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All (10) ((10 results))
- Articles and reports: 12-001-X202200200004Description:
This discussion attempts to add to Wu’s review of inference from non-probability samples, as well as to highlighting aspects that are likely avenues for useful additional work. It concludes with a call for an organized stable of high-quality probability surveys that will be focused on providing adjustment information for non-probability surveys.
Release date: 2022-12-15 - Articles and reports: 81-599-X2022002Description:
This fact sheet presents the most recent information about on-time and extended-time high school graduation rates in Canada. While this measure will not reflect whether youth ultimately graduate from high school during their lifetime, it does provide policy-makers and researchers with information on how youth progress through their secondary level studies and if they complete them within the expected amount of time.
Release date: 2022-10-20 - 3. Education Indicators in Canada: Report of the Pan-Canadian Education Indicators Program, September 2022 ArchivedArticles and reports: 81-582-X2022002Description: The Pan-Canadian Education Indicators Program (PCEIP) draws from a wide variety of data sources to provide information on the school-age population, elementary, secondary and postsecondary education, transitions, and labour market outcomes. PCEIP products include tables, fact sheets, reports and a methodological handbook. They present indicators for all of Canada, the provinces, the territories, as well as selected international comparisons and comparisons over time. The Pan-Canadian Education Indicators Program (PCEIP) is an ongoing initiative of the Canadian Education Statistics Council, a partnership between Statistics Canada and the Council of Ministers of Education, Canada that provides a set of statistical measures on education systems in Canada.Release date: 2022-09-08
- Articles and reports: 18-001-X2022001Description:
Monitoring traffic in large urban areas remains a challenge for both practical and technical reasons. This paper presents a computer vision-based system to periodically extract vehicle counts from Canadian traffic camera imagery.
Release date: 2022-09-06 - Articles and reports: 12-001-X202200100005Description:
Methodological studies of the effects that human interviewers have on the quality of survey data have long been limited by a critical assumption: that interviewers in a given survey are assigned random subsets of the larger overall sample (also known as interpenetrated assignment). Absent this type of study design, estimates of interviewer effects on survey measures of interest may reflect differences between interviewers in the characteristics of their assigned sample members, rather than recruitment or measurement effects specifically introduced by the interviewers. Previous attempts to approximate interpenetrated assignment have typically used regression models to condition on factors that might be related to interviewer assignment. We introduce a new approach for overcoming this lack of interpenetrated assignment when estimating interviewer effects. This approach, which we refer to as the “anchoring” method, leverages correlations between observed variables that are unlikely to be affected by interviewers (“anchors”) and variables that may be prone to interviewer effects to remove components of within-interviewer correlations that lack of interpenetrated assignment may introduce. We consider both frequentist and Bayesian approaches, where the latter can make use of information about interviewer effect variances in previous waves of a study, if available. We evaluate this new methodology empirically using a simulation study, and then illustrate its application using real survey data from the Behavioral Risk Factor Surveillance System (BRFSS), where interviewer IDs are provided on public-use data files. While our proposed method shares some of the limitations of the traditional approach – namely the need for variables associated with the outcome of interest that are also free of measurement error – it avoids the need for conditional inference and thus has improved inferential qualities when the focus is on marginal estimates, and it shows evidence of further reducing overestimation of larger interviewer effects relative to the traditional approach.
Release date: 2022-06-21 - Articles and reports: 82-003-X202200600002Description:
An evaluation of progress in cancer survival in Canada for all cancer types combined was recently conducted using the cancer survival index. This study provides a comprehensive evaluation of provincial-level progress in cancer survival for all cancer types combined in Canada.
Release date: 2022-06-15 - Articles and reports: 82-003-X202200400003Description:
Certain population groups face a disproportionate burden of exposure to COVID-19. This study examined characteristics of Canadians living in private households in fall 2020 and winter 2021 who had been infected with COVID-19.
