include respondent-level covariates in our models. We find significant differences in interviewer variances on one item (twelve items in total) in the Arab Barometer study; whereas for HRS, the results are three out of eighteen. Overall, we find the magnitude of the interviewer variances larger in FTF than TEL on sensitive items. We conduct simulations to understand the power to detect mode effects in the typically modest interviewer sample sizes.
Release date: 2024-12-20
7. Models of linkage error for capture-recapture estimation without clerical reviews
Articles and reports: 12-001-X202400200007
Description: The capture-recapture method can be applied to measure the coverage of administrative and big data sources, in official statistics. In its basic form, it involves the linkage of two sources while assuming a perfect linkage and other standard assumptions. In practice, linkage errors arise and are a potential source of bias, where the linkage is based on quasi-identifiers. These errors include false positives and false negatives, where the former arise when linking a pair of records from different units, and the latter arise when not linking a pair of records from the same unit. So far, the existing solutions have resorted to costly clerical reviews, or they have made the restrictive conditional independence assumption. In this work, these requirements are relaxed by modeling the number of links from a record instead. The same approach may be taken to estimate the linkage accuracy without clerical reviews, when linking two sources that each have some undercoverage.
Release date: 2024-12-20
8. Fully synthetic data for complex surveys
Articles and reports: 12-001-X202400200008
Description: When seeking to release public use files for confidential data, statistical agencies can generate fully synthetic data. We propose an approach for making fully synthetic data from surveys collected with complex sampling designs. Our approach adheres to the general strategy proposed by Rubin (1993). Specifically, we generate pseudo-populations by applying the weighted finite population Bayesian bootstrap to account for survey weights, take simple random samples from those pseudo-populations, estimate synthesis models using these simple random samples, and release simulated data drawn from the models as public use files. To facilitate variance estimation, we use the framework of multiple imputation with two data generation strategies. In the first, we generate multiple data sets from each simple random sample. In the second, we generate a single synthetic data set from each simple random sample. We present multiple imputation combining rules for each setting. We illustrate the repeated sampling properties of the combining rules via simulation studies, including comparisons with synthetic data generation based on pseudo-likelihood methods. We apply the proposed methods to a subset of data from the American Community Survey.
Release date: 2024-12-20
9. A small area estimation approach for reconciling differences in two surveys of recreational fishing effort
Articles and reports: 12-001-X202400200009
Description: Many studies face the problem of comparing estimates obtained with different survey methodology, including differences in frames, measurement instruments, and modes of delivery. The problem arises in multimode surveys and in surveys that are redesigned. Major redesign of survey processes could affect survey estimates systematically, and it is important to quantify and adjust for such discontinuities between the designs to ensure comparability of estimates over time. We propose a small area estimation approach to reconcile two sets of survey estimates, and apply it to two surveys in the Marine Recreational Information Program (MRIP), which monitors recreational fishing along the Atlantic and Gulf coasts of the United States. We develop a log-normal model for the estimates from the two surveys, accounting for temporal dynamics through regression on population size and state-by-wave seasonal factors, and accounting in part for changing coverage properties through regression on wireless telephone penetration. Using the estimated design variances, we develop a regression model that is analytically consistent with the log-normal mean model. We use the modeled design variances in a Fay-Herriot small area estimation procedure to obtain empirical best linear unbiased predictors of the reconciled estimates of fishing effort (requiring predictions at new sets of covariates), and provide an asymptotically valid mean square error approximation.
Release date: 2024-12-20
10. Design-based estimation of small and empty domains in survey data analysis using order constraints
Articles and reports: 12-001-X202400200010
Description: Recent work in survey domain estimation has shown that incorporating a priori assumptions about orderings of population domain means reduces the variance of the estimators and provides smaller confidence intervals with good coverage. Here we show how partial ordering assumptions allow design-based estimation of sample means in domains for which the sample size is zero, with conservative variance estimates and confidence intervals. Order restrictions can also substantially improve estimation and inference in small-size domains. Examples with well-known survey data sets demonstrate the utility of the methods. Code to implement the examples using the R package csurvey is given in the appendix.
Release date: 2024-12-20

Data (22)

Data (22) (0 to 10 of 22 results)

