무선재추출법에 기초한 사건관련전위 자료분석에 대한 탐색적 고찰
An Exploratory Observation of Analyzing Event-Related Potential Data on the Basis of Random-Resampling Method
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가설검증 과정에서 자료 분석 결과 산출된 통계치에 대한 해석은 몇 가지 통계학적 이론을 토대로 분석 결과 산출된 관련 통계치의 이론적 확률 분포에 의해 좌우된다. 예를 들어 실험 조건 간 측정치의 평균 차이에 대한 통계적 유의미성은 대개 전집 특성에 대한 몇 가지 이론적 가정에 기초해 구성된 해당 평균 차이값의 확률 분포(예: Student's t)에 기초해 결정된다. 본 연구는 이러한 이론적 통계치의 분포가 아닌 실측정 자료의 무선 재구성을 통해 얻어진 경험적 통계치의 분포에 기초해 가설 검증을 시도하는 무선재추출법의 기본 논리와 장점을 살펴보고 사건관련전위 분석 상황에서의 응용 가능성을 모색하였다. 더 나아가 무선 추출 원리에 기초한 무선치환법이 적용된 구체적 사례를 소개하고 ERP 자료 분석에 있어서 경험적 통계 분석 적용에 앞서 유의할 점을 살펴봄으로써 뇌파 연구자들의 무선재추출법에 대한 정확한 이해를 도모하였다.
Abstract
In hypothesis testing, the interpretation of a statistic obtained from the data analysis relies on a probabilistic distribution of the statistic constructed according to several statistical theories. For instance, the statistical significance of a mean difference between experimental conditions is determined according to a probabilistic distribution of the mean differences (e.g., Student's t) constructed under several theoretical assumptions for population characteristics. The present study explored the logic and advantages of random-resampling approach for analyzing event-related potentials (ERPs) where a hypothesis is tested according to the distribution of empirical statistics that is constructed based on randomly resampled dataset of real measures rather than a theoretical distribution of the statistics. To motivate ERP researchers' understanding of the random-resampling approach, the present study further introduced a specific example of data analyses where a random-permutation procedure was applied according to the random-resampling principle, as well as discussing several cautions ahead of its practical application to ERP data analyses.
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