Analysis

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All (323)

All (323) (0 to 10 of 323 results)

1. Bayesian differential privacy for small counties and individual commodities
Articles and reports: 12-001-X202600100011
Description: We construct a hybrid Bayesian method, which includes a differentially private mechanism, to mask Census county totals for a U.S. state on acreage of a commodity. We use surrogates for data collected at the farm level from a past U.S. Census of Agriculture to illustrate our procedure. We use two Bayesian small area models (parametric and mixture) to accommodate the smaller counties with fewer farms and some counties with large acres. In these models, the Laplace distribution provides a differentially private mechanism. In pre-processing, we also incorporate the Census weights to form the observed total acreage, a scaling factor to the Laplace mechanism for each county, a square-root transformation of the observed total acreage to avoid negative masked estimates especially for small counties, and the p-percent rule and the 3+ rule to partition the counties into suppressed counties, non-sensitive counties and sensitive counties. Because of difficulties in specifying and tuning the privacy budget (an unknown parameter), to balance security and utility, we specify a prior for the privacy budget, where the values are not specified, and the Gibbs sampler is used to fit the hierarchical Bayesian models. In post-processing, we use Bayesian predictive inference to obtain masked county acreages, and this includes a benchmarking so that the masked state total matches the observed state total. As a measure of reliability of the Bayesian procedure, we use the posterior coefficients of variation for the masked posterior means of the counties. As a measure of utility, we use the absolute relative errors for the individual counties, together with other global measures. For the sensitive counties, there are some differences between the two small area models but both are much better than an individual area model; the mixture model being the best compromise for security and utility.
Release date: 2026-06-29
2. 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
3. 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
4. Modelling the Impact of Immigration on Student Numbers
Articles and reports: 11-633-X2025005
Description: This study presents an approach to model changes in the numbers of elementary, secondary and postsecondary students who are immigrants (including both permanent residents and non permanent residents) in response to changes in overall immigration levels.
Release date: 2025-12-22
5. Fighting Misinformation
Stats in brief: 89-20-00062025001
Description: 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
6. Mapping location and co-location of industries at the neighborhood level: A spatial kernel density approach
Articles and reports: 18-001-X2025001
Description: This paper brings the analysis of business cluster to a more granular geographic scale by developing a methodology for identifying business clusters at the neighborhood level. The proposed method identifies clusters of businesses at the DB level, which is one of the most granular spatial units of analysis defined by Statistics Canada. The method is developed with an application to four census metropolitan areas (CMAs) of different sizes and for different industry cluster specifications, including simple 2-digit North American Industry Classification System (NAICS) groups as well as industry clusters resulting from groupings of NAICS codes, as defined by Delgado et al. (2014).
Release date: 2025-10-10
7. Recruitment and Collection of Web Panels at Statistics Canada Archived
Articles and reports: 11-522-X202500100008
Description: In 2020, Statistics Canada started to use probabilistic web panels as an alternate method of collecting official statistics. In a web panel, respondents to another survey are asked for contact information to participate in future short surveys. This paper will highlight Statistics Canada's experience with panels after 4 years, including what has been learned about the recruitment of panel participants and how to subsequently collect data using panel surveys. The ways in which recruitment questions are presented can result in very different rates of participation. Moreover, the wealth of auxiliary information available on the recruitment survey can be used to actively manage panel collection operations, by predicting the probability of response and using this information to target follow-up efforts.
Release date: 2025-09-08
8. A Bias Evaluation for Probabilistic Web Panels at Statistics Canada Archived
Articles and reports: 11-522-X202500100009
Description: Three series of web panels were implemented at Statistics Canada from 2020 to 2024. Participants for these web panel series were recruited from respondents of large probabilistic social surveys (recruitment surveys), and subsequently were invited to complete a series of short online surveys. Estimates of recruitment survey variables were calculated using both recruitment survey weights and web panel weights, and these were compared; differences signal the possibility of residual bias that was not corrected by the web panel weighting process. This investigation found more significant differences than would be expected if the web panel estimator fully corrected for the bias resulting from the web panel response process. Questions related to certain topics such as politics and voting, sense of belonging, and media consumption were found to have the most significant differences between web panel estimates and recruitment survey estimates.
Release date: 2025-09-08
9. Exploration of Approaches to Small Area Estimation with Measurement Errors and their Application to Indonesian Household Surveys Archived
Articles and reports: 11-522-X202500100028
Description: The United Nations Sustainable Development Goals require detailed, disaggregated data, typically obtained through household surveys. However, surveys alone cannot meet these needs for granular statistics. To address this, National Statistical Institutes adopt small area methods, but these face challenges as auxiliary variables, often derived from surveys, introduce measurement errors into the models. The aim is the application of measurement error correction in classic Fay-Herriot area-level model. The results demonstrate the robustness of the standard approach and ignoring measurement error but show there are specific scenarios where correction for measurement errors is beneficial. The approach is applied to a case study utilizing Indonesian household survey data.
Release date: 2025-09-08
10. Comparison of Recent Techniques of Combining Probability and Non-probability Samples Archived
Articles and reports: 11-522-X202500100031
Description: Several recent quasi-randomization methods for inferences from non-probability samples will be compared. The considered techniques are developed under the assumption that the sample selection is governed by an underlying latent random mechanism and that it can be uncovered by combining non-probability survey data with a "reference" probability-based sample, obtained from the same target population. Challenges prompting the development of alternative procedures include (i) non-probability sample participation indicators are available only on the observed sample units and (ii) it is not generally known which units from the underlying population belong to both the non-probability and reference samples. The ways different procedures address these challenges are considered, theoretical properties of the methods are discussed and their comparison is made using simulations.
Release date: 2025-09-08

