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미술치료에서 단일대상연구 자료의 효과크기 분석 방법

Methods for Effect Size Analysis of Single-subject Research Data in Art Therapy

유경미|조용태
사)한국아동발달지원연구소 (The Child Developmental Supporting Center)|2018.12|임상미술심리연구|제8권 제3호|pp. 1-26|DOI 10.34167/cliart.8.3.201812.1

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참고문헌

키워드

효과크기단일대상연구Effect Size비중복 분석방법Single-subject ResearchNon-overlapping Analysis Methods

초록

본 연구는 단일대상연구의 결과 분석방법을 간략하게 소개하고, 효과크기 계산의 유형과 개념을 중점적으로 탐구하는데 목적이 있다. 이를 위해 단일대상연구 자료의 분석 방법 유형과 단일대상연구 자료의 비중복 방법을 탐구한다. 단일대상연구의 분석방법에는 시각적 분석방법, 비중복 분석방법, 통계적 분석방법이 있다. 비중복 분석방법 가운데 비교적 빈번하게 언급되는 9가지 방법을 탐구하였다. 이 방법들을 검토한 결과 다음과 같은 결론을 얻을 수 있었다. 첫째, 단일대상연구에서 빈번하게 사용되는 비중복 분석방법은 비중복 자료 백분율이다. 둘째, 모든 쌍의 비중복과 Tau-U는 상대적으로 복잡한 통계적 절차를 거쳐야 하지만, 나머지 방법은 비교적 간단하게 효과크기를 계산할 수 있다. 셋째, 대부분의 방법은 정적 기초선 경향을 통제할 수 없지만, 확장된 변속선과 Tau-U는 기초선 경향을 통제한다. 넷째, 확장된 변속선과 중앙치 초과 자료 백분율은 자료가 집중경향을 보이는 경우에 적절하다. 그러나 자료의 형태가 쌍봉, 극단적 편포, 집중경향치의 부족, 중앙치에 의존하는 자료는 결과를 왜곡할 수 있다. 다른 비중복 분석방법은 집중 경향치에 의존하지 않기 때문에 보다 유연하게 적용할 수 있다. 다섯째, 확장된 변속선과 중앙치 초과 자료 백분율은 통계적 검증력이 매우 낮고, 다음으로 통계적 검증력이 높은 것이 향상율 차이이다. 모든 쌍의 비중복과 Tau-U는 통계적 검증력이 매우 높다. 여섯째, 확장된 변속선은 중앙치 경향 초과 자료 백분율과 동일하고, 중앙치 초과 자료 백분율은 확장된 변속선에 포함된다. 그리고 2×2 분할표에서 계산이 되는 전체 비중복 자료 백분율, 향상율 차이는 매우 유사하다는 것이다.

Abstract

The purpose of this study is to briefly introduce the results analysis method of a single-subject study and to focus on the type and concept of effect size calculation. There are visual analysis methods, non-overlapping analysis methods, and statistical analysis methods for the analysis of a single subject research. This study investigated 9 methods which are relatively frequently mentioned among non-overlapping methods. After reviewing these methods, the following conclusions were obtained. First, the non-overlapping analysis method that is frequently used in a single-subject research is the PND. Second, NAP and Tau-U must go through a relatively complicated statistical procedure, but the remaining methods can calculate the effect size relatively simply. Third, most methods can not control the positive baseline trend, but ECL and Tau-U control baseline trends. Fourth, ECL and PEM are appropriate when data tend to be concentrated. However, data types that are bimodal, extreme bias, lack of central tendency, and median dependent data may distort the results. Other non-overlap methods can be applied more flexibly since they do not depend on the central tendency. Fifth, ECL and PEM are very low statistical power, followed by IRD. NAP and Tau-U has highly statistical power. Sixth, ECL is equal to PEM-T, and PEM is included in ECL. In addition, PAND and IRD calculated in the 2×2 partition table are very similar. Furthermore, NAP and Tau-U are similar in terms of calculating in the same way.

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