The one-sayers model for the Extended Crosswise design
Gespeichert in:
| Titel: | The one-sayers model for the Extended Crosswise design |
|---|---|
| Autoren: | Maarten Cruyff, Khadiga H. A. Sayed, Andrea Petróczi, P.G.M. van der Heijden |
| Quelle: | Journal of the Royal Statistical Society Series A: Statistics in Society. 187:882-899 |
| Verlagsinformationen: | Oxford University Press (OUP), 2024. |
| Publikationsjahr: | 2024 |
| Schlagwörter: | Response bias, Statistics and Probability, Survey Sampling, 05 social sciences, 01 natural sciences, 0504 sociology, Artificial Intelligence, Cheating, Physical Sciences, Multi-label Text Classification in Machine Learning, Computer Science, Doping, FOS: Mathematics, Self-protection, 0101 mathematics, Randomized response, Statistical Methods for Sensitive Survey Questions, Model-Based Clustering with Mixture Models, 10. No inequality, Mathematics |
| Beschreibung: | The Extended Crosswise design is a randomized response design characterized by a sensitive and an innocuous question and two sub-samples with complementary randomization probabilities of the innocuous question. The response categories are ‘One’ with two different answers and ‘Two’ with two answers that are the same. Due to the complementary randomization probabilities, ‘One’ is the incriminating response in one sub-sample, and ‘Two’ in the other. The use of two sub-samples generates a degree of freedom to test for response biases with a goodness-of-fit test, but this test is unable to detect bias resulting from self-protective respondents giving the non-incriminating response when the incriminating response was required. This raises the question what a significant goodness-of-fit test measures? In this paper, we hypothesize that respondents are largely unaware which response is associated with the sensitive characteristic, and intuitively perceive ‘One’ as the safer response. We present empirical evidence for one-saying in six surveys among a total of 4,242 elite athletes, and present estimates of doping use corrected for it. Furthermore, logistic regression analyses are conducted to test the hypothesis that respondents who complete the survey in a short time are more likely to answer randomly, and therefore are less likely to be one-sayers. |
| Publikationsart: | Article Other literature type |
| Sprache: | English |
| ISSN: | 1467-985X 0964-1998 |
| DOI: | 10.1093/jrsssa/qnae009 |
| DOI: | 10.60692/x1wm3-1y891 |
| DOI: | 10.60692/wddzh-9t972 |
| Rights: | CC BY |
| Dokumentencode: | edsair.doi.dedup.....253cc8a27e7c77e7c69e8c292738f76e |
| Datenbank: | OpenAIRE |
| Abstract: | The Extended Crosswise design is a randomized response design characterized by a sensitive and an innocuous question and two sub-samples with complementary randomization probabilities of the innocuous question. The response categories are ‘One’ with two different answers and ‘Two’ with two answers that are the same. Due to the complementary randomization probabilities, ‘One’ is the incriminating response in one sub-sample, and ‘Two’ in the other. The use of two sub-samples generates a degree of freedom to test for response biases with a goodness-of-fit test, but this test is unable to detect bias resulting from self-protective respondents giving the non-incriminating response when the incriminating response was required. This raises the question what a significant goodness-of-fit test measures? In this paper, we hypothesize that respondents are largely unaware which response is associated with the sensitive characteristic, and intuitively perceive ‘One’ as the safer response. We present empirical evidence for one-saying in six surveys among a total of 4,242 elite athletes, and present estimates of doping use corrected for it. Furthermore, logistic regression analyses are conducted to test the hypothesis that respondents who complete the survey in a short time are more likely to answer randomly, and therefore are less likely to be one-sayers. |
|---|---|
| ISSN: | 1467985X 09641998 |
| DOI: | 10.1093/jrsssa/qnae009 |
Nájsť tento článok vo Web of Science