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All (192) (0 to 10 of 192 results)

1. Evaluating sampling methods for ethnic minorities Archived
Articles and reports: 11-522-X202200100014
Description: Ethnic minorities are often underrepresented in survey research, due to the challenges many researchers face in including these populations. While some studies discuss several methods in comparison, few have directly compared these methods empirically, leaving researchers seeking to include ethnic minorities in their studies unsure of their best options. In this article, I briefly review the methodological and ethical reasons for increasing ethnic minority representation in social science research, as well as challenges of doing so. I then present findings from ten studies which empirically compare methods of sampling and/or recruiting ethnic minority individuals. Finally, I discuss some implications for future research.
Release date: 2024-03-25
2. Bayesian model assisted design-based estimators of the size, total and mean of a hard-to-reach population from a link-tracing sample with initial cluster sample Archived
Articles and reports: 11-522-X202200100015
Description: We present design-based Horvitz-Thompson and multiplicity estimators of the population size, as well as of the total and mean of a response variable associated with the elements of a hidden population to be used with the link-tracing sampling variant proposed by Félix-Medina and Thompson (2004). Since the computation of the estimators requires to know the inclusion probabilities of the sampled people, but they are unknown, we propose a Bayesian model which allows us to estimate them, and consequently to compute the estimators of the population parameters. The results of a small numeric study indicate that the performance of the proposed estimators is acceptable.
Release date: 2024-03-25
3. From theory to practice: Lessons learned from implementing the Network Sampling with Memory method Archived
Articles and reports: 11-522-X202200100016
Description: To overcome the traditional drawbacks of chain sampling methods, the sampling method called “network sampling with memory” was developed. Its unique feature is to recreate, gradually in the field, a frame for the target population composed of individuals identified by respondents and to randomly draw future respondents from this frame, thereby minimizing selection bias. Tested for the first time in France between September 2020 and June 2021, for a survey among Chinese immigrants in Île-de-France (ChIPRe), this presentation describes the difficulties encountered during collection—sometimes contextual, due to the pandemic, but mostly inherent to the method.
Release date: 2024-03-25
4. Comments by Marc Christine on “Jean-Claude Deville’s contributions to survey theory and official statistics”
Articles and reports: 12-001-X202300200015
Description: This article discusses and provides comments on the Ardilly, Haziza, Lavallée and Tillé’s summary presentation of Jean-Claude Deville’s work on survey theory. It sheds light on the context, applications and uses of his findings, and shows how these have become engrained in the role of statisticians, in which Jean-Claude was a trailblazer. It also discusses other aspects of his career and his creative inventions.
Release date: 2024-01-03
5. 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
6. Agriculture–Population Linkage: Data quality report
Surveys and statistical programs – Documentation: 32-26-0006
Description: This report provides data quality information pertaining to the Agriculture–Population Linkage, such as sources of error, matching process, response rates, imputation rates, sampling, weighting, disclosure control methods and data quality indicators.
Release date: 2023-08-25
7. Sampling with adaptive drawing probabilities
Articles and reports: 12-001-X202300100009
Description: In this paper, with and without-replacement versions of adaptive proportional to size sampling are presented. Unbiased estimators are developed for these methods and their properties are studied. In the two versions, the drawing probabilities are adapted during the sampling process based on the observations already selected. To this end, in the version with-replacement, after each draw and observation of the variable of interest, the vector of the auxiliary variable will be updated using the observed values of the variable of interest to approximate the exact selection probability proportional to size. For the without-replacement version, first, using an initial sample, we model the relationship between the variable of interest and the auxiliary variable. Then, utilizing this relationship, we estimate the unknown (unobserved) population units. Finally, on these estimated population units, we select a new sample proportional to size without-replacement. These approaches can significantly improve the efficiency of designs not only in the case of a positive linear relationship, but also in the case of a non-linear or negative linear relationship between the variables. We investigate the efficiencies of the designs through simulations and real case studies on medicinal flowers, social and economic data.
Release date: 2023-06-30
8. Bayes, buttressed by design-based ideas, is the best overarching paradigm for sample survey inference
Articles and reports: 12-001-X202200200001
Description:
Conceptual arguments and examples are presented suggesting that the Bayesian approach to survey inference can address the many and varied challenges of survey analysis. Bayesian models that incorporate features of the complex design can yield inferences that are relevant for the specific data set obtained, but also have good repeated-sampling properties. Examples focus on the role of auxiliary variables and sampling weights, and methods for handling nonresponse. The article offers ten top reasons for favoring the Bayesian approach to survey inference.

