Analysis
Filter results by
Search HelpKeyword(s)
Subject
- Agriculture and food (2)
- Business performance and ownership (5)
- Construction (1)
- Economic accounts (2)
- Education, training and learning (1)
- Environment (1)
- Families, households and marital status (1)
- Health (9)
- Housing (2)
- Income, pensions, spending and wealth (4)
- Labour (16)
- Population and demography (1)
- Prices and price indexes (3)
- Retail and wholesale (1)
- Science and technology (1)
- Society and community (2)
- Statistical methods (898)
- Travel and tourism (2)
Year of publication
- 2008 (36)
- 2006 (34)
- 2024 (33)
- 2022 (30)
- 2014 (28)
- 2019 (28)
- 2000 (25)
- 1990 (24)
- 1988 (23)
- 2003 (23)
- 2005 (23)
- 1999 (22)
- 2002 (22)
- 2009 (22)
- 2015 (22)
- 1980 (21)
- 1995 (21)
- 2001 (21)
- 2004 (21)
- 2010 (21)
- 2012 (21)
- 1985 (20)
- 1994 (20)
- 1989 (19)
- 1997 (19)
- 1998 (19)
- 1986 (18)
- 1987 (18)
- 1991 (18)
- 1993 (18)
- 2011 (18)
- 1992 (17)
- 2016 (17)
- 2018 (17)
- 2017 (15)
- 1978 (13)
- 1984 (13)
- 1979 (11)
- 1983 (11)
- 2007 (11)
- 2020 (11)
- 2023 (11)
- 1981 (10)
- 1996 (10)
- 2013 (9)
- 2021 (9)
- 1982 (5)
- 1975 (2)
- 1977 (1)
Author(s)
Survey or statistical program
- Canadian Composite Leading Indicator (1)
- National Balance Sheet Accounts (1)
- International Travel Survey: Electronic questionnaires and Air Exit Survey (1)
- Quarterly Demographic Estimates (1)
- Estimates of Total Population for Canada, the Provinces and the Territories (1)
- Census of Population (1)
- Frontier Counts (1)
Results
All (901)
All (901) (0 to 10 of 901 results)
- Journals and periodicals: 12-001-XGeography: CanadaDescription: The journal publishes articles dealing with various aspects of statistical development relevant to a statistical agency, such as design issues in the context of practical constraints, use of different data sources and collection techniques, total survey error, survey evaluation, research in survey methodology, time series analysis, seasonal adjustment, demographic studies, data integration, estimation and data analysis methods, and general survey systems development. The emphasis is placed on the development and evaluation of specific methodologies as applied to data collection or the data themselves.Release date: 2024-06-25
- Articles and reports: 12-001-X202400100001Description: Inspired by the two excellent discussions of our paper, we offer some new insights and developments into the problem of estimating participation probabilities for non-probability samples. First, we propose an improvement of the method of Chen, Li and Wu (2020), based on best linear unbiased estimation theory, that more efficiently leverages the available probability and non-probability sample data. We also develop a sample likelihood approach, similar in spirit to the method of Elliott (2009), that properly accounts for the overlap between both samples when it can be identified in at least one of the samples. We use best linear unbiased prediction theory to handle the scenario where the overlap is unknown. Interestingly, our two proposed approaches coincide in the case of unknown overlap. Then, we show that many existing methods can be obtained as a special case of a general unbiased estimating function. Finally, we conclude with some comments on nonparametric estimation of participation probabilities.Release date: 2024-06-25
- Articles and reports: 12-001-X202400100002Description: We provide comparisons among three parametric methods for the estimation of participation probabilities and some brief comments on homogeneous groups and post-stratification.Release date: 2024-06-25
- Articles and reports: 12-001-X202400100003Description: Beaumont, Bosa, Brennan, Charlebois and Chu (2024) propose innovative model selection approaches for estimation of participation probabilities for non-probability sample units. We focus our discussion on the choice of a likelihood and parameterization of the model, which are key for the effectiveness of the techniques developed in the paper. We consider alternative likelihood and pseudo-likelihood based methods for estimation of participation probabilities and present simulations implementing and comparing the AIC based variable selection. We demonstrate that, under important practical scenarios, the approach based on a likelihood formulated over the observed pooled non-probability and probability samples performed better than the pseudo-likelihood based alternatives. The contrast in sensitivity of the AIC criteria is especially large for small probability sample sizes and low overlap in covariates domains.Release date: 2024-06-25
- Articles and reports: 12-001-X202400100004Description: Non-probability samples are being increasingly explored in National Statistical Offices as an alternative to probability samples. However, it is well known that the use of a non-probability sample alone may produce estimates with significant bias due to the unknown nature of the underlying selection mechanism. Bias reduction can be achieved by integrating data from the non-probability sample with data from a probability sample provided that both samples contain auxiliary variables in common. We focus on inverse probability weighting methods, which involve modelling the probability of participation in the non-probability sample. First, we consider the logistic model along with pseudo maximum likelihood estimation. We propose a variable selection procedure based on a modified Akaike Information Criterion (AIC) that properly accounts for the data structure and the probability sampling design. We also propose a simple rank-based method of forming homogeneous post-strata. Then, we extend the Classification and Regression Trees (CART) algorithm to this data integration scenario, while again properly accounting for the probability sampling design. A bootstrap variance estimator is proposed that reflects two sources of variability: the probability sampling design and the participation model. Our methods are illustrated using Statistics Canada’s crowdsourcing and survey data.Release date: 2024-06-25