1. The Open Database of Educational Facilities
Table: 37-26-0001
Description: The Open Database of Educational Facilities (ODEF) is a compilation of data from open and internet sources on the locations and types of educational facilities across Canada, originating from municipal, regional, and provincial governments. It is a centralized and harmonized repository of educational facility data made available under the Open Government License - Canada. The database is expected to be updated periodically as new open datasets from government sources become available. The database is made available for download as a zipped comma separated values (csv) file.
Release date: 2024-12-13
2. Rural Canada Business Profiles, 2022
Table: 45-20-00042024004
Description: Rural Canada Business Profiles is a database that provides financial profiles for Small and Medium-Sized Businesses in Canada with total annual revenues of $ 30,000 to $ 5,000,000 and $ 5,000,001 to $ 20,000,000 respectively. These data are available by industry, by province or territory, by legal status of businesses (incorporated and unincorporated) and the distinction of 'Rural and small town (RST)' or 'functional urban' location of businesses. Data released is for 2022.
Release date: 2024-12-09
3. Experimental environmental, social and governance dashboard
Data Visualization: 71-607-X2022002
Description: Environmental, social and governance (ESG) refers to three non-financial factors that can be used to inform the long-term risk and return of an investment. ESG are emerging as a priority for governments, businesses and international organisations. This experimental dashboard offers an overview of the performance over time of selected industries with respect to a collection of ESG indicators.
Release date: 2024-11-20
4. The Open Database of Infrastructure
Table: 34-26-0003
Description: The Open Database of Infrastructure (ODI) contains the locations of bridges, tunnels, solid waste facilities, pedestrian and cycling paths, public transit stops, and potable water, stormwater and wastewater infrastructure.
The ODI is compiled from both open and publicly available data sources and is made available under the Open Government License - Canada. This database is a component of the Linkable Open Data Environment (LODE).
Release date: 2024-11-13
5. Dataset on confirmed cases of COVID-19, Public Health Agency of Canada
Table: 13-26-0003
Description: In collaboration with the Public Health Agency of Canada (PHAC), this data file provides Canadians and researchers with data to monitor only the confirmed cases of coronavirus (COVID-19) in Canada.
Release date: 2024-10-11
6. Business Ownership Diversity Dashboard
Data Visualization: 71-607-X2024026
Description: The Business Ownership Diversity Dashboard allows users to examine the distribution of businesses in Canada by equity and diversity indicators and by business variable dimensions. Equity and diversity indicators include visible minority status, age, immigrant status, Indigenous group, and gender. Business variable dimensions include the location of business operations, revenue size, business size (number of employees), and industry sector.
Release date: 2024-09-12
7. Levels of Drugs in the Wastewater of Canadian Cities
Data Visualization: 71-607-X2024021
Description: This dashboard presents provisional monthly estimates of the levels of amphetamine, cannabis, cocaine (benzoylecgonine), codeine, fentanyl (norfentanyl), ecstasy, methadone, methamphetamine, morphine, and oxycodone in the wastewater of Halifax, Montréal, Toronto, Saskatoon, Prince Albert, Edmonton, and Metro Vancouver. The data that are relevant for monitoring the use of these substances in Canadian cities.
Release date: 2024-09-06
8. Canada at a Glance
Table: 12-581-X
Description: Canada at a Glance presents current statistics on Canadian society, including subjects such as the population, education, health, prices and the economy, among others. Updated yearly, this booklet is a very useful reference for those who want quick access to a current statistical portrait of Canada.
Release date: 2024-09-04
9. Age pyramids
Data Visualization: 98-504-X2021001
Description: Age pyramids are dynamic applications that allow users to see the evolution of the age structure of the Canadian population over a given time period and for selected geographies.
Release date: 2024-06-26
10. Animated Age Pyramids
Data Visualization: 98-504-X
Description: Age pyramids are dynamic applications that allow users to see the evolution of the age structure of the Canadian population over a given time period and for selected geographies.
Release date: 2024-06-26

Analysis (311)

Analysis (311) (0 to 10 of 311 results)