Release date: 2022-12-15
9. Canadian Income Survey: Population rebasing, 2012 to 2019
Articles and reports: 75F0002M2022001
Description:
Periodically, income statistics are updated to reflect the most recent population estimates derived after the census. Accordingly, with the release of the 2020 data from the Canadian Income Survey (CIS), Statistics Canada has revised estimates for 2012 to 2019 using totals from population estimates based on the 2016 Census. This paper presents a comparison between revised and unrevised estimates for key income series, and describes other modifications made to CIS variables.

Release date: 2022-03-23
10. Bayesian hierarchical weighting adjustment and survey inference
Articles and reports: 12-001-X202000200003
Description:
We combine weighting and Bayesian prediction in a unified approach to survey inference. The general principles of Bayesian analysis imply that models for survey outcomes should be conditional on all variables that affect the probability of inclusion. We incorporate all the variables that are used in the weighting adjustment under the framework of multilevel regression and poststratification, as a byproduct generating model-based weights after smoothing. We improve small area estimation by dealing with different complex issues caused by real-life applications to obtain robust inference at finer levels for subdomains of interest. We investigate deep interactions and introduce structured prior distributions for smoothing and stability of estimates. The computation is done via Stan and is implemented in the open-source R package rstanarm and available for public use. We evaluate the design-based properties of the Bayesian procedure. Simulation studies illustrate how the model-based prediction and weighting inference can outperform classical weighting. We apply the method to the New York Longitudinal Study of Wellbeing. The new approach generates smoothed weights and increases efficiency for robust finite population inference, especially for subsets of the population.
Release date: 2020-12-15

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Analysis (175)

Analysis (175) (0 to 10 of 175 results)

1. Evaluating sampling methods for ethnic minorities Archived
Articles and reports: 11-522-X202200100014
Description: Ethnic minorities are often underrepresented in survey research, due to the challenges many researchers face in including these populations. While some studies discuss several methods in comparison, few have directly compared these methods empirically, leaving researchers seeking to include ethnic minorities in their studies unsure of their best options. In this article, I briefly review the methodological and ethical reasons for increasing ethnic minority representation in social science research, as well as challenges of doing so. I then present findings from ten studies which empirically compare methods of sampling and/or recruiting ethnic minority individuals. Finally, I discuss some implications for future research.
Release date: 2024-03-25
2. Bayesian model assisted design-based estimators of the size, total and mean of a hard-to-reach population from a link-tracing sample with initial cluster sample Archived
Articles and reports: 11-522-X202200100015
Description: We present design-based Horvitz-Thompson and multiplicity estimators of the population size, as well as of the total and mean of a response variable associated with the elements of a hidden population to be used with the link-tracing sampling variant proposed by Félix-Medina and Thompson (2004). Since the computation of the estimators requires to know the inclusion probabilities of the sampled people, but they are unknown, we propose a Bayesian model which allows us to estimate them, and consequently to compute the estimators of the population parameters. The results of a small numeric study indicate that the performance of the proposed estimators is acceptable.
Release date: 2024-03-25
3. From theory to practice: Lessons learned from implementing the Network Sampling with Memory method Archived
Articles and reports: 11-522-X202200100016
Description: To overcome the traditional drawbacks of chain sampling methods, the sampling method called “network sampling with memory” was developed. Its unique feature is to recreate, gradually in the field, a frame for the target population composed of individuals identified by respondents and to randomly draw future respondents from this frame, thereby minimizing selection bias. Tested for the first time in France between September 2020 and June 2021, for a survey among Chinese immigrants in Île-de-France (ChIPRe), this presentation describes the difficulties encountered during collection—sometimes contextual, due to the pandemic, but mostly inherent to the method.
Release date: 2024-03-25
4. Comments by Marc Christine on “Jean-Claude Deville’s contributions to survey theory and official statistics”
Articles and reports: 12-001-X202300200015
Description: This article discusses and provides comments on the Ardilly, Haziza, Lavallée and Tillé’s summary presentation of Jean-Claude Deville’s work on survey theory. It sheds light on the context, applications and uses of his findings, and shows how these have become engrained in the role of statisticians, in which Jean-Claude was a trailblazer. It also discusses other aspects of his career and his creative inventions.
Release date: 2024-01-03
5. Sampling with adaptive drawing probabilities
Articles and reports: 12-001-X202300100009
Description: In this paper, with and without-replacement versions of adaptive proportional to size sampling are presented. Unbiased estimators are developed for these methods and their properties are studied. In the two versions, the drawing probabilities are adapted during the sampling process based on the observations already selected. To this end, in the version with-replacement, after each draw and observation of the variable of interest, the vector of the auxiliary variable will be updated using the observed values of the variable of interest to approximate the exact selection probability proportional to size. For the without-replacement version, first, using an initial sample, we model the relationship between the variable of interest and the auxiliary variable. Then, utilizing this relationship, we estimate the unknown (unobserved) population units. Finally, on these estimated population units, we select a new sample proportional to size without-replacement. These approaches can significantly improve the efficiency of designs not only in the case of a positive linear relationship, but also in the case of a non-linear or negative linear relationship between the variables. We investigate the efficiencies of the designs through simulations and real case studies on medicinal flowers, social and economic data.
Release date: 2023-06-30
6. Bayes, buttressed by design-based ideas, is the best overarching paradigm for sample survey inference
Articles and reports: 12-001-X202200200001
Description:
Conceptual arguments and examples are presented suggesting that the Bayesian approach to survey inference can address the many and varied challenges of survey analysis. Bayesian models that incorporate features of the complex design can yield inferences that are relevant for the specific data set obtained, but also have good repeated-sampling properties. Examples focus on the role of auxiliary variables and sampling weights, and methods for handling nonresponse. The article offers ten top reasons for favoring the Bayesian approach to survey inference.