- Articles and reports: 12-001-X202400100005Description: In this rejoinder, I address the comments from the discussants, Dr. Takumi Saegusa, Dr. Jae-Kwang Kim and Ms. Yonghyun Kwon. Dr. Saegusa’s comments about the differences between the conditional exchangeability (CE) assumption for causal inferences versus the CE assumption for finite population inferences using nonprobability samples, and the distinction between design-based versus model-based approaches for finite population inference using nonprobability samples, are elaborated and clarified in the context of my paper. Subsequently, I respond to Dr. Kim and Ms. Kwon’s comprehensive framework for categorizing existing approaches for estimating propensity scores (PS) into conditional and unconditional approaches. I expand their simulation studies to vary the sampling weights, allow for misspecified PS models, and include an additional estimator, i.e., scaled adjusted logistic propensity estimator (Wang, Valliant and Li (2021), denoted by sWBS). In my simulations, it is observed that the sWBS estimator consistently outperforms or is comparable to the other estimators under the misspecified PS model. The sWBS, as well as WBS or ABS described in my paper, do not assume that the overlapped units in both the nonprobability and probability reference samples are negligible, nor do they require the identification of overlap units as needed by the estimators proposed by Dr. Kim and Ms. Kwon.Release date: 2024-06-25
- Articles and reports: 12-001-X202400100006Description: In some of non-probability sample literature, the conditional exchangeability assumption is considered to be necessary for valid statistical inference. This assumption is rooted in causal inference though its potential outcome framework differs greatly from that of non-probability samples. We describe similarities and differences of two frameworks and discuss issues to consider when adopting the conditional exchangeability assumption in non-probability sample setups. We also discuss the role of finite population inference in different approaches of propensity scores and outcome regression modeling to non-probability samples.Release date: 2024-06-25
- Articles and reports: 12-001-X202400100007Description: Pseudo weight construction for data integration can be understood in the two-phase sampling framework. Using the two-phase sampling framework, we discuss two approaches to the estimation of propensity scores and develop a new way to construct the propensity score function for data integration using the conditional maximum likelihood method. Results from a limited simulation study are also presented.Release date: 2024-06-25
- Articles and reports: 12-001-X202400100008Description: Nonprobability samples emerge rapidly to address time-sensitive priority topics in different areas. These data are timely but subject to selection bias. To reduce selection bias, there has been wide literature in survey research investigating the use of propensity-score (PS) adjustment methods to improve the population representativeness of nonprobability samples, using probability-based survey samples as external references. Conditional exchangeability (CE) assumption is one of the key assumptions required by PS-based adjustment methods. In this paper, I first explore the validity of the CE assumption conditional on various balancing score estimates that are used in existing PS-based adjustment methods. An adaptive balancing score is proposed for unbiased estimation of population means. The population mean estimators under the three CE assumptions are evaluated via Monte Carlo simulation studies and illustrated using the NIH SARS-CoV-2 seroprevalence study to estimate the proportion of U.S. adults with COVID-19 antibodies from April 01-August 04, 2020.Release date: 2024-06-25
- Articles and reports: 12-001-X202400100009Description: Our comments respond to discussion from Sen, Brick, and Elliott. We weigh the potential upside and downside of Sen’s suggestion of using machine learning to identify bogus respondents through interactions and improbable combinations of variables. We join Brick in reflecting on bogus respondents’ impact on the state of commercial nonprobability surveys. Finally, we consider Elliott’s discussion of solutions to the challenge raised in our study.Release date: 2024-06-25
- Previous Go to previous page of All results
- 1 (current) Go to page 1 of All results
- 2 Go to page 2 of All results
- 3 Go to page 3 of All results
- 4 Go to page 4 of All results
- 5 Go to page 5 of All results
- 6 Go to page 6 of All results
- 7 Go to page 7 of All results
- ...
