Predicting Response Rates Once Again

We investigate the relation between a survey’s response burden and response rate, differentiating recruitment efforts and incentives paid. The results indicate that the effect of response burden is more negative than previously expected. Recruitment shifts the response curve upwards, while incentives flatten it. Surveys beyond 2,000 points appear overly burdensome, sustaining high response rates only through recruitment coupled with incentives. Without incentives, the level effect of recruitment quickly vanishes. Contrary to previous findings, we can not identify a negative time-trend. The data, functions and workflow underlying this analysis are organized as an R -package to foster a collective effort towards understanding response rates.

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Publication Details

Journal
Findings
Published
2024-11-15
DOI
https://doi.org/10.32866/001c.125481
Citations
3
Primary Topic
Statistical Methods and Bayesian Inference
Type
article
Field-Weighted Citation Impact
2.13
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article

Predicting Response Rates Once Again

Kay W. Axhausen, Daniel Heimgartner
3 citations
Findings
Statistical Methods and Bayesian Inference
2.13
article

Predicting Response Rates Once Again

Kay W. Axhausen, Daniel Heimgartner
article en
3 citations

Abstract

We investigate the relation between a survey’s response burden and response rate, differentiating recruitment efforts and incentives paid. The results indicate that the effect of response burden is more negative than previously expected. Recruitment shifts the response curve upwards, while incentives flatten it. Surveys beyond 2,000 points appear overly burdensome, sustaining high response rates only through recruitment coupled with incentives. Without incentives, the level effect of recruitment quickly vanishes. Contrary to previous findings, we can not identify a negative time-trend. The data, functions and workflow underlying this analysis are organized as an R -package to foster a collective effort towards understanding response rates.

Findings
ETH Zurich (CH)
Openalex Percentile: Top 12%
Statistical Methods and Bayesian Inference
2.13
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