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Vol.35 No.1

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탐색적 요인분석: 어떻게 달라지나?

Exploratory Factor Analysis: How has it Changed?

한국심리학회지: 일반 / Korean Journal of Psychology: General, (P)1229-067X; (E)2734-1127
2016, v.35 no.1, pp.217-255
https://doi.org/10.22257/kjp.2016.03.35.1.217
이순묵 (성균관대학교)
윤창영 (대구대학교)
이민형 (성균관대학교)
정선호 (경희대학교)
이순묵, 윤창영, 이민형, & 정선호. (2016). 탐색적 요인분석: 어떻게 달라지나?. 한국심리학회지: 일반, 35(1), 217-255, https://doi.org/10.22257/kjp.2016.03.35.1.217

초록

본 연구에서는 21세기 전후하여 새롭게 제안된 탐색적 요인분석(EFA)에 대한 지침들을 정리하고 실제 자료의 분석 예를 제시하였다. 대략적인 요인수효를 결정하기 위한 발견법(heuristics)의 내용가운데 평행성 분석에 대한 평가가 정리되었고 랜덤자료에서의 고유치로서 기존의 Horn(1965) 방식이나 주축요인방식이 아닌, 최소계수요인 방식(MRFA: minimum rank factor analysis)에서의 고유치가 권고된다. 요인수효 결정을 위한 추론적 접근에서 합치도의 참조는 카이제곱 검증뿐만 아니라 표본 크기에 영향을 덜 받는 다양한 판단적 합치도(예: CFI, RMSEA 등)를 함께 참조할 수 있고 이로 인해 요인수효 결정에서 “다양한 정보의 종합적 사용”이 가능해졌다. 여기에 서열자료 분석에 사용될 수 있는 추정법이 개발되면서, 문항점수들을 연속변수에 준하는 것으로 보고 피어슨상관을 구하여 고전적 요인분석을 하는 관행을 벗어나 문항의 범주별 반응형태를 반영하는 문항요인분석이 현실화되었다. 요인구조의 회전에 있어서는 사각구조의 추정이 용이해졌고, 임의적인 파라메터의 설정 없이 복잡도 함수만을 최소화함으로써 단순구조를 추구할 수 있게 되었다. 또한 탐색과정에서 연구자의 내용적 판단을 반영하는 목표행렬을 주고 그 방향을 따르도록 회전하는 부분제약 목표회전의 사용이 가능해져 이전의 기계적인 회전을 벗어나게 되었다. 요인구조의 해석 가능성에서 가장 큰 변화로 볼 수 있는 것은 측정오차 간 상관을 허용하는 탐색적 구조방정식 모형(ESEM: Exploratory Structural Equation Modeling)이 개발되어, EFA를 할 때 측정오차 간 상관이 없다는 종래의 강한 가정을 완화시키면서 현실적이고 해석 가능한 구조를 산출하게 되었다. 실제 자료의 분석 예시에서는 탐색적 요인분석에서 새로운 지침들이 어떻게 활용되고 있는지를 상세히 설명하고 있다.

keywords
탐색적 요인분석, 공통요인분석, 탐색적 회전, 목표회전, 탐색적 구조방정식 모형, exploratory factor analysis, common factor analysis, exploratory rotation, target rotation, exploratory sturctural equation modeling

Abstract

In the present study new developments in EFA(Exploratory Factor Analysis) that have occurred at the turn of the 21th century are discussed. New guidelines and an analysis of real data following the guidelines are given with practical comments. First, in a process of determining the number of factors, MRFA (minimum rank factor analysis) is recommended as the best method of Parallel Analysis (PA) instead of Horn's method (1965) and PA-PAFA (parallel analysis in principal axis factor analysis). Various fit indices such as CFI, RMSEA, and etc. allow us to consider “various psychometric criteria” before determining the number of factors as the indices are less sensitive to sample sizes than the conventional statistic. In addition estimation methods that are applicable to categorical data (dichotomous or polytomous) have been developed so that item factor analysis can be readily performed for categorical data. Second, in a process of rotating factor structures, “simple” oblique structure can be easily computed just by minimzing the value of complexity function, and a “partially specified target” rotaion is also available adopting a target matrix whose elements are partially hypothesized by a researcher. Finally, in a process of interpreting factor structures, ESEM (Exploratory Structural Equation Modeling) will get prevalence in the near future as it allows us to free correlations between unique factors (measurement errors) and can produce more practical and interpretable factor stucutures. New guidelines on EFA are described in detail in the later part of this paper.

keywords
탐색적 요인분석, 공통요인분석, 탐색적 회전, 목표회전, 탐색적 구조방정식 모형, exploratory factor analysis, common factor analysis, exploratory rotation, target rotation, exploratory sturctural equation modeling

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