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• 1. Nonresponse adjustments with misspecified models in stratified designs Archived

  Articles and reports: 12-001-X201600114546

  Description:

  Adjusting the base weights using weighting classes is a standard approach for dealing with unit nonresponse. A common approach is to create nonresponse adjustments that are weighted by the inverse of the assumed response propensity of respondents within weighting classes under a quasi-randomization approach. Little and Vartivarian (2003) questioned the value of weighting the adjustment factor. In practice the models assumed are misspecified, so it is critical to understand the impact of weighting might have in this case. This paper describes the effects on nonresponse adjusted estimates of means and totals for population and domains computed using the weighted and unweighted inverse of the response propensities in stratified simple random sample designs. The performance of these estimators under different conditions such as different sample allocation, response mechanism, and population structure is evaluated. The findings show that for the scenarios considered the weighted adjustment has substantial advantages for estimating totals and using an unweighted adjustment may lead to serious biases except in very limited cases. Furthermore, unlike the unweighted estimates, the weighted estimates are not sensitive to how the sample is allocated.

  Release date: 2016-06-22

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• 2. Nonsampling errors in dual frame telephone surveys Archived

  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

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Articles and reports (2) ((2 results))

• 1. Nonresponse adjustments with misspecified models in stratified designs Archived

  Articles and reports: 12-001-X201600114546

  Description:

  Adjusting the base weights using weighting classes is a standard approach for dealing with unit nonresponse. A common approach is to create nonresponse adjustments that are weighted by the inverse of the assumed response propensity of respondents within weighting classes under a quasi-randomization approach. Little and Vartivarian (2003) questioned the value of weighting the adjustment factor. In practice the models assumed are misspecified, so it is critical to understand the impact of weighting might have in this case. This paper describes the effects on nonresponse adjusted estimates of means and totals for population and domains computed using the weighted and unweighted inverse of the response propensities in stratified simple random sample designs. The performance of these estimators under different conditions such as different sample allocation, response mechanism, and population structure is evaluated. The findings show that for the scenarios considered the weighted adjustment has substantial advantages for estimating totals and using an unweighted adjustment may lead to serious biases except in very limited cases. Furthermore, unlike the unweighted estimates, the weighted estimates are not sensitive to how the sample is allocated.

  Release date: 2016-06-22

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• 2. Nonsampling errors in dual frame telephone surveys Archived

  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

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