Release date: 2022-04-20 - Articles and reports: 82-003-X202200100001Description:
Recent evidence from the United States and Canada suggests an unexplained increase in small-for-gestational-age births (<10th percentile). This study aimed to identify reasons for the recent increase in small-for-gestational-age births in Canada.
Release date: 2022-01-19 - Articles and reports: 12-001-X202100200004Description:
This note presents a comparative study of three methods for constructing confidence intervals for the mean and quantiles based on survey data with nonresponse. These methods, empirical likelihood, linearization, and that of Woodruff’s (1952), were applied to data on income obtained from the 2015 Mexican Intercensal Survey, and to simulated data. A response propensity model was used for adjusting the sampling weights, and the empirical performance of the methods was assessed in terms of the coverage of the confidence intervals through simulation studies. The empirical likelihood and linearization methods had a good performance for the mean, except when the variable of interest had some extreme values. For quantiles, the linearization method had a poor performance, while the empirical likelihood and Woodruff methods had a better one, though without reaching the nominal coverage when the variable of interest had values with high frequency near the quantile of interest.
Release date: 2022-01-06 - Articles and reports: 12-001-X202100200006Description:
Sample-based calibration occurs when the weights of a survey are calibrated to control totals that are random, instead of representing fixed population-level totals. Control totals may be estimated from different phases of the same survey or from another survey. Under sample-based calibration, valid variance estimation requires that the error contribution due to estimating the control totals be accounted for. We propose a new variance estimation method that directly uses the replicate weights from two surveys, one survey being used to provide control totals for calibration of the other survey weights. No restrictions are set on the nature of the two replication methods and no variance-covariance estimates need to be computed, making the proposed method straightforward to implement in practice. A general description of the method for surveys with two arbitrary replication methods with different numbers of replicates is provided. It is shown that the resulting variance estimator is consistent for the asymptotic variance of the calibrated estimator, when calibration is done using regression estimation or raking. The method is illustrated in a real-world application, in which the demographic composition of two surveys needs to be harmonized to improve the comparability of the survey estimates.
Release date: 2022-01-06
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Articles and reports (10)
Articles and reports (10) ((10 results))
- Articles and reports: 12-001-X202200200004Description:
This discussion attempts to add to Wu’s review of inference from non-probability samples, as well as to highlighting aspects that are likely avenues for useful additional work. It concludes with a call for an organized stable of high-quality probability surveys that will be focused on providing adjustment information for non-probability surveys.
Release date: 2022-12-15 - Articles and reports: 81-599-X2022002Description:
This fact sheet presents the most recent information about on-time and extended-time high school graduation rates in Canada. While this measure will not reflect whether youth ultimately graduate from high school during their lifetime, it does provide policy-makers and researchers with information on how youth progress through their secondary level studies and if they complete them within the expected amount of time.
Release date: 2022-10-20 - 3. Education Indicators in Canada: Report of the Pan-Canadian Education Indicators Program, September 2022 ArchivedArticles and reports: 81-582-X2022002Description: The Pan-Canadian Education Indicators Program (PCEIP) draws from a wide variety of data sources to provide information on the school-age population, elementary, secondary and postsecondary education, transitions, and labour market outcomes. PCEIP products include tables, fact sheets, reports and a methodological handbook. They present indicators for all of Canada, the provinces, the territories, as well as selected international comparisons and comparisons over time. The Pan-Canadian Education Indicators Program (PCEIP) is an ongoing initiative of the Canadian Education Statistics Council, a partnership between Statistics Canada and the Council of Ministers of Education, Canada that provides a set of statistical measures on education systems in Canada.Release date: 2022-09-08
- Articles and reports: 18-001-X2022001Description:
Monitoring traffic in large urban areas remains a challenge for both practical and technical reasons. This paper presents a computer vision-based system to periodically extract vehicle counts from Canadian traffic camera imagery.
