Symmetric variant truth detection model in sample surveys - a randomized response approach

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dc.contributor.author Mbala, Simon
dc.date.accessioned 2021-06-18T06:31:29Z
dc.date.available 2021-06-18T06:31:29Z
dc.date.issued 2021-06-18
dc.identifier.uri http://41.204.161.209/handle/11295/154097
dc.identifier.uri http://repository.seku.ac.ke/handle/123456789/6269
dc.description Doctor of Philosophy in Mathematical Statistics, 2020 en_US
dc.description.abstract In every survey truthfulness is required so as to come up with validdata for decision making. Most surveys use direct questioning to col-ect data. This method does not yield reliable information when the opic under investigation is sensitive in nature. In such surveys, directquestions are not useful as the respondents will either refuse to answerthe survey questions or, even if they do, may give false answers for fearof being known to have the sensitive characteristics. The less privacy a design offers, the more likely respondents cheat by disobeying the instructions thus giving very unreliable information which can lead to wrong decision making. In this study we have formulated a technique which we have called symmetric variant truth detection model. We have also formulated symmetric stratified truth detection model for analyzing stratified data. In this technique, we have used two ran-domization devices which do not require the respondents to disclosetheir identity thus increasing their privacy leading to more honest re-sponses. After developing the models, they were validated by the useof data simulation as well as real life application. It was establishedthat the symmetric truth detection models were more efficient com-pared to the asymmetric truth detection models. This study thereforerecommended that researchers on sensitive information to use symmet-ric truth detection models as opposed to asymmetric truth detection models. en_US
dc.language.iso en en_US
dc.title Symmetric variant truth detection model in sample surveys - a randomized response approach en_US
dc.type Thesis en_US


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