Response and nonresponse

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All (6)

All (6) ((6 results))

  • 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-X201200111688
    Description:

    We study the problem of nonignorable nonresponse in a two dimensional contingency table which can be constructed for each of several small areas when there is both item and unit nonresponse. In general, the provision for both types of nonresponse with small areas introduces significant additional complexity in the estimation of model parameters. For this paper, we conceptualize the full data array for each area to consist of a table for complete data and three supplemental tables for missing row data, missing column data, and missing row and column data. For nonignorable nonresponse, the total cell probabilities are allowed to vary by area, cell and these three types of "missingness". The underlying cell probabilities (i.e., those which would apply if full classification were always possible) for each area are generated from a common distribution and their similarity across the areas is parametrically quantified. Our approach is an extension of the selection approach for nonignorable nonresponse investigated by Nandram and Choi (2002a, b) for binary data; this extension creates additional complexity because of the multivariate nature of the data coupled with the small area structure. As in that earlier work, the extension is an expansion model centered on an ignorable nonresponse model so that the total cell probability is dependent upon which of the categories is the response. Our investigation employs hierarchical Bayesian models and Markov chain Monte Carlo methods for posterior inference. The models and methods are illustrated with data from the third National Health and Nutrition Examination Survey.

    Release date: 2012-06-27

  • Articles and reports: 12-001-X201100111443
    Description:

    Dual frame telephone surveys are becoming common in the U.S. because of the incompleteness of the landline frame as people transition to cell phones. This article examines nonsampling errors in dual frame telephone surveys. Even though nonsampling errors are ignored in much of the dual frame literature, we find that under some conditions substantial biases may arise in dual frame telephone surveys due to these errors. We specifically explore biases due to nonresponse and measurement error in these telephone surveys. To reduce the bias resulting from these errors, we propose dual frame sampling and weighting methods. The compositing factor for combining the estimates from the two frames is shown to play an important role in reducing nonresponse bias.

    Release date: 2011-06-29

  • Articles and reports: 12-001-X20050018089
    Description:

    We use hierarchical Bayesian models to analyze body mass index (BMI) data of children and adolescents with nonignorable nonresponse from the Third National Health and Nutrition Examination Survey (NHANES III). Our objective is to predict the finite population mean BMI and the proportion of respondents for domains formed by age, race and sex (covariates in the regression models) in each of thirty five large counties, accounting for the nonrespondents. Markov chain Monte Carlo methods are used to fit the models (two selection and two pattern mixture) to the NHANES III BMI data. Using a deviance measure and a cross-validation study, we show that the nonignorable selection model is the best among the four models. We also show that inference about BMI is not too sensitive to the model choice. An improvement is obtained by including a spline regression into the selection model to reflect changes in the relationship between BMI and age.

    Release date: 2005-07-21

  • Articles and reports: 12-001-X20020026428
    Description:

    The analysis of survey data from different geographical areas where the data from each area are polychotomous can be easily performed using hierarchical Bayesian models, even if there are small cell counts in some of these areas. However, there are difficulties when the survey data have missing information in the form of non-response, especially when the characteristics of the respondents differ from the non-respondents. We use the selection approach for estimation when there are non-respondents because it permits inference for all the parameters. Specifically, we describe a hierarchical Bayesian model to analyse multinomial non-ignorable non-response data from different geographical areas; some of them can be small. For the model, we use a Dirichlet prior density for the multinomial probabilities and a beta prior density for the response probabilities. This permits a 'borrowing of strength' of the data from larger areas to improve the reliability in the estimates of the model parameters corresponding to the smaller areas. Because the joint posterior density of all the parameters is complex, inference is sampling-based and Markov chain Monte Carlo methods are used. We apply our method to provide an analysis of body mass index (BMI) data from the third National Health and Nutrition Examination Survey (NHANES III). For simplicity, the BMI is categorized into 3 natural levels, and this is done for each of 8 age-race-sex domains and 34 counties. We assess the performance of our model using the NHANES III data and simulated examples, which show our model works reasonably well.

    Release date: 2003-01-29

  • Articles and reports: 12-001-X19980024352
    Description:

    The National Population Health Survey (NPHS) is one of Statistics Canada's three major longitudinal household surveys providing an extensive coverage of the Canadian population. A panel of approximately 17,000 people are being followed up every two years for up to twenty years. The survey data are used for longitudinal analyses, although an important objective is the production of cross-sectional estimates. Each cycle panel respondents provide detailed health information (H) while, to augment the cross-sectional sample, general socio-demographic and health information (G) are collected from all members of their households. This particular collection strategy presents several observable response patterns for Panel Members after two cycles: GH-GH, GH-G*, GH-**, G*-GH, G*-G* and G*-**, where "*" denotes a missing portion of data. The article presents the methodology developed to deal with these types of longitudinal nonresponse as well as with nonresponse from a cross-sectional perspective. The use of weight adjustments for nonresponse and the creation of adjustment cells for weighting using a CHAID algorithm are discussed.

