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FUZZY matching using propensity score: IBM SPSS 22 Ver
Journal of the Korean Data & Information Science Society 2016;27:91-100
Published online January 31, 2016
© 2016 .

So Youn Kim · Jong Il Baek

Division of mathematics and informational statistics, Wonkwang University
Correspondence to:

Jong Il Baek

Professor, Division of mathematics and informational statistics, Wonkwang University, Iksan 54538, Korea.
E-mail: jibaek@wku.ac.kr

Received December 1, 2015; Revised January 8, 2016; Accepted January 18, 2016.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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.
Keywords : FUZZY mathcing, KYRBS, propensity score