Estimation tools for reducing the impact of sampling and nonresponse errors in dual‐frame RDD telephone surveys

We discuss alternative estimators of the population total given a dual‐frame random‐digit‐dial (RDD) telephone survey in which samples are selected from landline and cell phone sampling frames. The estimators are subject to sampling and nonsampling errors. To reduce sampling variability when an opti...

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Published inStatistics in medicine Vol. 38; no. 23; pp. 4718 - 4732
Main Authors Wolter, Kirk M., Ganesh, N., Copeland, Kennon R., Singleton, James A., Khare, Meena
Format Journal Article
LanguageEnglish
Published England Wiley Subscription Services, Inc 15.10.2019
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Abstract We discuss alternative estimators of the population total given a dual‐frame random‐digit‐dial (RDD) telephone survey in which samples are selected from landline and cell phone sampling frames. The estimators are subject to sampling and nonsampling errors. To reduce sampling variability when an optimum balance of landline and cell phone samples is not feasible, we develop an application of shrinkage estimation. We demonstrate the implications for survey weighting of a differential nonresponse mechanism by telephone status. We illustrate these ideas using data from the National Immunization Survey‐Child, a large dual‐frame RDD telephone survey sponsored by the Centers for Disease Control and Prevention and conducted to measure the vaccination status of American children aged 19 to 35 months.
AbstractList We discuss alternative estimators of the population total given a dual‐frame random‐digit‐dial (RDD) telephone survey in which samples are selected from landline and cell phone sampling frames. The estimators are subject to sampling and nonsampling errors. To reduce sampling variability when an optimum balance of landline and cell phone samples is not feasible, we develop an application of shrinkage estimation. We demonstrate the implications for survey weighting of a differential nonresponse mechanism by telephone status. We illustrate these ideas using data from the National Immunization Survey‐Child, a large dual‐frame RDD telephone survey sponsored by the Centers for Disease Control and Prevention and conducted to measure the vaccination status of American children aged 19 to 35 months.
We discuss alternative estimators of the population total given a dual‐frame random‐digit‐dial (RDD) telephone survey in which samples are selected from landline and cell phone sampling frames. The estimators are subject to sampling and nonsampling errors. To reduce sampling variability when an optimum balance of landline and cell phone samples is not feasible, we develop an application of shrinkage estimation. We demonstrate the implications for survey weighting of a differential nonresponse mechanism by telephone status. We illustrate these ideas using data from the National Immunization Survey‐Child, a large dual‐frame RDD telephone survey sponsored by the Centers for Disease Control and Prevention and conducted to measure the vaccination status of American children aged 19 to 35 months.
We discuss alternative estimators of the population total given a dual-frame random-digit-dial (RDD) telephone survey in which samples are selected from landline and cell phone sampling frames. The estimators are subject to sampling and nonsampling errors. To reduce sampling variability when an optimum balance of landline and cell phone samples is not feasible, we develop an application of shrinkage estimation. We demonstrate the implications for survey weighting of a differential nonresponse mechanism by telephone status. We illustrate these ideas using data from the National Immunization Survey-Child, a large dual-frame RDD telephone survey sponsored by the Centers for Disease Control and Prevention and conducted to measure the vaccination status of American children aged 19 to 35 months.We discuss alternative estimators of the population total given a dual-frame random-digit-dial (RDD) telephone survey in which samples are selected from landline and cell phone sampling frames. The estimators are subject to sampling and nonsampling errors. To reduce sampling variability when an optimum balance of landline and cell phone samples is not feasible, we develop an application of shrinkage estimation. We demonstrate the implications for survey weighting of a differential nonresponse mechanism by telephone status. We illustrate these ideas using data from the National Immunization Survey-Child, a large dual-frame RDD telephone survey sponsored by the Centers for Disease Control and Prevention and conducted to measure the vaccination status of American children aged 19 to 35 months.
Author Copeland, Kennon R.
Wolter, Kirk M.
Singleton, James A.
Ganesh, N.
Khare, Meena
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Keywords National Immunization Survey
RDD telephone surveys
differential nonresponse
sampling and nonsampling errors
shrinkage estimation
dual-frame sampling design
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Snippet We discuss alternative estimators of the population total given a dual‐frame random‐digit‐dial (RDD) telephone survey in which samples are selected from...
We discuss alternative estimators of the population total given a dual-frame random-digit-dial (RDD) telephone survey in which samples are selected from...
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SubjectTerms differential nonresponse
dual‐frame sampling design
Medical research
National Immunization Survey
RDD telephone surveys
sampling and nonsampling errors
shrinkage estimation
Title Estimation tools for reducing the impact of sampling and nonresponse errors in dual‐frame RDD telephone surveys
URI https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fsim.8329
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