Stats in brief (8)

Stats in brief (8) ((8 results))

1. Fighting Misinformation
Stats in brief: 89-20-00062025001
Description: 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
2. Unlock the Power of Data Linkage
Stats in brief: 89-20-00062024003
Description: 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
3. The Importance of Disaggregated Data: An Introduction (part 1) Archived
Stats in brief: 89-20-00062024001
Description: This short video explains how it can be very effective for all levels of governments and organizations that serve communities to use disaggregated data to make evidence-informed public policy decisions. By using disaggregated data, policymakers are able to design more appropriate and effective policies that meet the needs of each diverse and unique Canadian.
Release date: 2024-07-16
4. The Importance of Disaggregated Data: An Introduction (part 2) Archived
Stats in brief: 89-20-00062024002
Description: This short video explains how the use of disaggregated data can help policymakers to develop more targeted and effective policies by identifying the unique needs and challenges faced by different demographic groups.
Release date: 2024-07-16
5. Analysis 101: How to read a table Archived
Stats in brief: 89-20-00062023002
Description: This video is for learners beginning their own journey to increase their current level of data literacy. No prerequisite learning is required to fully understand this video. By the end of this video, you will have a better understanding of why data tables are important, how data tables are structured and how to interpret data quality indicators within a table.
Release date: 2023-10-24
6. Data Quality as Fitness for Use Archived
Stats in brief: 89-20-00062023001
Description: This course is intended for Government of Canada employees who would like to learn about evaluating the quality of data for a particular use. Whether you are a new employee interested in learning the basics, or an experienced subject matter expert looking to refresh your skills, this course is here to help.
Release date: 2023-07-17
7. The Journey of Statistics Canada Data: Why it Matters to You Archived
Stats in brief: 11-627-M2022016
Description:
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
8. The Canadian Research and Development Satellite Account, 1997 to 2004 Archived
Stats in brief: 13-604-M2007056
Description:
This paper highlights the newly constructed Research and Development Satellite Account (RDSA) developed by Statistics Canada. The RDSA provides an analysis for the capitalization of research and development (R&D) as proposed by international guidelines for the System of National Accounts. The account calculates several methods to measure the impact on Gross Domestic Product of R&D expenditures. This paper presents the results of the RDSA for the years 1997 to 2004.
Release date: 2008-05-30

Articles and reports (314)

Articles and reports (314) (0 to 10 of 314 results)

