• 응용통계연구
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• 제26권6호
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• pp.933-942
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• 2013

본 연구에서는 시뮬레이션 방법을 사용해서 다양한 조건에서 주성분분석이 얼마나 잘 요인 구조를 복원할 수 있는지를 공통요인분석과 비교하여 체계적으로 평가하였다. 이 연구에서 요인 대 변수 비율, 공통성, 그리고 표본크기를 실험변수로 설정하였다. 주성분분석은 표본의 크기가 200개 이하인 경우 공통적으로 공통요인분석에 비해 더 우수한 요인구조의 복원력을 보여주었다. 특히, 요인 당 변수 수가 적은 경우, 주성분분석은 50개의 표본에서도 만족할 만한 수준의 요인복원능력을 보여주었다. 이와 더불어 공통성 수준 또한 낮은 경우 필요한 표본수는 100개로 늘어난다. 본 연구결과는 요인추출방법으로서 주성분분석의 선택의 근거를 제시하고 타당한 사용에 관한 가이드라인을 제시해 준다.

• 한국과학교육학회지
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• 제17권4호
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• pp.481-492
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• 1997

The purpose of this study is to construct validity of Tripartite model of "Attitudes towards Science" using Exploratory and Confirmatory Factor Analyses. Exploratory and confirmatory factor analyses are two major approaches to factor analysis. The primary goal of factor analysis is to explain the covariances or correlations between many observed variables by means of relatively few underlying latent variables. In exploratory factor analysis, the number of latent variables is not determined before the analysis, all latent variables typically influence all observed variables, the measurement errors(${\delta}$) are not allowed to correlate, and unidentification of parameters is common. Confirmatory factor analysis requires a detailed and identified initial model. Confirmatory factor analysis techniques allow relations between latent and observed variables that are not possible with traditional, exploratory factor analysis techniques. As a result of exploratory factor analysis, tripartite model of "Attitudes towards Science" being composed of affection, behavioral intention and cognition is empirically identified. But attitude of science career being composed of affection and behavioral intention is identified. In validity test using confirmatory factor analysis, measurement structure of Tripartite model of "Attitudes towards Science" is not correspondent to data set. Because it is concluded that the object of attitudes are not specific.

• 한국산학기술학회논문지
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• 제8권3호
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• pp.654-659
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• 2007

본 논문은 호텔기업이 많이 활용하고 있는 CRM의 운용성과를 측정하기 위한 측정 요인에 대한 연구를 하였다. 이를 위해 통계적인 방법인 요인분석(Factor Analysis)과 AHP(Analytic Hierarchy Process)분석 기법을 연계하여 분석하는 방법을 연구하였다. 요인분석은 서로 상관있는 변수들만을 그룹화하고, 상관도가 낮은 변수는 또 다른 그룹으로 묶는 결과를 도출한다. 그러나 요인분석은 요인의 분류 외에는 주요 정보를 제공하지 못한다. 그렇기 때문에 이를 극복하기 위해서 분산분석, 회귀분석 등 다른 통계분석 방법을 시도한다. 그러나 이러한 분석은 요인분석 결과와 연계되는 것이 아니라 독립적인 분석을 하게 되는 것이다. 그렇지만 호텔기업의 CRM 운용성과 분석에서는 요인의 중요도 분석이 중요하다. 따라서 AHP 분석기법을 연계하여 호텔기업의 CRM 운용 성과를 측정하기 위한 요인 분석방법에 대해 고찰하였다.

• 한국조리학회지
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• 제13권1호
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• pp.166-178
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• 2007

A review of literature relating to the research topic and a survey method have been implemented in order to analyze effects of service orientation. For data analysis, a reliability analysis was performed to test the reliability of the construct and a series of an exploratory factor analysis was used for the validity test. The findings of the study were as follows: Classified into sex, service leadership factor and service skill factor showed meaningful difference between groups. Classified with age, service training factor, service leadership factor, service standardization factor, service technology factor, and service compensation factor showed meaningful difference between groups. Classified with scholarship, service compensation factor showed meaningful difference. Classified into working year, employees' discretion factor showed meaningful difference. Classified into work department, service training factor and employees' right factor showed meaningful difference. In addition, classified into monthly average incomes, employees' discretion factor showed meaningful difference.

