<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Survey researchers have been investigating alternative approaches to reduce data collection costs while mitigating the risk of nonresponse bias or to produce more accurate estimates within the same budget. Responsive or adaptive design has been suggested as one means for doing this. Falling survey response rates and the need to find effective ways of implementing responsive design has focused attention on the relationship between response rates and nonresponse bias. In our article, we re-examine the data compiled by <xref ref-type="bibr" rid="j_jos-2017-0034_ref_013_w2aab2b8c10b1b7b1ab1ac13Aa">Groves and Peytcheva (2008)</xref> in their influential article and show there is an important between-study component of variance in addition to the within-study variance highlighted in the original analysis. We also show that theory implies that raising response rates can help reduce the nonresponse bias on average across the estimates within a study. We then propose a typology of response propensity models that help explain the empirical findings, including the relative weak relationship between nonresponse rates and nonresponse bias. Using these results, we explore when responsive design tools such as switching modes, giving monetary incentives, and increasing the level of effort are likely to be effective. We conclude with some comments on the use of responsive design and weighting to control nonresponse bias.</p></abstract>

Survey researchers have been investigating alternative approaches to reduce data collection costs while mitigating the risk of nonresponse bias or to produce more accurate estimates within the same budget. Responsive or adaptive design has been suggested as one means for doing this. Falling survey response rates and the need to find effective ways of implementing responsive design has focused attention on the relationship between response rates and nonresponse bias. In our article, we re-examine the data compiled by Groves and Peytcheva (2008) in their influential article and show there is an important between-study component of variance in addition to the within-study variance highlighted in the original analysis. We also show that theory implies that raising response rates can help reduce the nonresponse bias on average across the estimates within a study. We then propose a typology of response propensity models that help explain the empirical findings, including the relative weak relationship between nonresponse rates and nonresponse bias. Using these results, we explore when responsive design tools such as switching modes, giving monetary incentives, and increasing the level of effort are likely to be effective. We conclude with some comments on the use of responsive design and weighting to control nonresponse bias.

Responsive Survey Designs for Reducing Nonresponse Bias

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Responsive Survey Designs for Reducing Nonresponse Bias"}

Survey researchers have been investigating alternative approaches to reduce data collection costs while mitigating the risk of nonresponse bias or to produce...

Responsive Survey Designs for Reducing Nonresponse Bias

Online veröffentlicht: 09. Sept. 2017

Seitenbereich: 735 - 752

Eingereicht: 01. Mai 2016

Akzeptiert: 01. Juni 2017

DOI: https://doi.org/10.1515/jos-2017-0034

Schlüsselwörter
Adaptive design, response propensities, two-phase sampling

© 2017 J. Michael Brick et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Responsive Survey Designs for Reducing Nonresponse Bias

Online veröffentlicht: 09. Sept. 2017

Seitenbereich: 735 - 752

Eingereicht: 01. Mai 2016

Akzeptiert: 01. Juni 2017

DOI: https://doi.org/10.1515/jos-2017-0034

SchlüsselwörterAdaptive design, response propensities, two-phase sampling

© 2017 J. Michael Brick et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Adaptive design, response propensities, two-phase sampling