1. Bayesian differential privacy for small counties and individual commodities
Articles and reports: 12-001-X202600100011
Description: We construct a hybrid Bayesian method, which includes a differentially private mechanism, to mask Census county totals for a U.S. state on acreage of a commodity. We use surrogates for data collected at the farm level from a past U.S. Census of Agriculture to illustrate our procedure. We use two Bayesian small area models (parametric and mixture) to accommodate the smaller counties with fewer farms and some counties with large acres. In these models, the Laplace distribution provides a differentially private mechanism. In pre-processing, we also incorporate the Census weights to form the observed total acreage, a scaling factor to the Laplace mechanism for each county, a square-root transformation of the observed total acreage to avoid negative masked estimates especially for small counties, and the p-percent rule and the 3+ rule to partition the counties into suppressed counties, non-sensitive counties and sensitive counties. Because of difficulties in specifying and tuning the privacy budget (an unknown parameter), to balance security and utility, we specify a prior for the privacy budget, where the values are not specified, and the Gibbs sampler is used to fit the hierarchical Bayesian models. In post-processing, we use Bayesian predictive inference to obtain masked county acreages, and this includes a benchmarking so that the masked state total matches the observed state total. As a measure of reliability of the Bayesian procedure, we use the posterior coefficients of variation for the masked posterior means of the counties. As a measure of utility, we use the absolute relative errors for the individual counties, together with other global measures. For the sensitive counties, there are some differences between the two small area models but both are much better than an individual area model; the mixture model being the best compromise for security and utility.
Release date: 2026-06-29
2. 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
3. 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
4. Modelling the Impact of Immigration on Student Numbers
Articles and reports: 11-633-X2025005
Description: This study presents an approach to model changes in the numbers of elementary, secondary and postsecondary students who are immigrants (including both permanent residents and non permanent residents) in response to changes in overall immigration levels.
Release date: 2025-12-22
5. Mapping location and co-location of industries at the neighborhood level: A spatial kernel density approach
Articles and reports: 18-001-X2025001
Description: This paper brings the analysis of business cluster to a more granular geographic scale by developing a methodology for identifying business clusters at the neighborhood level. The proposed method identifies clusters of businesses at the DB level, which is one of the most granular spatial units of analysis defined by Statistics Canada. The method is developed with an application to four census metropolitan areas (CMAs) of different sizes and for different industry cluster specifications, including simple 2-digit North American Industry Classification System (NAICS) groups as well as industry clusters resulting from groupings of NAICS codes, as defined by Delgado et al. (2014).
Release date: 2025-10-10
6. Recruitment and Collection of Web Panels at Statistics Canada Archived
Articles and reports: 11-522-X202500100008
Description: In 2020, Statistics Canada started to use probabilistic web panels as an alternate method of collecting official statistics. In a web panel, respondents to another survey are asked for contact information to participate in future short surveys. This paper will highlight Statistics Canada's experience with panels after 4 years, including what has been learned about the recruitment of panel participants and how to subsequently collect data using panel surveys. The ways in which recruitment questions are presented can result in very different rates of participation. Moreover, the wealth of auxiliary information available on the recruitment survey can be used to actively manage panel collection operations, by predicting the probability of response and using this information to target follow-up efforts.
Release date: 2025-09-08
7. A Bias Evaluation for Probabilistic Web Panels at Statistics Canada Archived
Articles and reports: 11-522-X202500100009
Description: Three series of web panels were implemented at Statistics Canada from 2020 to 2024. Participants for these web panel series were recruited from respondents of large probabilistic social surveys (recruitment surveys), and subsequently were invited to complete a series of short online surveys. Estimates of recruitment survey variables were calculated using both recruitment survey weights and web panel weights, and these were compared; differences signal the possibility of residual bias that was not corrected by the web panel weighting process. This investigation found more significant differences than would be expected if the web panel estimator fully corrected for the bias resulting from the web panel response process. Questions related to certain topics such as politics and voting, sense of belonging, and media consumption were found to have the most significant differences between web panel estimates and recruitment survey estimates.
Release date: 2025-09-08
8. Exploration of Approaches to Small Area Estimation with Measurement Errors and their Application to Indonesian Household Surveys Archived
Articles and reports: 11-522-X202500100028
Description: The United Nations Sustainable Development Goals require detailed, disaggregated data, typically obtained through household surveys. However, surveys alone cannot meet these needs for granular statistics. To address this, National Statistical Institutes adopt small area methods, but these face challenges as auxiliary variables, often derived from surveys, introduce measurement errors into the models. The aim is the application of measurement error correction in classic Fay-Herriot area-level model. The results demonstrate the robustness of the standard approach and ignoring measurement error but show there are specific scenarios where correction for measurement errors is beneficial. The approach is applied to a case study utilizing Indonesian household survey data.
Release date: 2025-09-08
9. Comparison of Recent Techniques of Combining Probability and Non-probability Samples Archived
Articles and reports: 11-522-X202500100031
Description: Several recent quasi-randomization methods for inferences from non-probability samples will be compared. The considered techniques are developed under the assumption that the sample selection is governed by an underlying latent random mechanism and that it can be uncovered by combining non-probability survey data with a "reference" probability-based sample, obtained from the same target population. Challenges prompting the development of alternative procedures include (i) non-probability sample participation indicators are available only on the observed sample units and (ii) it is not generally known which units from the underlying population belong to both the non-probability and reference samples. The ways different procedures address these challenges are considered, theoretical properties of the methods are discussed and their comparison is made using simulations.
Release date: 2025-09-08
10. Factors Affecting Response Propensity, with an Interest in Units Sampled Multiple Times Archived
Articles and reports: 11-522-X202500100036
Description: As the need for data has grown over the past number of years, the effect and burden of repeatedly sampling the same units for multiple surveys have become an increasing concern. Response burden is generally assumed to contribute to decreasing response rates; however, there are few empirical studies looking into this question. As part of this study, data on response to social surveys conducted at Statistics Canada between 2021 and 2023 was aggregated in order to investigate factors contributing to the observed response patterns, including the effect of having been selected multiple times. It was found that, relative to some other demographic and geographic characteristics, a unit being sampled multiple times is not an influential factor in predicting response propensity.
Release date: 2025-09-08

Journals and periodicals (1)

Journals and periodicals (1) ((1 result))

1. Technical Report on the Analysis of Small Groups in the 1999 GSS Archived
Journals and periodicals: 85F0036X
Geography: Canada
Description:
This study documents the methodological and technical challenges that are involved in performing analysis on small groups using a sample survey, oversampling, response rate, non-response rate due to language, release feasibility and sampling variability. It is based on the 1999 General Social Survey (GSS) on victimization.
Release date: 2002-05-14

Date modified:: 2026-08-14

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