1. Exploring a skewness conjecture: Expanding Cochran’s rule to a proportion estimated from a complex sample
Articles and reports: 12-001-X202400200001
Description: Cochran’s rule states that a standard (Wald) two-sided 95% confidence interval around a sample mean drawn from a population with positive skewness is reasonable when the sample size is greater than 25 times the square of the skewness coefficient of the population. We investigate whether a variant of this crude rule applies for a proportion estimated from a stratified simple random sample.
Release date: 2024-12-20
2. Daily rhythm of data quality: Evidence from the Survey of Unemployed Workers in New Jersey
Articles and reports: 12-001-X202400200002
Description: This paper investigates whether survey data quality fluctuates over the day. After laying out the argument theoretically, panel data from the Survey of Unemployed Workers in New Jersey are analyzed. Several indirect indicators of response error are investigated, including item nonresponse, interview completion time, rounding, and measures of the quality of time diary data. The evidence that we assemble for a time of day of interview effect is weak or nonexistent. Item nonresponse and the probability that interview completion time is among the 5% shortest appear to increase in the evening, but a more thorough assessment requires instrumental variables.
Release date: 2024-12-20
3. Recursive Neyman algorithm for optimum sample allocation under box constraints on sample sizes in strata
Articles and reports: 12-001-X202400200003
Description: The optimum sample allocation in stratified sampling is one of the basic issues of survey methodology. It is a procedure of dividing the overall sample size into strata sample sizes in such a way that for given sampling designs in strata the variance of the stratified \pi estimator of the population total (or mean) for a given study variable assumes its minimum. In this work, we consider the optimum allocation of a sample, under lower and upper bounds imposed jointly on sample sizes in strata. We are concerned with the variance function of some generic form that, in particular, covers the case of the simple random sampling without replacement in strata. The goal of this paper is twofold. First, we establish (using the Karush-Kuhn-Tucker conditions) a generic form of the optimal solution, the so-called optimality conditions. Second, based on the established optimality conditions, we derive an efficient recursive algorithm, named RNABOX, which solves the allocation problem under study. The RNABOX can be viewed as a generalization of the classical recursive Neyman allocation algorithm, a popular tool for optimum allocation when only upper bounds are imposed on sample strata-sizes. We implement RNABOX in R as a part of our package stratallo which is available from the Comprehensive R Archive Network (CRAN) repository.
Release date: 2024-12-20
4. Bayesian predictive inference of a finite population mean without specifying the relation between the study variable and the covariates
Articles and reports: 12-001-X202400200004
Description: 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
5. Robust adaptive survey design for time changes in mixed-mode response propensities
Articles and reports: 12-001-X202400200005
Description: 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
6. Investigating mode effects in interviewer variances using two representative multi-mode surveys
Articles and reports: 12-001-X202400200006
Description: As mixed-mode designs become increasingly popular, their effects on data quality have attracted much scholarly attention. Most studies focused on the bias properties of mixed-mode designs; few of them have investigated whether mixed-mode designs have heterogeneous variance structures across modes. While many characteristics of mixed-mode designs, such as varied interviewer usage, systematic differences in respondents, varying levels of social desirability bias, among others, may lead to heterogeneous variances in mode-specific point estimates of population means, this study specifically investigates whether interviewer variances remain consistent across different modes in mixed-mode studies. To address this research question, we utilize data collected from two distinct study designs. In the first design, when interviewers are responsible for either face-to-face or telephone mode, we examine whether there are mode differences in interviewer variances for 1) sensitive political questions, 2) international items, 3) and item missing indicators on international items, using the Arab Barometer wave 6 Jordan data. In the second design, we draw on Health and Retirement Study (HRS) 2016 core survey data to examine the question on three topics when interviewers are responsible for both modes. The topics cover 1) the CESD depression scale, 2) interviewer observations, and 3) the physical activity scale. To account for the lack of interpenetrated designs in both data sources, we include respondent-level covariates in our models. We find significant differences in interviewer variances on one item (twelve items in total) in the Arab Barometer study; whereas for HRS, the results are three out of eighteen. Overall, we find the magnitude of the interviewer variances larger in FTF than TEL on sensitive items. We conduct simulations to understand the power to detect mode effects in the typically modest interviewer sample sizes.
Release date: 2024-12-20
7. Models of linkage error for capture-recapture estimation without clerical reviews
Articles and reports: 12-001-X202400200007
Description: The capture-recapture method can be applied to measure the coverage of administrative and big data sources, in official statistics. In its basic form, it involves the linkage of two sources while assuming a perfect linkage and other standard assumptions. In practice, linkage errors arise and are a potential source of bias, where the linkage is based on quasi-identifiers. These errors include false positives and false negatives, where the former arise when linking a pair of records from different units, and the latter arise when not linking a pair of records from the same unit. So far, the existing solutions have resorted to costly clerical reviews, or they have made the restrictive conditional independence assumption. In this work, these requirements are relaxed by modeling the number of links from a record instead. The same approach may be taken to estimate the linkage accuracy without clerical reviews, when linking two sources that each have some undercoverage.
Release date: 2024-12-20
8. Fully synthetic data for complex surveys
Articles and reports: 12-001-X202400200008
Description: When seeking to release public use files for confidential data, statistical agencies can generate fully synthetic data. We propose an approach for making fully synthetic data from surveys collected with complex sampling designs. Our approach adheres to the general strategy proposed by Rubin (1993). Specifically, we generate pseudo-populations by applying the weighted finite population Bayesian bootstrap to account for survey weights, take simple random samples from those pseudo-populations, estimate synthesis models using these simple random samples, and release simulated data drawn from the models as public use files. To facilitate variance estimation, we use the framework of multiple imputation with two data generation strategies. In the first, we generate multiple data sets from each simple random sample. In the second, we generate a single synthetic data set from each simple random sample. We present multiple imputation combining rules for each setting. We illustrate the repeated sampling properties of the combining rules via simulation studies, including comparisons with synthetic data generation based on pseudo-likelihood methods. We apply the proposed methods to a subset of data from the American Community Survey.
Release date: 2024-12-20
9. A small area estimation approach for reconciling differences in two surveys of recreational fishing effort
Articles and reports: 12-001-X202400200009
Description: Many studies face the problem of comparing estimates obtained with different survey methodology, including differences in frames, measurement instruments, and modes of delivery. The problem arises in multimode surveys and in surveys that are redesigned. Major redesign of survey processes could affect survey estimates systematically, and it is important to quantify and adjust for such discontinuities between the designs to ensure comparability of estimates over time. We propose a small area estimation approach to reconcile two sets of survey estimates, and apply it to two surveys in the Marine Recreational Information Program (MRIP), which monitors recreational fishing along the Atlantic and Gulf coasts of the United States. We develop a log-normal model for the estimates from the two surveys, accounting for temporal dynamics through regression on population size and state-by-wave seasonal factors, and accounting in part for changing coverage properties through regression on wireless telephone penetration. Using the estimated design variances, we develop a regression model that is analytically consistent with the log-normal mean model. We use the modeled design variances in a Fay-Herriot small area estimation procedure to obtain empirical best linear unbiased predictors of the reconciled estimates of fishing effort (requiring predictions at new sets of covariates), and provide an asymptotically valid mean square error approximation.
Release date: 2024-12-20
10. Design-based estimation of small and empty domains in survey data analysis using order constraints
Articles and reports: 12-001-X202400200010
Description: Recent work in survey domain estimation has shown that incorporating a priori assumptions about orderings of population domain means reduces the variance of the estimators and provides smaller confidence intervals with good coverage. Here we show how partial ordering assumptions allow design-based estimation of sample means in domains for which the sample size is zero, with conservative variance estimates and confidence intervals. Order restrictions can also substantially improve estimation and inference in small-size domains. Examples with well-known survey data sets demonstrate the utility of the methods. Code to implement the examples using the R package csurvey is given in the appendix.
Release date: 2024-12-20

