Response and nonresponse
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All (22)
All (22) (0 to 10 of 22 results)
- Articles and reports: 12-001-X201900100002Description:
Item nonresponse is frequently encountered in sample surveys. Hot-deck imputation is commonly used to fill in missing item values within homogeneous groups called imputation classes. We propose a fractional hot-deck imputation procedure and an associated empirical likelihood for inference on the population mean of a function of a variable of interest with missing data under probability proportional to size sampling with negligible sampling fractions. We derive the limiting distributions of the maximum empirical likelihood estimator and empirical likelihood ratio, and propose two related asymptotically valid bootstrap procedures to construct confidence intervals for the population mean. Simulation studies show that the proposed bootstrap procedures outperform the customary bootstrap procedures which are shown to be asymptotically incorrect when the number of random draws in the fractional imputation is fixed. Moreover, the proposed bootstrap procedure based on the empirical likelihood ratio is seen to perform significantly better than the method based on the limiting distribution of the maximum empirical likelihood estimator when the inclusion probabilities vary considerably or when the sample size is not large.
Release date: 2019-05-07 - Articles and reports: 12-001-X201800254952Description:
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 - Articles and reports: 12-001-X201700114820Description:
Measurement errors can induce bias in the estimation of transitions, leading to erroneous conclusions about labour market dynamics. Traditional literature on gross flows estimation is based on the assumption that measurement errors are uncorrelated over time. This assumption is not realistic in many contexts, because of survey design and data collection strategies. In this work, we use a model-based approach to correct observed gross flows from classification errors with latent class Markov models. We refer to data collected with the Italian Continuous Labour Force Survey, which is cross-sectional, quarterly, with a 2-2-2 rotating design. The questionnaire allows us to use multiple indicators of labour force conditions for each quarter: two collected in the first interview, and a third collected one year later. Our approach provides a method to estimate labour market mobility, taking into account correlated errors and the rotating design of the survey. The best-fitting model is a mixed latent class Markov model with covariates affecting latent transitions and correlated errors among indicators; the mixture components are of mover-stayer type. The better fit of the mixture specification is due to more accurately estimated latent transitions.
Release date: 2017-06-22 - Articles and reports: 12-001-X201200211755Description:
Non-response in longitudinal studies is addressed by assessing the accuracy of response propensity models constructed to discriminate between and predict different types of non-response. Particular attention is paid to summary measures derived from receiver operating characteristic (ROC) curves and logit rank plots. The ideas are applied to data from the UK Millennium Cohort Study. The results suggest that the ability to discriminate between and predict non-respondents is not high. Weights generated from the response propensity models lead to only small adjustments in employment transitions. Conclusions are drawn in terms of the potential of interventions to prevent non-response.
Release date: 2012-12-19 - 5. Respondent differences and length of data collection in the Behavioral Risk Factor Surveillance System ArchivedArticles and reports: 12-001-X201000211384Description:
The current economic downturn in the US could challenge costly strategies in survey operations. In the Behavioral Risk Factor Surveillance System (BRFSS), ending the monthly data collection at 31 days could be a less costly alternative. However, this could potentially exclude a portion of interviews completed after 31 days (late responders) whose respondent characteristics could be different in many respects from those who completed the survey within 31 days (early responders). We examined whether there are differences between the early and late responders in demographics, health-care coverage, general health status, health risk behaviors, and chronic disease conditions or illnesses. We used 2007 BRFSS data, where a representative sample of the noninstitutionalized adult U.S. population was selected using a random digit dialing method. Late responders were significantly more likely to be male; to report race/ethnicity as Hispanic; to have annual income higher than $50,000; to be younger than 45 years of age; to have less than high school education; to have health-care coverage; to be significantly more likely to report good health; and to be significantly less likely to report hypertension, diabetes, or being obese. The observed differences between early and late responders on survey estimates may hardly influence national and state-level estimates. As the proportion of late responders may increase in the future, its impact on surveillance estimates should be examined before excluding from the analysis. Analysis on late responders only should combine several years of data to produce reliable estimates.
