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All (142)

All (142) (0 to 10 of 142 results)

1. Inference from sampling with response probabilities estimated via calibration
Articles and reports: 12-001-X202400200013
Description: A solution to control for nonresponse bias consists of multiplying the design weights of respondents by the inverse of estimated response probabilities to compensate for the nonrespondents. Maximum likelihood and calibration are two approaches that can be applied to obtain estimated response probabilities. We consider a common framework in which these approaches can be compared. We develop an asymptotic study of the behavior of the resulting estimator when calibration is applied. A logistic regression model for the response probabilities is postulated. Missing at random and unclustered data are supposed. Three main contributions of this work are: 1) we show that the estimators with the response probabilities estimated via calibration are asymptotically equivalent to unbiased estimators and that a gain in efficiency is obtained when estimating the response probabilities via calibration as compared to the estimator with the true response probabilities, 2) we show that the estimators with the response probabilities estimated via calibration are doubly robust to model misspecification and explain why double robustness is not guaranteed when maximum likelihood is applied, and 3) we highlight problems related to response probabilities estimation, namely existence of a solution to the estimating equations, problems of convergence, and extreme weights. We present the results of a simulation study in order to illustrate these elements.
Release date: 2024-12-20
2. Authors’ response to comments on “Exploring the assumption that commercial online nonprobability survey respondents are answering in good faith”
Articles and reports: 12-001-X202400100009
Description: 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
3. Comments by Aditi Sen on “Exploring the assumption that commercial online nonprobability survey respondents are answering in good faith”
Articles and reports: 12-001-X202400100010
Description: 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
4. Comments by Michael R. Elliott on “Exploring the assumption that commercial online nonprobability survey respondents are answering in good faith”
Articles and reports: 12-001-X202400100011
Description: Kennedy, Mercer, and Lau explore misreporting by respondents in non-probability samples and discover a new feature, namely that of deliberate misreporting of demographic characteristics. This finding suggests that the “arms race” between researchers and those determined to disrupt the practice of social science is not over and researchers need to account for such respondents if using high-quality probability surveys to help reduce error in non-probability samples.
Release date: 2024-06-25
5. Comments by J. Michael Brick on “Exploring the assumption that commercial online nonprobability survey respondents are answering in good faith”
Articles and reports: 12-001-X202400100012
Description: Nonprobability samples are quick and low-cost and have become popular for some types of survey research. Kennedy, Mercer and Lau examine data quality issues associated with opt-in nonprobability samples frequently used in the United States. They show that the estimates from these samples have serious problems that go beyond representativeness. A total survey error perspective is important for evaluating all types of surveys.
Release date: 2024-06-25
6. Exploring the assumption that commercial online nonprobability survey respondents are answering in good faith
Articles and reports: 12-001-X202400100013
Description: Statistical approaches developed for nonprobability samples generally focus on nonrandom selection as the primary reason survey respondents might differ systematically from the target population. Well-established theory states that in these instances, by conditioning on the necessary auxiliary variables, selection can be rendered ignorable and survey estimates will be free of bias. But this logic rests on the assumption that measurement error is nonexistent or small. In this study we test this assumption in two ways. First, we use a large benchmarking study to identify subgroups for which errors in commercial, online nonprobability samples are especially large in ways that are unlikely due to selection effects. Then we present a follow-up study examining one cause of the large errors: bogus responding (i.e., survey answers that are fraudulent, mischievous or otherwise insincere). We find that bogus responding, particularly among respondents identifying as young or Hispanic, is a significant and widespread problem in commercial, online nonprobability samples, at least in the United States. This research highlights the need for statisticians working with commercial nonprobability samples to address bogus responding and issues of representativeness – not just the latter.
Release date: 2024-06-25
7. Ensuring that the Labour Force Survey Remains the Cornerstone of Canada’s Labour Market Information Ecosystem
Articles and reports: 75-005-M2024001
Description: From 2010 to 2019, the Labour Force Survey (LFS) response rate – or the proportion of selected households who complete an LFS interview – had been on a slow downward trend, due to a range of social and technological changes which have made it more challenging to contact selected households and to persuade Canadians to participate when they are contacted. These factors were exacerbated by the COVID-19 pandemic, which resulted in the suspension of face-to-face interviewing between April 2020 and fall 2022. Statistics Canada is committed to restoring LFS response rates to the greatest extent possible. This technical paper discusses two initiatives that are underway to ensure that the LFS estimates continue to provide an accurate and representative portrait of the Canadian labour market.
Release date: 2024-02-16
8. Sample designs and estimators for multimode surveys with face-to-face data collection
Articles and reports: 12-001-X202300200006
Description: Survey researchers are increasingly turning to multimode data collection to deal with declines in survey response rates and increasing costs. An efficient approach offers the less costly modes (e.g., web) followed with a more expensive mode for a subsample of the units (e.g., households) within each primary sampling unit (PSU). We present two alternatives to this traditional design. One alternative subsamples PSUs rather than units to constrain costs. The second is a hybrid design that includes a clustered (two-stage) sample and an independent, unclustered sample. Using a simulation, we demonstrate the hybrid design has considerable advantages.
Release date: 2024-01-03
9. Labour Force Survey Response Rates, September 2023
Surveys and statistical programs – Documentation: 75-005-M2023001
Description: This document provides information on the evolution of response rates for the Labour Force Survey (LFS) and a discussion of the evaluation of two aspects of data quality that ensure the LFS estimates continue providing an accurate portrait of the Canadian labour market.
Release date: 2023-10-30
10. Study: Attrition in the Longitudinal and International Study of Adults, Wave 1 (2012) to Wave 4 (2018) Archived
Stats in brief: 11-001-X202231822683
Description: Release published in The Daily – Statistics Canada’s official release bulletin
Release date: 2022-11-14

