Propensity score matching with SPSS : logistic regression analysis for cross-sectional data /
This case study presents the use of propensity score matching for measuring causal effects of e-cigarette use on smoking cessation in cross-sectional data. Compared with experimental studies, observational studies are vulnerable to selection bias. To identify a more robust measure of the association...
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| Format: | eBook |
| Language: | English |
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London :
SAGE Publications Ltd,
2020.
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| Series: | SAGE Research Methods Cases: Medicine and Health.
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| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
| Summary: | This case study presents the use of propensity score matching for measuring causal effects of e-cigarette use on smoking cessation in cross-sectional data. Compared with experimental studies, observational studies are vulnerable to selection bias. To identify a more robust measure of the association between e-cigarette use and smoking cessation, propensity score matching was applied in the study. This case study describes the problem of selection effects and the use of cross-sectional methods that minimize the selection bias in representing the influence of e-cigarette use on smoking cessation. This case study uses data from the sixth Korea National Health and Nutrition Examination Survey (2013-2015) of 2,965 adult smokers aged 19 years and older, and it takes a stage-by-stage approach to explain how to conduct propensity score matching using statistical software package SPSS 23.0. This case study can help other researchers and students learn how to understand the concept of propensity score matching, conduct propensity score matching, and interpret the results of propensity score matching. |
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| Physical Description: | 1 online resource : illustrations. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 9781529719468 1529719461 |