Reference (25)

Reference (25) (0 to 10 of 25 results)

1. Ocean and coastal ecosystem extent: Data product specifications
Geographic files and documentation: 16-510-X2024005
Description: This product contains specifications intended for users of the ocean and coastal ecosystem extent geospatial files. This document provides important technical information for users and links to methodology.
Release date: 2024-12-16
2. Ocean and coastal ecosystem extent: Geospatial files
Geographic files and documentation: 16-510-X2024006
Description: This product contains gridded datasets of ocean and coastal ecosystem extent for ocean and coastal areas of Canada. Mapping the extent of ocean ecosystems is the first stage in creating spatially explicit accounts, to help understand the ocean and coastal ecosystems of Canada. The files cover the area from the coastline, defined by the 2021 Statistics Canada Census of Population, to the outer boundary of Canada’s exclusive economic zone (EEZ). Coastal areas of salt marsh are also included in the files.
Release date: 2024-12-16
3. Persistence and graduation indicators of postsecondary students, 2011/2012 to 2022/2023
Surveys and statistical programs – Documentation: 37-20-00012024003
Description: This technical reference guide is intended for users of the Education and Labour Market Longitudinal Platform (ELMLP). The data for the products associated with this issue are based on the longitudinal Postsecondary Student Information System (PSIS) administrative data files. Statistics Canada has derived a series of annual indicators of public postsecondary students including persistence rates, graduation rates, and average time to graduation by educational qualification, field of study, age group and gender for Canada, the provinces, and the three combined Territories.
Release date: 2024-12-11
4. 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
5. 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
6. Census of Agriculture: Content Consultation Report
Notices and consultations: 95-635-X
Description: To stay relevant, preparing for a new Census of Agriculture requires a thorough evaluation of data requirements. Before each census, Statistics Canada conducts consultations to solicit input and feedback on the Census of Agriculture's content. This report describes those consultations and the process that was followed to test and determine which topics could be potentially retained for the next census.
Release date: 2024-11-27
7. Urban greenness: Data product specifications
Geographic files and documentation: 16-510-X2024004
Description: This product contains specifications intended for users of the urban greenness geospatial files. This document provides important technical information for users and links to methodology.
Release date: 2024-11-21
8. 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
9. Water yield: Geospatial files
Geographic files and documentation: 16-510-X2024001
Description: This product contains gridded datasets of annual and 30-year average estimates of water yield. Tracking water yield—an estimate of renewable water supply derived from National Water Data Archive (HYDAT) streamflow data—provides information to help understand water resources available for human use and ecosystem needs. Annual datasets are available for the years 1971 to 2021 and cover southern Canada. Thirty-year averages are available for 1971-2000, 1981-2010, and 1991-2020. They cover the terrestrial and freshwater extent of Canada, except for the Arctic Archipelago.
Release date: 2024-09-19
10. Water yield: Data product specifications
Geographic files and documentation: 16-510-X2024002
Description: This product contains specifications intended for users of the water yield geospatial files. This document provides important technical information for users and links to methodology.
Release date: 2024-09-19

Date modified:: 2026-08-03

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