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- 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: 12-001-X202200100007Description:
By record linkage one joins records residing in separate files which are believed to be related to the same entity. In this paper we approach record linkage as a classification problem, and adapt the maximum entropy classification method in machine learning to record linkage, both in the supervised and unsupervised settings of machine learning. The set of links will be chosen according to the associated uncertainty. On the one hand, our framework overcomes some persistent theoretical flaws of the classical approach pioneered by Fellegi and Sunter (1969); on the other hand, the proposed algorithm is fully automatic, unlike the classical approach that generally requires clerical review to resolve the undecided cases.
Release date: 2022-06-21 - Articles and reports: 12-001-X202100100003Description:
One effective way to conduct statistical disclosure control is to use scrambled responses. Scrambled responses can be generated by using a controlled random device. In this paper, we propose using the sample empirical likelihood approach to conduct statistical inference under complex survey design with scrambled responses. Specifically, we propose using a Wilk-type confidence interval for statistical inference. Our proposed method can be used as a general tool for inference with confidential public use survey data files. Asymptotic properties are derived, and the limited simulation study verifies the validity of theory. We further apply the proposed method to some real applications.
Release date: 2021-06-24 - Articles and reports: 11-633-X2021003Description:
Canada continues to experience an opioid crisis. While there is solid information on the demographic and geographic characteristics of people experiencing fatal and non-fatal opioid overdoses in Canada, there is limited information on the social and economic conditions of those who experience these events. To fill this information gap, Statistics Canada collaborated with existing partnerships in British Columbia, including the BC Coroners Service, BC Stats, the BC Centre for Disease Control and the British Columbia Ministry of Health, to create the Statistics Canada British Columbia Opioid Overdose Analytical File (BC-OOAF).
Release date: 2021-02-17 - Articles and reports: 12-001-X202000100005Description:
Selecting the right sample size is central to ensure the quality of a survey. The state of the art is to account for complex sampling designs by calculating effective sample sizes. These effective sample sizes are determined using the design effect of central variables of interest. However, in face-to-face surveys empirical estimates of design effects are often suspected to be conflated with the impact of the interviewers. This typically leads to an over-estimation of design effects and consequently risks misallocating resources towards a higher sample size instead of using more interviewers or improving measurement accuracy. Therefore, we propose a corrected design effect that separates the interviewer effect from the effects of the sampling design on the sampling variance. The ability to estimate the corrected design effect is tested using a simulation study. In this respect, we address disentangling cluster and interviewer variance. Corrected design effects are estimated for data from the European Social Survey (ESS) round 6 and compared with conventional design effect estimates. Furthermore, we show that for some countries in the ESS round 6 the estimates of conventional design effect are indeed strongly inflated by interviewer effects.
Release date: 2020-06-30
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Articles and reports (5)
Articles and reports (5) ((5 results))
- 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: 12-001-X202200100007Description:
By record linkage one joins records residing in separate files which are believed to be related to the same entity. In this paper we approach record linkage as a classification problem, and adapt the maximum entropy classification method in machine learning to record linkage, both in the supervised and unsupervised settings of machine learning. The set of links will be chosen according to the associated uncertainty. On the one hand, our framework overcomes some persistent theoretical flaws of the classical approach pioneered by Fellegi and Sunter (1969); on the other hand, the proposed algorithm is fully automatic, unlike the classical approach that generally requires clerical review to resolve the undecided cases.
Release date: 2022-06-21 - Articles and reports: 12-001-X202100100003Description:
One effective way to conduct statistical disclosure control is to use scrambled responses. Scrambled responses can be generated by using a controlled random device. In this paper, we propose using the sample empirical likelihood approach to conduct statistical inference under complex survey design with scrambled responses. Specifically, we propose using a Wilk-type confidence interval for statistical inference. Our proposed method can be used as a general tool for inference with confidential public use survey data files. Asymptotic properties are derived, and the limited simulation study verifies the validity of theory. We further apply the proposed method to some real applications.
Release date: 2021-06-24 - Articles and reports: 11-633-X2021003Description:
Canada continues to experience an opioid crisis. While there is solid information on the demographic and geographic characteristics of people experiencing fatal and non-fatal opioid overdoses in Canada, there is limited information on the social and economic conditions of those who experience these events. To fill this information gap, Statistics Canada collaborated with existing partnerships in British Columbia, including the BC Coroners Service, BC Stats, the BC Centre for Disease Control and the British Columbia Ministry of Health, to create the Statistics Canada British Columbia Opioid Overdose Analytical File (BC-OOAF).
Release date: 2021-02-17 - Articles and reports: 12-001-X202000100005Description:
Selecting the right sample size is central to ensure the quality of a survey. The state of the art is to account for complex sampling designs by calculating effective sample sizes. These effective sample sizes are determined using the design effect of central variables of interest. However, in face-to-face surveys empirical estimates of design effects are often suspected to be conflated with the impact of the interviewers. This typically leads to an over-estimation of design effects and consequently risks misallocating resources towards a higher sample size instead of using more interviewers or improving measurement accuracy. Therefore, we propose a corrected design effect that separates the interviewer effect from the effects of the sampling design on the sampling variance. The ability to estimate the corrected design effect is tested using a simulation study. In this respect, we address disentangling cluster and interviewer variance. Corrected design effects are estimated for data from the European Social Survey (ESS) round 6 and compared with conventional design effect estimates. Furthermore, we show that for some countries in the ESS round 6 the estimates of conventional design effect are indeed strongly inflated by interviewer effects.
Release date: 2020-06-30
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