Release date: 2010-12-21 - Articles and reports: 11-522-X200800010976Description:
Many survey organizations use the response rate as an indicator for the quality of survey data. As a consequence, a variety of measures are implemented to reduce non-response or to maintain response at an acceptable level. However, the response rate is not necessarily a good indicator of non-response bias. A higher response rate does not imply smaller non-response bias. What matters is how the composition of the response differs from the composition of the sample as a whole. This paper describes the concept of R-indicators to assess potential differences between the sample and the response. Such indicators may facilitate analysis of survey response over time, between various fieldwork strategies or data collection modes. Some practical examples are given.
Release date: 2009-12-03 - Articles and reports: 11-522-X200800010994Description:
The growing difficulty of reaching respondents has a general impact on non-response in telephone surveys, especially those that use random digit dialling (RDD), such as the General Social Survey (GSS). The GSS is an annual multipurpose survey with 25,000 respondents. Its aim is to monitor the characteristics of and major changes in Canada's social structure. GSS Cycle 21 (2007) was about the family, social support and retirement. Its target population consisted of persons aged 45 and over living in the 10 Canadian provinces. For more effective coverage, part of the sample was taken from a follow-up with the respondents of GSS Cycle 20 (2006), which was on family transitions. The remainder was a new RDD sample. In this paper, we describe the survey's sampling plan and the random digit dialling method used. Then we discuss the challenges of calculating the non-response rate in an RDD survey that targets a subset of a population, for which the in-scope population must be estimated or modelled. This is done primarily through the use of paradata. The methodology used in GSS Cycle 21 is presented in detail.
Release date: 2009-12-03 - 8. Is there really any benefit in sending out introductory letters in Random Digit Dialling (RDD) surveys? ArchivedArticles and reports: 11-522-X200800011001Description:
Currently underway, the Québec Population Health Survey (EQSP), for which collection will wrap up in February 2009, provides an opportunity, because of the size of its sample, to assess the impact that sending out introductory letters to respondents has on the response rate in a controlled environment. Since this regional telephone survey is expected to have more than 38,000 respondents, it was possible to use part of its sample for this study without having too great an impact on its overall response rate. In random digit dialling (RDD) surveys such as the EQSP, one of the main challenges in sending out introductory letters is reaching the survey units. Doing so depends largely on our capacity to associate an address with the sample units and on the quality of that information.
This article describes the controlled study proposed by the Institut de la statistique du Québec to measure the effect that sending out introductory letters to respondents had on the survey's response rate.
Release date: 2009-12-03 - Articles and reports: 12-001-X200900110887Description:
Many survey organisations focus on the response rate as being the quality indicator for the impact of non-response bias. As a consequence, they implement a variety of measures to reduce non-response or to maintain response at some acceptable level. However, response rates alone are not good indicators of non-response bias. In general, higher response rates do not imply smaller non-response bias. The literature gives many examples of this (e.g., Groves and Peytcheva 2006, Keeter, Miller, Kohut, Groves and Presser 2000, Schouten 2004).
We introduce a number of concepts and an indicator to assess the similarity between the response and the sample of a survey. Such quality indicators, which we call R-indicators, may serve as counterparts to survey response rates and are primarily directed at evaluating the non-response bias. These indicators may facilitate analysis of survey response over time, between various fieldwork strategies or data collection modes. We apply the R-indicators to two practical examples.
Release date: 2009-06-22 - 10. Effects of measurement on obesity and morbidity ArchivedArticles and reports: 82-003-X200800210564Geography: CanadaDescription:
This article compares associations between body mass index categories based on self-reported versus measured data with selected health conditions. The goal is to determine if the misclassification resulting from the use of self-reported data alters associations between excess weight and these health conditions. The analysis is based on 2,667 respondents aged 40 or older from the 2005 Canadian Community Health Survey.
