Responsive and Adaptive Survey Design: Use of Bias Propensity During Data Collection to Reduce Nonresponse Bias
| Autor: | Daniel J. Pratt, Andy Peytchev, Michael Andrew Duprey |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Statistics and Probability
Data collection Applied Mathematics 05 social sciences Survey research 01 natural sciences 0506 political science 010104 statistics & probability Statistics 050602 political science & public administration Non-response bias 0101 mathematics Statistics Probability and Uncertainty Psychology Social Sciences (miscellaneous) |
| Zdroj: | Journal of Survey Statistics and Methodology. 10:131-148 |
| ISSN: | 2325-0992 2325-0984 |
| DOI: | 10.1093/jssam/smaa013 |
| Popis: | Reduction in nonresponse bias has been a key focus in responsive and adaptive survey designs, through multiple phases of data collection, each defined by a different protocol, and targeting interventions to a subset of sample elements. Key in this approach is the identification of nonrespondents who, if interviewed, can reduce nonresponse bias in survey estimates. From a design perspective, we need to identify an appropriate model to select targeted cases, in addition to an effective intervention (change in protocol). From an evaluation perspective, we need to compare estimates to a control condition that is often omitted from study designs, in addition to the need for benchmark estimates for key survey measures to provide estimates of nonresponse bias. We introduced a bias propensity approach for the selection of sample members to reduce nonresponse bias. Unlike a response propensity approach in which the objective is to maximize the prediction of nonresponse, this new approach deliberately excludes strong predictors of nonresponse that are uncorrelated with survey measures and uses covariates that are of substantive interest to the study. We also devised an analytic approach to simulate which sample members would have responded in a control condition. This study also provided a rare opportunity to estimate nonresponse bias, using rich sampling frame information, prior round survey data, and data from extensive nonresponse follow-up. The bias propensity model yielded reasonable fit despite the exclusion of the strongest predictors of nonresponse. The intervention was found to be effective in increasing participation among identified sample members. On average, the responsive and adaptive survey design reduced nonresponse bias by more than one-quarter—almost one percentage point—regardless of the choice of benchmark estimates. Effort under the control condition did not reduce nonresponse bias. While results are strongly encouraging, we argue for replication with varied populations and methods. |
| Databáze: | OpenAIRE |
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