- 91 Go to page 91 of All results
- Next Go to next page of All results
Stats in brief (0)
Stats in brief (0) (0 results)
No content available at this time.
Articles and reports (897)
Articles and reports (897) (0 to 10 of 897 results)
- Articles and reports: 12-001-X202400100001Description: Inspired by the two excellent discussions of our paper, we offer some new insights and developments into the problem of estimating participation probabilities for non-probability samples. First, we propose an improvement of the method of Chen, Li and Wu (2020), based on best linear unbiased estimation theory, that more efficiently leverages the available probability and non-probability sample data. We also develop a sample likelihood approach, similar in spirit to the method of Elliott (2009), that properly accounts for the overlap between both samples when it can be identified in at least one of the samples. We use best linear unbiased prediction theory to handle the scenario where the overlap is unknown. Interestingly, our two proposed approaches coincide in the case of unknown overlap. Then, we show that many existing methods can be obtained as a special case of a general unbiased estimating function. Finally, we conclude with some comments on nonparametric estimation of participation probabilities.Release date: 2024-06-25
- Articles and reports: 12-001-X202400100002Description: We provide comparisons among three parametric methods for the estimation of participation probabilities and some brief comments on homogeneous groups and post-stratification.Release date: 2024-06-25
- Articles and reports: 12-001-X202400100003Description: Beaumont, Bosa, Brennan, Charlebois and Chu (2024) propose innovative model selection approaches for estimation of participation probabilities for non-probability sample units. We focus our discussion on the choice of a likelihood and parameterization of the model, which are key for the effectiveness of the techniques developed in the paper. We consider alternative likelihood and pseudo-likelihood based methods for estimation of participation probabilities and present simulations implementing and comparing the AIC based variable selection. We demonstrate that, under important practical scenarios, the approach based on a likelihood formulated over the observed pooled non-probability and probability samples performed better than the pseudo-likelihood based alternatives. The contrast in sensitivity of the AIC criteria is especially large for small probability sample sizes and low overlap in covariates domains.Release date: 2024-06-25
- Articles and reports: 12-001-X202400100004Description: Non-probability samples are being increasingly explored in National Statistical Offices as an alternative to probability samples. However, it is well known that the use of a non-probability sample alone may produce estimates with significant bias due to the unknown nature of the underlying selection mechanism. Bias reduction can be achieved by integrating data from the non-probability sample with data from a probability sample provided that both samples contain auxiliary variables in common. We focus on inverse probability weighting methods, which involve modelling the probability of participation in the non-probability sample. First, we consider the logistic model along with pseudo maximum likelihood estimation. We propose a variable selection procedure based on a modified Akaike Information Criterion (AIC) that properly accounts for the data structure and the probability sampling design. We also propose a simple rank-based method of forming homogeneous post-strata. Then, we extend the Classification and Regression Trees (CART) algorithm to this data integration scenario, while again properly accounting for the probability sampling design. A bootstrap variance estimator is proposed that reflects two sources of variability: the probability sampling design and the participation model. Our methods are illustrated using Statistics Canada’s crowdsourcing and survey data.Release date: 2024-06-25
- Articles and reports: 12-001-X202400100005Description: In this rejoinder, I address the comments from the discussants, Dr. Takumi Saegusa, Dr. Jae-Kwang Kim and Ms. Yonghyun Kwon. Dr. Saegusa’s comments about the differences between the conditional exchangeability (CE) assumption for causal inferences versus the CE assumption for finite population inferences using nonprobability samples, and the distinction between design-based versus model-based approaches for finite population inference using nonprobability samples, are elaborated and clarified in the context of my paper. Subsequently, I respond to Dr. Kim and Ms. Kwon’s comprehensive framework for categorizing existing approaches for estimating propensity scores (PS) into conditional and unconditional approaches. I expand their simulation studies to vary the sampling weights, allow for misspecified PS models, and include an additional estimator, i.e., scaled adjusted logistic propensity estimator (Wang, Valliant and Li (2021), denoted by sWBS). In my simulations, it is observed that the sWBS estimator consistently outperforms or is comparable to the other estimators under the misspecified PS model. The sWBS, as well as WBS or ABS described in my paper, do not assume that the overlapped units in both the nonprobability and probability reference samples are negligible, nor do they require the identification of overlap units as needed by the estimators proposed by Dr. Kim and Ms. Kwon.Release date: 2024-06-25
