The poisson extension of the unrelated question model: improving surveys with time-constrained questions on sensitive topics
The Poisson model (Iberl & Ulrich, in print) is a new survey technique that enables the estimation of how frequently a certain behavior occurs, while employing easy-to-answer yes/no-questions that refer to a specific time frame. In this paper, this model is combined with the unrelated question m...
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| Medienart: | Elektronisch Aufsatz |
| Sprache: | Englisch |
| Veröffentlicht: |
2024
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| In: |
Survey research methods
Jahr: 2024, Band: 18, Heft: 1, Seiten: 21-38 |
| Online-Zugang: |
Volltext (kostenfrei) Volltext (kostenfrei) |
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| 109 | |a Iberl, Benedikt |a Iberl, Benedikt Jonas |a Iberl, B. | ||
| 245 | 1 | 4 | |a The poisson extension of the unrelated question model |b improving surveys with time-constrained questions on sensitive topics |c Benedikt Iberl, Anesa Aljovic, Rolf Ulrich, Fabiola Reiber |
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| 520 | |a The Poisson model (Iberl & Ulrich, in print) is a new survey technique that enables the estimation of how frequently a certain behavior occurs, while employing easy-to-answer yes/no-questions that refer to a specific time frame. In this paper, this model is combined with the unrelated question model (UQM) by Greenberg et al. (1969). The UQM is another survey technique which guarantees complete and objective anonymity to participants in order to achieve more valid survey results when asking sensitive questions (e.g., about drug use). The resulting Poisson extension of the UQM (UQMP) is expected to yield valid estimations for how many participants engage in a researched sensitive behavior, and how regularly they do so. The performance of the UQMP was compared to the performance of the standard Poisson model, employing direct questions, in a survey on drinking and driving. While prevalence estimates differ greatly between the UQMP and the standard Poisson model, the results of both models indicate a high rate of drinking and driving among those German traffic participants who generally engage in this behavior. The difference between prevalence estimates might be explainable by problems of the UQMs’ validity; we discuss possible causes for these problems, and why the UQMP or similar approaches can be valuable nonetheless. | ||
| 650 | 4 | |a Randomized Response Technique | |
| 650 | 4 | |a Unrelated Question Model | |
| 650 | 4 | |a Survey Research | |
| 650 | 4 | |a time-constrained questions | |
| 650 | 4 | |a prevalence curve | |
| 650 | 4 | |a Poisson process | |
| 700 | 1 | |a Aljovic, Anesa |e VerfasserIn |4 aut | |
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| 700 | 1 | |a Reiber, Fabiola |e VerfasserIn |0 (DE-588)1152968688 |0 (DE-627)1014326559 |0 (DE-576)499964632 |4 aut | |
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