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Analysis (141)

Analysis (141) (0 to 10 of 141 results)

1. Inference from sampling with response probabilities estimated via calibration
Articles and reports: 12-001-X202400200013
Description: A solution to control for nonresponse bias consists of multiplying the design weights of respondents by the inverse of estimated response probabilities to compensate for the nonrespondents. Maximum likelihood and calibration are two approaches that can be applied to obtain estimated response probabilities. We consider a common framework in which these approaches can be compared. We develop an asymptotic study of the behavior of the resulting estimator when calibration is applied. A logistic regression model for the response probabilities is postulated. Missing at random and unclustered data are supposed. Three main contributions of this work are: 1) we show that the estimators with the response probabilities estimated via calibration are asymptotically equivalent to unbiased estimators and that a gain in efficiency is obtained when estimating the response probabilities via calibration as compared to the estimator with the true response probabilities, 2) we show that the estimators with the response probabilities estimated via calibration are doubly robust to model misspecification and explain why double robustness is not guaranteed when maximum likelihood is applied, and 3) we highlight problems related to response probabilities estimation, namely existence of a solution to the estimating equations, problems of convergence, and extreme weights. We present the results of a simulation study in order to illustrate these elements.
Release date: 2024-12-20
2. Authors’ response to comments on “Exploring the assumption that commercial online nonprobability survey respondents are answering in good faith”
Articles and reports: 12-001-X202400100009
Description: 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
3. Comments by Aditi Sen on “Exploring the assumption that commercial online nonprobability survey respondents are answering in good faith”
Articles and reports: 12-001-X202400100010
Description: 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
4. Comments by Michael R. Elliott on “Exploring the assumption that commercial online nonprobability survey respondents are answering in good faith”
Articles and reports: 12-001-X202400100011
Description: Kennedy, Mercer, and Lau explore misreporting by respondents in non-probability samples and discover a new feature, namely that of deliberate misreporting of demographic characteristics. This finding suggests that the “arms race” between researchers and those determined to disrupt the practice of social science is not over and researchers need to account for such respondents if using high-quality probability surveys to help reduce error in non-probability samples.
Release date: 2024-06-25
5. Comments by J. Michael Brick on “Exploring the assumption that commercial online nonprobability survey respondents are answering in good faith”
Articles and reports: 12-001-X202400100012
Description: Nonprobability samples are quick and low-cost and have become popular for some types of survey research. Kennedy, Mercer and Lau examine data quality issues associated with opt-in nonprobability samples frequently used in the United States. They show that the estimates from these samples have serious problems that go beyond representativeness. A total survey error perspective is important for evaluating all types of surveys.
Release date: 2024-06-25
6. Exploring the assumption that commercial online nonprobability survey respondents are answering in good faith
Articles and reports: 12-001-X202400100013