Release date: 2022-12-15
7. Canadian Income Survey: Population rebasing, 2012 to 2019
Articles and reports: 75F0002M2022001
Description:
Periodically, income statistics are updated to reflect the most recent population estimates derived after the census. Accordingly, with the release of the 2020 data from the Canadian Income Survey (CIS), Statistics Canada has revised estimates for 2012 to 2019 using totals from population estimates based on the 2016 Census. This paper presents a comparison between revised and unrevised estimates for key income series, and describes other modifications made to CIS variables.

Release date: 2022-03-23
8. Bayesian hierarchical weighting adjustment and survey inference
Articles and reports: 12-001-X202000200003
Description:
We combine weighting and Bayesian prediction in a unified approach to survey inference. The general principles of Bayesian analysis imply that models for survey outcomes should be conditional on all variables that affect the probability of inclusion. We incorporate all the variables that are used in the weighting adjustment under the framework of multilevel regression and poststratification, as a byproduct generating model-based weights after smoothing. We improve small area estimation by dealing with different complex issues caused by real-life applications to obtain robust inference at finer levels for subdomains of interest. We investigate deep interactions and introduce structured prior distributions for smoothing and stability of estimates. The computation is done via Stan and is implemented in the open-source R package rstanarm and available for public use. We evaluate the design-based properties of the Bayesian procedure. Simulation studies illustrate how the model-based prediction and weighting inference can outperform classical weighting. We apply the method to the New York Longitudinal Study of Wellbeing. The new approach generates smoothed weights and increases efficiency for robust finite population inference, especially for subsets of the population.
Release date: 2020-12-15
9. Variance estimation under monotone non-response for a panel survey Archived
Articles and reports: 12-001-X201800254952
Description:
Panel surveys are frequently used to measure the evolution of parameters over time. Panel samples may suffer from different types of unit non-response, which is currently handled by estimating the response probabilities and by reweighting respondents. In this work, we consider estimation and variance estimation under unit non-response for panel surveys. Extending the work by Kim and Kim (2007) for several times, we consider a propensity score adjusted estimator accounting for initial non-response and attrition, and propose a suitable variance estimator. It is then extended to cover most estimators encountered in surveys, including calibrated estimators, complex parameters and longitudinal estimators. The properties of the proposed variance estimator and of a simplified variance estimator are estimated through a simulation study. An illustration of the proposed methods on data from the ELFE survey is also presented.
Release date: 2018-12-20
10. Sample-based estimation of mean electricity consumption curves for small domains Archived
Articles and reports: 12-001-X201800254955
Description:
Many studies conducted by various electric utilities around the world are based on the analysis of mean electricity consumption curves for various subpopulations, particularly geographic in nature. Those mean curves are estimated from samples of thousands of curves measured at very short intervals over long periods. Estimation for small subpopulations, also called small domains, is a very timely topic in sampling theory.
In this article, we will examine this problem based on functional data and we will try to estimate the mean curves for small domains. For this, we propose four methods: functional linear regression; modelling the scores of a principal component analysis by unit-level linear mixed models; and two non-parametric estimators, with one based on regression trees and the other on random forests, adapted to the curves. All these methods have been tested and compared using real electricity consumption data for households in France.
Release date: 2018-12-20

