Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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FUZZY matching using propensity score: IBM SPSS 22 Ver.

성향 점수를 이용한 퍼지 매칭 방법: IBM SPSS 22 Ver.

  • 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 : 2015.12.01
  • Accepted : 2016.01.18
  • Published : 2016.01.31
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Abstract

Fuzzy matching is proposed to make propensities of two groups similar with their propensity scores and a way to select control variable to make propensity scores with a process that shows how to acquire propensity scores using logic regression analysis, is presented. With such scores, it was a method to obtain an experiment group and a control group that had similar propensity employing the Fuzzy Matching. In the study, it was proven that the two groups were the same but with a different distribution chart and standardization which made edge tolerance different and we realized that the number of chosen cases decreased when the edge tolerance score became smaller. So with the idea, we were able to determine that it is possible to merge groups using fuzzy matching without a precontrol and use them when data (big data) are used while to check the pros and cons of Fuzzy Matching were made possible.

성향점수 매칭이란 선택편의가 존재 할 수 있는 두 집단의 데이터를 성향 점수로 매칭하여 비슷한 성향을 갖는 데이터를 추출하는 방법이다. 본 논문은 그 중 하나인 퍼지 매칭 방법을 제시하였다. 성향 점수를 만들기 위해 통제변수를 선정하는 방법과 로지스틱 회귀분석을 통하여 성향 점수를 구하는 방법을 제시하였으며, 이 점수로 퍼지 매칭을 통해 성향이 비슷한 실험 집단과 통제 집단을 추출할 수 있었다. 본 논문에서는 허용오차 범위를 달리하여 분포도와 표준화 차이를 통해 두 집단이 동일한 집단임을 증명했으며, 허용오차 범위 점수가 작아질수록 선택되어 지는 케이스 수도 작아지는 것을 확인 할 수 있었다.

Keywords

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