Release date: 2022-09-06 - Articles and reports: 12-001-X202200100005Description:
Methodological studies of the effects that human interviewers have on the quality of survey data have long been limited by a critical assumption: that interviewers in a given survey are assigned random subsets of the larger overall sample (also known as interpenetrated assignment). Absent this type of study design, estimates of interviewer effects on survey measures of interest may reflect differences between interviewers in the characteristics of their assigned sample members, rather than recruitment or measurement effects specifically introduced by the interviewers. Previous attempts to approximate interpenetrated assignment have typically used regression models to condition on factors that might be related to interviewer assignment. We introduce a new approach for overcoming this lack of interpenetrated assignment when estimating interviewer effects. This approach, which we refer to as the “anchoring” method, leverages correlations between observed variables that are unlikely to be affected by interviewers (“anchors”) and variables that may be prone to interviewer effects to remove components of within-interviewer correlations that lack of interpenetrated assignment may introduce. We consider both frequentist and Bayesian approaches, where the latter can make use of information about interviewer effect variances in previous waves of a study, if available. We evaluate this new methodology empirically using a simulation study, and then illustrate its application using real survey data from the Behavioral Risk Factor Surveillance System (BRFSS), where interviewer IDs are provided on public-use data files. While our proposed method shares some of the limitations of the traditional approach – namely the need for variables associated with the outcome of interest that are also free of measurement error – it avoids the need for conditional inference and thus has improved inferential qualities when the focus is on marginal estimates, and it shows evidence of further reducing overestimation of larger interviewer effects relative to the traditional approach.
Release date: 2022-06-21 - Articles and reports: 82-003-X202200600002Description:
An evaluation of progress in cancer survival in Canada for all cancer types combined was recently conducted using the cancer survival index. This study provides a comprehensive evaluation of provincial-level progress in cancer survival for all cancer types combined in Canada.
Release date: 2022-06-15 - Articles and reports: 82-003-X202200400003Description:
Certain population groups face a disproportionate burden of exposure to COVID-19. This study examined characteristics of Canadians living in private households in fall 2020 and winter 2021 who had been infected with COVID-19.
Release date: 2022-04-20 - Articles and reports: 82-003-X202200100001Description:
Recent evidence from the United States and Canada suggests an unexplained increase in small-for-gestational-age births (<10th percentile). This study aimed to identify reasons for the recent increase in small-for-gestational-age births in Canada.
Release date: 2022-01-19 - Articles and reports: 12-001-X202100200004Description:
This note presents a comparative study of three methods for constructing confidence intervals for the mean and quantiles based on survey data with nonresponse. These methods, empirical likelihood, linearization, and that of Woodruff’s (1952), were applied to data on income obtained from the 2015 Mexican Intercensal Survey, and to simulated data. A response propensity model was used for adjusting the sampling weights, and the empirical performance of the methods was assessed in terms of the coverage of the confidence intervals through simulation studies. The empirical likelihood and linearization methods had a good performance for the mean, except when the variable of interest had some extreme values. For quantiles, the linearization method had a poor performance, while the empirical likelihood and Woodruff methods had a better one, though without reaching the nominal coverage when the variable of interest had values with high frequency near the quantile of interest.
Release date: 2022-01-06 - Articles and reports: 12-001-X202100200006Description:
Sample-based calibration occurs when the weights of a survey are calibrated to control totals that are random, instead of representing fixed population-level totals. Control totals may be estimated from different phases of the same survey or from another survey. Under sample-based calibration, valid variance estimation requires that the error contribution due to estimating the control totals be accounted for. We propose a new variance estimation method that directly uses the replicate weights from two surveys, one survey being used to provide control totals for calibration of the other survey weights. No restrictions are set on the nature of the two replication methods and no variance-covariance estimates need to be computed, making the proposed method straightforward to implement in practice. A general description of the method for surveys with two arbitrary replication methods with different numbers of replicates is provided. It is shown that the resulting variance estimator is consistent for the asymptotic variance of the calibrated estimator, when calibration is done using regression estimation or raking. The method is illustrated in a real-world application, in which the demographic composition of two surveys needs to be harmonized to improve the comparability of the survey estimates.
Release date: 2022-01-06
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