Reference (17)

Reference (17) (0 to 10 of 17 results)

1. 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
2. Agriculture–Population Linkage: Data quality report
Surveys and statistical programs – Documentation: 32-26-0006
Description: This report provides data quality information pertaining to the Agriculture–Population Linkage, such as sources of error, matching process, response rates, imputation rates, sampling, weighting, disclosure control methods and data quality indicators.
Release date: 2023-08-25
3. Statistics Canada Quality Guidelines
Surveys and statistical programs – Documentation: 12-539-X
Description:
This document brings together guidelines and checklists on many issues that need to be considered in the pursuit of quality objectives in the execution of statistical activities. Its focus is on how to assure quality through effective and appropriate design or redesign of a statistical project or program from inception through to data evaluation, dissemination and documentation. These guidelines draw on the collective knowledge and experience of many Statistics Canada employees. It is expected that Quality Guidelines will be useful to staff engaged in the planning and design of surveys and other statistical projects, as well as to those who evaluate and analyze the outputs of these projects.
Release date: 2019-12-04
4. Methodology of the Canadian Labour Force Survey
Surveys and statistical programs – Documentation: 71-526-X
Description:
The Canadian Labour Force Survey (LFS) is the official source of monthly estimates of total employment and unemployment. Following the 2011 census, the LFS underwent a sample redesign to account for the evolution of the population and labour market characteristics, to adjust to changes in the information needs and to update the geographical information used to carry out the survey. The redesign program following the 2011 census culminated with the introduction of a new sample at the beginning of 2015. This report is a reference on the methodological aspects of the LFS, covering stratification, sampling, collection, processing, weighting, estimation, variance estimation and data quality.
Release date: 2017-12-21
5. Sampling and Weighting Technical Report, National Household Survey, 2011 Archived
Surveys and statistical programs – Documentation: 99-002-X2011001
Description:
This report describes sampling and weighting procedures used in the 2011 National Household Survey. It provides operational and theoretical justifications for them, and presents the results of the evaluation studies of these procedures.
Release date: 2015-01-28
6. Survey of Household Spending 2004: Data Quality Indicators Archived
Surveys and statistical programs – Documentation: 62F0026M2010001
Description:
This report describes the quality indicators produced for the 2004 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.
Release date: 2010-04-26
7. Survey of Household Spending 2005: Data Quality Indicators Archived
Surveys and statistical programs – Documentation: 62F0026M2010002
Description:
This report describes the quality indicators produced for the 2005 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.
Release date: 2010-04-26
8. Survey of Household Spending 2006: Data Quality Indicators Archived
Surveys and statistical programs – Documentation: 62F0026M2010003
Description:
This report describes the quality indicators produced for the 2006 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.
Release date: 2010-04-26
9. 2006 Census Technical Report: Sampling and Weighting
Surveys and statistical programs – Documentation: 92-568-X
Description:
This report describes sampling and weighting procedures used in the 2006 Census. It reviews the history of these procedures in Canadian censuses, provides operational and theoretical justifications for them, and presents the results of the evaluation studies of these procedures.
Release date: 2009-08-11
10. Survey of Labour and Income Dynamics: 2003 Historical Revision Archived
Surveys and statistical programs – Documentation: 75F0002M2005009
Description:
The release of the 2003 data from the Survey of Labour and Income Dynamics (SLID) was accompanied by a historical revision which accomplished three things. First, the survey weights were updated to take into account new population projections based on the 2001 Census of Population, instead of the 1996 Census. Second, a new procedure in the weight adjustments was introduced to take into account an external source of information on the overall distribution of income in the population, namely the T4 file of employer remittances to Canada Revenue Agency. Third, the low income estimates were revised due to new low income cut-offs (LICOs). This paper describes the second of these improvements' the new weighting procedure to reflect the distribution of income in the population with greater accuracy. Part 1 explains in non-technical terms how this new procedure came about and how it works. Part 2 provides some examples of the impacts on the results for previous years.
Release date: 2005-07-22

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Date modified:: 2024-09-21