    Release date: 1999-01-14
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Analysis (6)

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  • 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-X201200111688
    Description:

    We study the problem of nonignorable nonresponse in a two dimensional contingency table which can be constructed for each of several small areas when there is both item and unit nonresponse. In general, the provision for both types of nonresponse with small areas introduces significant additional complexity in the estimation of model parameters. For this paper, we conceptualize the full data array for each area to consist of a table for complete data and three supplemental tables for missing row data, missing column data, and missing row and column data. For nonignorable nonresponse, the total cell probabilities are allowed to vary by area, cell and these three types of "missingness". The underlying cell probabilities (i.e., those which would apply if full classification were always possible) for each area are generated from a common distribution and their similarity across the areas is parametrically quantified. Our approach is an extension of the selection approach for nonignorable nonresponse investigated by Nandram and Choi (2002a, b) for binary data; this extension creates additional complexity because of the multivariate nature of the data coupled with the small area structure. As in that earlier work, the extension is an expansion model centered on an ignorable nonresponse model so that the total cell probability is dependent upon which of the categories is the response. Our investigation employs hierarchical Bayesian models and Markov chain Monte Carlo methods for posterior inference. The models and methods are illustrated with data from the third National Health and Nutrition Examination Survey.

    Release date: 2012-06-27

  • Articles and reports: 12-001-X201100111443
    Description:

    Dual frame telephone surveys are becoming common in the U.S. because of the incompleteness of the landline frame as people transition to cell phones. This article examines nonsampling errors in dual frame telephone surveys. Even though nonsampling errors are ignored in much of the dual frame literature, we find that under some conditions substantial biases may arise in dual frame telephone surveys due to these errors. We specifically explore biases due to nonresponse and measurement error in these telephone surveys. To reduce the bias resulting from these errors, we propose dual frame sampling and weighting methods. The compositing factor for combining the estimates from the two frames is shown to play an important role in reducing nonresponse bias.

    Release date: 2011-06-29

  • Articles and reports: 12-001-X20050018089
    Description:

    We use hierarchical Bayesian models to analyze body mass index (BMI) data of children and adolescents with nonignorable nonresponse from the Third National Health and Nutrition Examination Survey (NHANES III). Our objective is to predict the finite population mean BMI and the proportion of respondents for domains formed by age, race and sex (covariates in the regression models) in each of thirty five large counties, accounting for the nonrespondents. Markov chain Monte Carlo methods are used to fit the models (two selection and two pattern mixture) to the NHANES III BMI data. Using a deviance measure and a cross-validation study, we show that the nonignorable selection model is the best among the four models. We also show that inference about BMI is not too sensitive to the model choice. An improvement is obtained by including a spline regression into the selection model to reflect changes in the relationship between BMI and age.

    Release date: 2005-07-21

  • Articles and reports: 12-001-X20020026428
    Description:

    The analysis of survey data from different geographical areas where the data from each area are polychotomous can be easily performed using hierarchical Bayesian models, even if there are small cell counts in some of these areas. However, there are difficulties when the survey data have missing information in the form of non-response, especially when the characteristics of the respondents differ from the non-respondents. We use the selection approach for estimation when there are non-respondents because it permits inference for all the parameters. Specifically, we describe a hierarchical Bayesian model to analyse multinomial non-ignorable non-response data from different geographical areas; some of them can be small. For the model, we use a Dirichlet prior density for the multinomial probabilities and a beta prior density for the response probabilities. This permits a 'borrowing of strength' of the data from larger areas to improve the reliability in the estimates of the model parameters corresponding to the smaller areas. Because the joint posterior density of all the parameters is complex, inference is sampling-based and Markov chain Monte Carlo methods are used. We apply our method to provide an analysis of body mass index (BMI) data from the third National Health and Nutrition Examination Survey (NHANES III). For simplicity, the BMI is categorized into 3 natural levels, and this is done for each of 8 age-race-sex domains and 34 counties. We assess the performance of our model using the NHANES III data and simulated examples, which show our model works reasonably well.

    Release date: 2003-01-29

  • Articles and reports: 12-001-X19980024352
    Description:

    The National Population Health Survey (NPHS) is one of Statistics Canada's three major longitudinal household surveys providing an extensive coverage of the Canadian population. A panel of approximately 17,000 people are being followed up every two years for up to twenty years. The survey data are used for longitudinal analyses, although an important objective is the production of cross-sectional estimates. Each cycle panel respondents provide detailed health information (H) while, to augment the cross-sectional sample, general socio-demographic and health information (G) are collected from all members of their households. This particular collection strategy presents several observable response patterns for Panel Members after two cycles: GH-GH, GH-G*, GH-**, G*-GH, G*-G* and G*-**, where "*" denotes a missing portion of data. The article presents the methodology developed to deal with these types of longitudinal nonresponse as well as with nonresponse from a cross-sectional perspective. The use of weight adjustments for nonresponse and the creation of adjustment cells for weighting using a CHAID algorithm are discussed.

    Release date: 1999-01-14
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