- Articles and reports: 12-001-X202400100006Description: In some of non-probability sample literature, the conditional exchangeability assumption is considered to be necessary for valid statistical inference. This assumption is rooted in causal inference though its potential outcome framework differs greatly from that of non-probability samples. We describe similarities and differences of two frameworks and discuss issues to consider when adopting the conditional exchangeability assumption in non-probability sample setups. We also discuss the role of finite population inference in different approaches of propensity scores and outcome regression modeling to non-probability samples.Release date: 2024-06-25
- Articles and reports: 12-001-X202400100007Description: Pseudo weight construction for data integration can be understood in the two-phase sampling framework. Using the two-phase sampling framework, we discuss two approaches to the estimation of propensity scores and develop a new way to construct the propensity score function for data integration using the conditional maximum likelihood method. Results from a limited simulation study are also presented.Release date: 2024-06-25
- Articles and reports: 12-001-X202400100008Description: Nonprobability samples emerge rapidly to address time-sensitive priority topics in different areas. These data are timely but subject to selection bias. To reduce selection bias, there has been wide literature in survey research investigating the use of propensity-score (PS) adjustment methods to improve the population representativeness of nonprobability samples, using probability-based survey samples as external references. Conditional exchangeability (CE) assumption is one of the key assumptions required by PS-based adjustment methods. In this paper, I first explore the validity of the CE assumption conditional on various balancing score estimates that are used in existing PS-based adjustment methods. An adaptive balancing score is proposed for unbiased estimation of population means. The population mean estimators under the three CE assumptions are evaluated via Monte Carlo simulation studies and illustrated using the NIH SARS-CoV-2 seroprevalence study to estimate the proportion of U.S. adults with COVID-19 antibodies from April 01-August 04, 2020.Release date: 2024-06-25
- Articles and reports: 12-001-X202400100009Description: Our comments respond to discussion from Sen, Brick, and Elliott. We weigh the potential upside and downside of Sen’s suggestion of using machine learning to identify bogus respondents through interactions and improbable combinations of variables. We join Brick in reflecting on bogus respondents’ impact on the state of commercial nonprobability surveys. Finally, we consider Elliott’s discussion of solutions to the challenge raised in our study.Release date: 2024-06-25
- Articles and reports: 12-001-X202400100010Description: This discussion summarizes the interesting new findings around measurement errors in opt-in surveys by Kennedy, Mercer and Lau (KML). While KML enlighten readers about “bogus responding” and possible patterns in them, this discussion suggests combining these new-found results with other avenues of research in nonprobability sampling, such as improvement of representativeness.Release date: 2024-06-25
- Previous Go to previous page of Articles and reports results
- 1 (current) Go to page 1 of Articles and reports results
- 2 Go to page 2 of Articles and reports results
- 3 Go to page 3 of Articles and reports results
- 4 Go to page 4 of Articles and reports results
- 5 Go to page 5 of Articles and reports results
- 6 Go to page 6 of Articles and reports results
- 7 Go to page 7 of Articles and reports results
- ...
- 90 Go to page 90 of Articles and reports results
- Next Go to next page of Articles and reports results
Journals and periodicals (4)
Journals and periodicals (4) ((4 results))
- Journals and periodicals: 12-001-XGeography: CanadaDescription: The journal publishes articles dealing with various aspects of statistical development relevant to a statistical agency, such as design issues in the context of practical constraints, use of different data sources and collection techniques, total survey error, survey evaluation, research in survey methodology, time series analysis, seasonal adjustment, demographic studies, data integration, estimation and data analysis methods, and general survey systems development. The emphasis is placed on the development and evaluation of specific methodologies as applied to data collection or the data themselves.Release date: 2024-06-25
- Journals and periodicals: 11-008-XGeography: CanadaDescription:
This publication discusses the social, economic, and demographic changes affecting the lives of Canadians.
Free downloadable PDF and HTML files: Published every six weeks Printed issue: Published every six months (twice per year)
Release date: 2012-07-30 - Journals and periodicals: 11-010-XGeography: CanadaDescription:
This monthly periodical is Statistics Canada's flagship publication for economic statistics. Each issue contains a monthly summary of the economy, major economic events and a feature article. A statistical summary contains a wide range of tables and graphs on the principal economic indicators for Canada, the provinces and the major industrial nations. A historical listing of this same data is contained in the Canadian economic observer: historical supplement (Catalogue no. 11-210-XPB and XIB).
Release date: 2012-06-15 - 4. Travel-log (Touriscope) ArchivedJournals and periodicals: 87-003-XGeography: CanadaDescription:
Travel-log is a quarterly tourism newsletter that examines international travel trends, international travel accounts and the travel price index. It also features the latest tourism indicators and includes feature articles related to tourism.
Release date: 2005-01-26
- Date modified: