Response and nonresponse

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All (136) (0 to 10 of 136 results)

  • 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

  • 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

  • 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

  • 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

  • Stats in brief: 11-001-X202231822683
    Description: Release published in The Daily – Statistics Canada’s official release bulletin
    Release date: 2022-11-14

  • Articles and reports: 12-001-X202200100001
    Description:

    In this study, we investigate to what extent the respondent characteristics age and educational level may be associated with undesirable answer behaviour (UAB) consistently across surveys. We use data from panel respondents who participated in ten general population surveys of CentERdata and Statistics Netherlands. A new method to visually present UAB and an inventive adaptation of a non-parametric effect size measure are used. The occurrence of UAB of respondents with specific characteristics is summarized in density distributions that we refer to as respondent profiles. An adaptation of the robust effect size Cliff’s Delta is used to compare respondent profiles on the potentially consistent occurrence of UAB across surveys. Taking all surveys together, the degree of UAB varies by age and education. The results do not show consistent UAB across individual surveys: Age and educational level are associated with a relatively higher occurrence of UAB for some surveys, but a relatively lower occurrence for other surveys. We conclude that the occurrence of UAB across surveys may be more dependent on the survey and its items than on respondent’s cognitive ability.

    Release date: 2022-06-21

  • Articles and reports: 12-001-X202200100006
    Description:

    In the last two decades, survey response rates have been steadily falling. In that context, it has become increasingly important for statistical agencies to develop and use methods that reduce the adverse effects of non-response on the accuracy of survey estimates. Follow-up of non-respondents may be an effective, albeit time and resource-intensive, remedy for non-response bias. We conducted a simulation study using real business survey data to shed some light on several questions about non-response follow-up. For instance, assuming a fixed non-response follow-up budget, what is the best way to select non-responding units to be followed up? How much effort should be dedicated to repeatedly following up non-respondents until a response is received? Should they all be followed up or a sample of them? If a sample is followed up, how should it be selected? We compared Monte Carlo relative biases and relative root mean square errors under different follow-up sampling designs, sample sizes and non-response scenarios. We also determined an expression for the minimum follow-up sample size required to expend the budget, on average, and showed that it maximizes the expected response rate. A main conclusion of our simulation experiment is that this sample size also appears to approximately minimize the bias and mean square error of the estimates.

    Release date: 2022-06-21

  • Articles and reports: 12-001-X202100200004
    Description:

    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-X201900300002
    Description:

    Paradata is often collected during the survey process to monitor the quality of the survey response. One such paradata is a respondent behavior, which can be used to construct response models. The propensity score weight using the respondent behavior information can be applied to the final analysis to reduce the nonresponse bias. However, including the surrogate variable in the propensity score weighting does not always guarantee the efficiency gain. We show that the surrogate variable is useful only when it is correlated with the study variable. Results from a limited simulation study confirm the finding. A real data application using the Korean Workplace Panel Survey data is also presented.

    Release date: 2019-12-17

  • Articles and reports: 12-001-X201900300006
    Description:

    High nonresponse is a very common problem in sample surveys today. In statistical terms we are worried about increased bias and variance of estimators for population quantities such as totals or means. Different methods have been suggested in order to compensate for this phenomenon. We can roughly divide them into imputation and calibration and it is the latter approach we will focus on here. A wide spectrum of possibilities is included in the class of calibration estimators. We explore linear calibration, where we suggest using a nonresponse version of the design-based optimal regression estimator. Comparisons are made between this estimator and a GREG type estimator. Distance measures play a very important part in the construction of calibration estimators. We show that an estimator of the average response propensity (probability) can be included in the “optimal” distance measure under nonresponse, which will help to reduce the bias of the resulting estimator. To illustrate empirically the theoretically derived results for the suggested estimators, a simulation study has been carried out. The population is called KYBOK and consists of clerical municipalities in Sweden, where the variables include financial as well as size measurements. The results are encouraging for the “optimal” estimator in combination with the estimated average response propensity, where the bias was reduced for most of the Poisson sampling cases in the study.

    Release date: 2019-12-17
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Analysis (135)

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  • 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

  • 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

  • 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

  • Stats in brief: 11-001-X202231822683
    Description: Release published in The Daily – Statistics Canada’s official release bulletin
    Release date: 2022-11-14

  • Articles and reports: 12-001-X202200100001
    Description:

    In this study, we investigate to what extent the respondent characteristics age and educational level may be associated with undesirable answer behaviour (UAB) consistently across surveys. We use data from panel respondents who participated in ten general population surveys of CentERdata and Statistics Netherlands. A new method to visually present UAB and an inventive adaptation of a non-parametric effect size measure are used. The occurrence of UAB of respondents with specific characteristics is summarized in density distributions that we refer to as respondent profiles. An adaptation of the robust effect size Cliff’s Delta is used to compare respondent profiles on the potentially consistent occurrence of UAB across surveys. Taking all surveys together, the degree of UAB varies by age and education. The results do not show consistent UAB across individual surveys: Age and educational level are associated with a relatively higher occurrence of UAB for some surveys, but a relatively lower occurrence for other surveys. We conclude that the occurrence of UAB across surveys may be more dependent on the survey and its items than on respondent’s cognitive ability.

    Release date: 2022-06-21

  • Articles and reports: 12-001-X202200100006
    Description:

    In the last two decades, survey response rates have been steadily falling. In that context, it has become increasingly important for statistical agencies to develop and use methods that reduce the adverse effects of non-response on the accuracy of survey estimates. Follow-up of non-respondents may be an effective, albeit time and resource-intensive, remedy for non-response bias. We conducted a simulation study using real business survey data to shed some light on several questions about non-response follow-up. For instance, assuming a fixed non-response follow-up budget, what is the best way to select non-responding units to be followed up? How much effort should be dedicated to repeatedly following up non-respondents until a response is received? Should they all be followed up or a sample of them? If a sample is followed up, how should it be selected? We compared Monte Carlo relative biases and relative root mean square errors under different follow-up sampling designs, sample sizes and non-response scenarios. We also determined an expression for the minimum follow-up sample size required to expend the budget, on average, and showed that it maximizes the expected response rate. A main conclusion of our simulation experiment is that this sample size also appears to approximately minimize the bias and mean square error of the estimates.

    Release date: 2022-06-21

  • Articles and reports: 12-001-X202100200004
    Description:

    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-X201900300002
    Description:

    Paradata is often collected during the survey process to monitor the quality of the survey response. One such paradata is a respondent behavior, which can be used to construct response models. The propensity score weight using the respondent behavior information can be applied to the final analysis to reduce the nonresponse bias. However, including the surrogate variable in the propensity score weighting does not always guarantee the efficiency gain. We show that the surrogate variable is useful only when it is correlated with the study variable. Results from a limited simulation study confirm the finding. A real data application using the Korean Workplace Panel Survey data is also presented.

    Release date: 2019-12-17

  • Articles and reports: 12-001-X201900300006
    Description:

    High nonresponse is a very common problem in sample surveys today. In statistical terms we are worried about increased bias and variance of estimators for population quantities such as totals or means. Different methods have been suggested in order to compensate for this phenomenon. We can roughly divide them into imputation and calibration and it is the latter approach we will focus on here. A wide spectrum of possibilities is included in the class of calibration estimators. We explore linear calibration, where we suggest using a nonresponse version of the design-based optimal regression estimator. Comparisons are made between this estimator and a GREG type estimator. Distance measures play a very important part in the construction of calibration estimators. We show that an estimator of the average response propensity (probability) can be included in the “optimal” distance measure under nonresponse, which will help to reduce the bias of the resulting estimator. To illustrate empirically the theoretically derived results for the suggested estimators, a simulation study has been carried out. The population is called KYBOK and consists of clerical municipalities in Sweden, where the variables include financial as well as size measurements. The results are encouraging for the “optimal” estimator in combination with the estimated average response propensity, where the bias was reduced for most of the Poisson sampling cases in the study.

    Release date: 2019-12-17

  • Articles and reports: 12-001-X201900300008
    Description:

    Dual frame surveys are useful when no single frame with adequate coverage exists. However estimators from dual frame designs require knowledge of the frame memberships of each sampled unit. When this information is not available from the frame itself, it is often collected from the respondent. When respondents provide incorrect membership information, the resulting estimators of means or totals can be biased. A method for reducing this bias, using accurate membership information obtained about a subsample of respondents, is proposed. The properties of the new estimator are examined and compared to alternative estimators. The proposed estimator is applied to the data from the motivating example, which was a recreational angler survey, using an address frame and an incomplete fishing license frame.

    Release date: 2019-12-17
Reference (1)

Reference (1) ((1 result))

  • 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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