Release date: 2008-05-14
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Analysis (22)
Analysis (22) (0 to 10 of 22 results)
- Articles and reports: 12-001-X201900100002Description:
Item nonresponse is frequently encountered in sample surveys. Hot-deck imputation is commonly used to fill in missing item values within homogeneous groups called imputation classes. We propose a fractional hot-deck imputation procedure and an associated empirical likelihood for inference on the population mean of a function of a variable of interest with missing data under probability proportional to size sampling with negligible sampling fractions. We derive the limiting distributions of the maximum empirical likelihood estimator and empirical likelihood ratio, and propose two related asymptotically valid bootstrap procedures to construct confidence intervals for the population mean. Simulation studies show that the proposed bootstrap procedures outperform the customary bootstrap procedures which are shown to be asymptotically incorrect when the number of random draws in the fractional imputation is fixed. Moreover, the proposed bootstrap procedure based on the empirical likelihood ratio is seen to perform significantly better than the method based on the limiting distribution of the maximum empirical likelihood estimator when the inclusion probabilities vary considerably or when the sample size is not large.
Release date: 2019-05-07 - Articles and reports: 12-001-X201800254952Description:
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 - Articles and reports: 12-001-X201700114820Description:
Measurement errors can induce bias in the estimation of transitions, leading to erroneous conclusions about labour market dynamics. Traditional literature on gross flows estimation is based on the assumption that measurement errors are uncorrelated over time. This assumption is not realistic in many contexts, because of survey design and data collection strategies. In this work, we use a model-based approach to correct observed gross flows from classification errors with latent class Markov models. We refer to data collected with the Italian Continuous Labour Force Survey, which is cross-sectional, quarterly, with a 2-2-2 rotating design. The questionnaire allows us to use multiple indicators of labour force conditions for each quarter: two collected in the first interview, and a third collected one year later. Our approach provides a method to estimate labour market mobility, taking into account correlated errors and the rotating design of the survey. The best-fitting model is a mixed latent class Markov model with covariates affecting latent transitions and correlated errors among indicators; the mixture components are of mover-stayer type. The better fit of the mixture specification is due to more accurately estimated latent transitions.
Release date: 2017-06-22 - Articles and reports: 12-001-X201200211755Description:
Non-response in longitudinal studies is addressed by assessing the accuracy of response propensity models constructed to discriminate between and predict different types of non-response. Particular attention is paid to summary measures derived from receiver operating characteristic (ROC) curves and logit rank plots. The ideas are applied to data from the UK Millennium Cohort Study. The results suggest that the ability to discriminate between and predict non-respondents is not high. Weights generated from the response propensity models lead to only small adjustments in employment transitions. Conclusions are drawn in terms of the potential of interventions to prevent non-response.
Release date: 2012-12-19 - 5. Respondent differences and length of data collection in the Behavioral Risk Factor Surveillance System ArchivedArticles and reports: 12-001-X201000211384Description:
The current economic downturn in the US could challenge costly strategies in survey operations. In the Behavioral Risk Factor Surveillance System (BRFSS), ending the monthly data collection at 31 days could be a less costly alternative. However, this could potentially exclude a portion of interviews completed after 31 days (late responders) whose respondent characteristics could be different in many respects from those who completed the survey within 31 days (early responders). We examined whether there are differences between the early and late responders in demographics, health-care coverage, general health status, health risk behaviors, and chronic disease conditions or illnesses. We used 2007 BRFSS data, where a representative sample of the noninstitutionalized adult U.S. population was selected using a random digit dialing method. Late responders were significantly more likely to be male; to report race/ethnicity as Hispanic; to have annual income higher than $50,000; to be younger than 45 years of age; to have less than high school education; to have health-care coverage; to be significantly more likely to report good health; and to be significantly less likely to report hypertension, diabetes, or being obese. The observed differences between early and late responders on survey estimates may hardly influence national and state-level estimates. As the proportion of late responders may increase in the future, its impact on surveillance estimates should be examined before excluding from the analysis. Analysis on late responders only should combine several years of data to produce reliable estimates.
