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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2019.7
- 수록면
- 951 - 966 (16page)
- DOI
- 10.7465/jkdi.2019.30.4.951
이용수
초록· 키워드
오류제보하기
A multilevel structure of data is widely used in a variety of social science settings. To investigate the effects of interventions, researchers often conduct observational studies that use large scale secondary data and incorporate propensity score methods; this is beneficial in performing causal inference in non-randomized observational studies. The standard propensity score uses a logistic regression approach; however, this approach could be outperformed by alternative methods based on statistical learning and data mining algorithms. To date, little research had addressed data utilizing mining methods within propensity score design, especially with multilevel observational data. The purpose of this study is to examine the performance of propensity scores associated with the use of stratification, estimated by a multilevel logistic versus a conditional inference tree using large scale secondary data derived from the Programme for International Student Assessment. The results showed that a conditional inference tree more conservatively estimates the treatment effect. In addition, the covariate balance result showed that the CIT better produced a randomized treatment / control design than did the multilevel logistic regression.
목차
Abstract
1. Introduction
2. Theoretical framework
3. Methods
4. Results
5. Conclusion
References
참고문헌 (19)
참고문헌 신청
Austin, P. C. / 2011 / A tutorial and case study in propensity score analysis: an application to estimating the effect of in-hospital smoking cessation counseling on mortality / Multivariate Behavioral Research 46:119~151
Breiman, L. / 2001 / Random forests / Machine Learning 45:5~32
Bryer, J. M. / 2011 / An international comparison of private and public schools using multilevel propensity score methods and graphics / Multivariate Behavioral Research 46:1010~1011
dAgostino, R. B. / 1998 / Tutorial in biostatistics: Propensity score methods for bias reduction in the com-parison of a treatment to a non-randomized control group / Stat Med 17:2265~2281
Gurel, S. / 2014 / Evaluation propensity score strategies with multilevel data when treatment assignment mechanism varies between clusters
Breiman, L. / 2001 / Random forests / Machine Learning 45:5~32
Bryer, J. M. / 2011 / An international comparison of private and public schools using multilevel propensity score methods and graphics / Multivariate Behavioral Research 46:1010~1011
dAgostino, R. B. / 1998 / Tutorial in biostatistics: Propensity score methods for bias reduction in the com-parison of a treatment to a non-randomized control group / Stat Med 17:2265~2281
Gurel, S. / 2014 / Evaluation propensity score strategies with multilevel data when treatment assignment mechanism varies between clusters
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UCI(KEPA) : I410-ECN-0101-2019-041-000951433