Description: Statistical approaches developed for nonprobability samples generally focus on nonrandom selection as the primary reason survey respondents might differ systematically from the target population. Well-established theory states that in these instances, by conditioning on the necessary auxiliary variables, selection can be rendered ignorable and survey estimates will be free of bias. But this logic rests on the assumption that measurement error is nonexistent or small. In this study we test this assumption in two ways. First, we use a large benchmarking study to identify subgroups for which errors in commercial, online nonprobability samples are especially large in ways that are unlikely due to selection effects. Then we present a follow-up study examining one cause of the large errors: bogus responding (i.e., survey answers that are fraudulent, mischievous or otherwise insincere). We find that bogus responding, particularly among respondents identifying as young or Hispanic, is a significant and widespread problem in commercial, online nonprobability samples, at least in the United States. This research highlights the need for statisticians working with commercial nonprobability samples to address bogus responding and issues of representativeness – not just the latter.
Release date: 2024-06-25
7. Ensuring that the Labour Force Survey Remains the Cornerstone of Canada’s Labour Market Information Ecosystem
Articles and reports: 75-005-M2024001
Description: From 2010 to 2019, the Labour Force Survey (LFS) response rate – or the proportion of selected households who complete an LFS interview – had been on a slow downward trend, due to a range of social and technological changes which have made it more challenging to contact selected households and to persuade Canadians to participate when they are contacted. These factors were exacerbated by the COVID-19 pandemic, which resulted in the suspension of face-to-face interviewing between April 2020 and fall 2022. Statistics Canada is committed to restoring LFS response rates to the greatest extent possible. This technical paper discusses two initiatives that are underway to ensure that the LFS estimates continue to provide an accurate and representative portrait of the Canadian labour market.
Release date: 2024-02-16
8. Sample designs and estimators for multimode surveys with face-to-face data collection
Articles and reports: 12-001-X202300200006
Description: Survey researchers are increasingly turning to multimode data collection to deal with declines in survey response rates and increasing costs. An efficient approach offers the less costly modes (e.g., web) followed with a more expensive mode for a subsample of the units (e.g., households) within each primary sampling unit (PSU). We present two alternatives to this traditional design. One alternative subsamples PSUs rather than units to constrain costs. The second is a hybrid design that includes a clustered (two-stage) sample and an independent, unclustered sample. Using a simulation, we demonstrate the hybrid design has considerable advantages.
Release date: 2024-01-03
9. Study: Attrition in the Longitudinal and International Study of Adults, Wave 1 (2012) to Wave 4 (2018) Archived
Stats in brief: 11-001-X202231822683
Description: Release published in The Daily – Statistics Canada’s official release bulletin
Release date: 2022-11-14
10. Attrition in the Longitudinal and International Study of Adults, Wave 1 (2012) to Wave 4 (2018)
Articles and reports: 89-648-X2022001
Description:
This report explores the size and nature of the attrition challenges faced by the Longitudinal and International Study of Adults (LISA) survey, as well as the use of a non-response weight adjustment and calibration strategy to mitigate the effects of attrition on the LISA estimates. The study focuses on data from waves 1 (2012) to 4 (2018) and uses practical examples based on selected demographic variables, to illustrate how attrition be assessed and treated.

Release date: 2022-11-14

Reference (1)

Reference (1) ((1 result))

1. Labour Force Survey Response Rates, September 2023
Surveys and statistical programs – Documentation: 75-005-M2023001
Description: This document provides information on the evolution of response rates for the Labour Force Survey (LFS) and a discussion of the evaluation of two aspects of data quality that ensure the LFS estimates continue providing an accurate portrait of the Canadian labour market.
Release date: 2023-10-30

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Date modified:: 2025-03-22