On Logistic Regression Analysis Using Propensity Score Matching

성향점수매칭 방법을 사용한 로지스틱 회귀분석에 관한 연구

  • Kim, So Youn (Division of Mathematics and Informational Statistics, Wonkwang University) ;
  • Baek, Jong Il (Division of Mathematics and Informational Statistics, Wonkwang University)
  • 김소연 (원광대학교 자연과학대학 수학정보통계학부) ;
  • 백종일 (원광대학교 자연과학대학 수학정보통계학부)
  • Received : 2016.09.30
  • Accepted : 2016.12.16
  • Published : 2016.12.25


Purpose: Recently, propensity score matching method is used in a large number of research paper, nonetheless, there is no research using fitness test of before and after propensity score matching. Therefore, comparing fitness of before and after propensity score matching by logistic regression analysis using data from 'online survey of adolescent health' is the main significance of this research. Method: Data that has similar propensity in two groups is extracted by using propensity score matching then implement logistic regression analysis on before and after matching separately. Results: To test fitness of logistic regression analysis model, we use Model summary, -2Log Likelihood and Hosmer-Lomeshow methods. As a result, it is confirmed that the data after matching is more suitable for logistic regression analysis than data before matching. Conclusion: Therefore, better result which has appropriate fitness will be shown by using propensity score matching shows better result which has better fitness.



Supported by : 원광대학교


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