| Wang Jiao,Li Changping,Hu Liangping,One-level multiple Logistic regression analysis of the dichotomous choice data collected from the complex sampling survey design[J].SICHUAN MENTAL HEALTH,2019,32(5):385-389 |
| One-level multiple Logistic regression analysis of the dichotomous choice data collected from the complex sampling survey design |
DOI：10.11886/j.issn.1007-3256.2019.05.001 |
| English keywords:Complex sampling Binary data Logistic regression analysis Sampling weights Derived variable |
| Fund projects:国家高技术研究发展计划课题资助（2015AA020102） |
| Author Name | Affiliation | Postcode | | Wang Jiao | Department of Health Statistics, School of Public Health, Tianjin Medical University, Tianjin 300070, China | 300070 | | Li Changping* | Department of Health Statistics, School of Public Health, Tianjin Medical University, Tianjin 300070, China
Specialty Committee of Clinical Scientific Research Statistics of World Federation of Chinese Medicine Societies, Beijing 100029, China | 100029 | | Hu Liangping | Specialty Committee of Clinical Scientific Research Statistics of World Federation of Chinese Medicine Societies, Beijing 100029, China
Graduate School, Academy of Military Sciences PLA China, Beijing 100850, China | 100850 |
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| English abstract: |
| The purpose of this paper was to introduce the method of multiple logistic regression analysis for binary data of complex sampling survey design. Eight different analysis strategies (regardless of sampling design and sampling weights; considering sampling design without considering sampling weights; without considering sampling design but considering sampling weights, and considering both sampling design and sampling weights, and then considering the derived variables under the four situations mentioned before, respectively) were used to model and analyze the survey data. By comparing the results, the following conclusions were drawn: in the process of statistical analysis of complex sampling design data, the conclusions obtained by considering sampling design and sampling weights were more in line with the real situation of the dependence between internal variables of data. In addition, this study also introduced the detailed steps of using SURVEYLOGISTIC procedure in SAS software to carry out multiple logistic regression analysis of complex sampling survey data. |
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