Release date: 2010-12-21 - Articles and reports: 11-522-X200800010976Description:
Many survey organizations use the response rate as an indicator for the quality of survey data. As a consequence, a variety of measures are implemented to reduce non-response or to maintain response at an acceptable level. However, the response rate is not necessarily a good indicator of non-response bias. A higher response rate does not imply smaller non-response bias. What matters is how the composition of the response differs from the composition of the sample as a whole. This paper describes the concept of R-indicators to assess potential differences between the sample and the response. Such indicators may facilitate analysis of survey response over time, between various fieldwork strategies or data collection modes. Some practical examples are given.
Release date: 2009-12-03 - Articles and reports: 11-522-X200800010994Description:
The growing difficulty of reaching respondents has a general impact on non-response in telephone surveys, especially those that use random digit dialling (RDD), such as the General Social Survey (GSS). The GSS is an annual multipurpose survey with 25,000 respondents. Its aim is to monitor the characteristics of and major changes in Canada's social structure. GSS Cycle 21 (2007) was about the family, social support and retirement. Its target population consisted of persons aged 45 and over living in the 10 Canadian provinces. For more effective coverage, part of the sample was taken from a follow-up with the respondents of GSS Cycle 20 (2006), which was on family transitions. The remainder was a new RDD sample. In this paper, we describe the survey's sampling plan and the random digit dialling method used. Then we discuss the challenges of calculating the non-response rate in an RDD survey that targets a subset of a population, for which the in-scope population must be estimated or modelled. This is done primarily through the use of paradata. The methodology used in GSS Cycle 21 is presented in detail.
Release date: 2009-12-03 - 8. Is there really any benefit in sending out introductory letters in Random Digit Dialling (RDD) surveys? ArchivedArticles and reports: 11-522-X200800011001Description:
Currently underway, the Québec Population Health Survey (EQSP), for which collection will wrap up in February 2009, provides an opportunity, because of the size of its sample, to assess the impact that sending out introductory letters to respondents has on the response rate in a controlled environment. Since this regional telephone survey is expected to have more than 38,000 respondents, it was possible to use part of its sample for this study without having too great an impact on its overall response rate. In random digit dialling (RDD) surveys such as the EQSP, one of the main challenges in sending out introductory letters is reaching the survey units. Doing so depends largely on our capacity to associate an address with the sample units and on the quality of that information.
This article describes the controlled study proposed by the Institut de la statistique du Québec to measure the effect that sending out introductory letters to respondents had on the survey's response rate.
Release date: 2009-12-03 - Articles and reports: 12-001-X200900110887Description:
Many survey organisations focus on the response rate as being the quality indicator for the impact of non-response bias. As a consequence, they implement a variety of measures to reduce non-response or to maintain response at some acceptable level. However, response rates alone are not good indicators of non-response bias. In general, higher response rates do not imply smaller non-response bias. The literature gives many examples of this (e.g., Groves and Peytcheva 2006, Keeter, Miller, Kohut, Groves and Presser 2000, Schouten 2004).
We introduce a number of concepts and an indicator to assess the similarity between the response and the sample of a survey. Such quality indicators, which we call R-indicators, may serve as counterparts to survey response rates and are primarily directed at evaluating the non-response bias. These indicators may facilitate analysis of survey response over time, between various fieldwork strategies or data collection modes. We apply the R-indicators to two practical examples.
Release date: 2009-06-22 - 10. Effects of measurement on obesity and morbidity ArchivedArticles and reports: 82-003-X200800210564Geography: CanadaDescription:
This article compares associations between body mass index categories based on self-reported versus measured data with selected health conditions. The goal is to determine if the misclassification resulting from the use of self-reported data alters associations between excess weight and these health conditions. The analysis is based on 2,667 respondents aged 40 or older from the 2005 Canadian Community Health Survey.
Release date: 2008-05-14
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