• 유통과학연구
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• 제13권9호
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• pp.103-112
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• 2015

Purpose - This paper aims to provide a step-by-step approach to factor analytic procedures, such as principal component analysis (PCA) and exploratory factor analysis (EFA), and to offer a guideline for factor analysis. Authors have argued that the results of PCA and EFA are substantially similar. Additionally, they assert that PCA is a more appropriate technique for factor analysis because PCA produces easily interpreted results that are likely to be the basis of better decisions. For these reasons, many researchers have used PCA as a technique instead of EFA. However, these techniques are clearly different. PCA should be used for data reduction. On the other hand, EFA has been tailored to identify any underlying factor structure, a set of measured variables that cause the manifest variables to covary. Thus, it is needed for a guideline and for procedures to use in factor analysis. To date, however, these two techniques have been indiscriminately misused. Research design, data, and methodology - This research conducted a literature review. For this, we summarized the meaningful and consistent arguments and drew up guidelines and suggested procedures for rigorous EFA. Results - PCA can be used instead of common factor analysis when all measured variables have high communality. However, common factor analysis is recommended for EFA. First, researchers should evaluate the sample size and check for sampling adequacy before conducting factor analysis. If these conditions are not satisfied, then the next steps cannot be followed. Sample size must be at least 100 with communality above 0.5 and a minimum subject to item ratio of at least 5:1, with a minimum of five items in EFA. Next, Bartlett's sphericity test and the Kaiser-Mayer-Olkin (KMO) measure should be assessed for sampling adequacy. The chi-square value for Bartlett's test should be significant. In addition, a KMO of more than 0.8 is recommended. The next step is to conduct a factor analysis. The analysis is composed of three stages. The first stage determines a rotation technique. Generally, ML or PAF will suggest to researchers the best results. Selection of one of the two techniques heavily hinges on data normality. ML requires normally distributed data; on the other hand, PAF does not. The second step is associated with determining the number of factors to retain in the EFA. The best way to determine the number of factors to retain is to apply three methods including eigenvalues greater than 1.0, the scree plot test, and the variance extracted. The last step is to select one of two rotation methods: orthogonal or oblique. If the research suggests some variables that are correlated to each other, then the oblique method should be selected for factor rotation because the method assumes all factors are correlated in the research. If not, the orthogonal method is possible for factor rotation. Conclusions - Recommendations are offered for the best factor analytic practice for empirical research.

• 한국식생활문화학회지
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• 제31권2호
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• pp.149-160
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• 2016

This study utilized importance and performance analysis (IPA) in order to improve and plan tangible (menu) and intangible (service) products at dumpling shops. Menu attributes for tangible products were classified into sensory factor, health factor, hygiene factor, and external factor. Attributes for intangible products were classified into response factor, visual factor, spatial factor, package factor, and promotion factor. In IPA analysis of tangible products, sensory factor and hygiene factor were located in Quadrant I (Keep up the good work). Health factor was located in Quadrant III (Low priority for management) and the external factor was located in Quadrant II (Possible overkill). In IPA analysis of intangible products, response factor and visual factor were located in Quadrant I, whereas promotion factor was located in Quadrant III. The attributes related to kindness of staff and space for customers in the store were more important, but due to their low performance level, they were located in Quadrant IV (Concentrate management here). Thus, the product planner should improve attributes of the related product immediately. As a result, the development of competitive products within the market is possible.

• 한국생활과학회지
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• 제17권6호
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• pp.1167-1180
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• 2008

The purpose of this study was to take the gauge of the importance-performance of the clothing store attribute in the department store. The survey was administered to customers of department stores in Deajeon city and frequency analysis, factor analysis, reliability analysis, and importance-performance analysis were used to analyze the data of 37 clothing store attributes. The findings of this study were as bellows: 1. 8 factors were distracted from 37 clothing store attributes by factor analysis (Factor 1: goods, Factor 2; store's facility and environment, Factor 3; salesman and service, Factor 4; brand, Factor 5; price, Factor 6; store's atmosphere, Factor 7; convenience of the transportation and access, Factor 8; promotion and advertisement) 2. as results of importance-performance analysis, 10 attributes were shown in area I (high importance and high performance) which needed a strategy of Keep Up the Good Work, 6 attributes in area II (low importance but high performance) fitted a strategy of Possible Overkill, 12 attributes in area III (high importance but low performance) corresponded to a strategy of Concentrate Here, and finally a strategy of Low Priority was needed to 9 attributes in area IV (low importance and low performance).

• 대한불안의학회지
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• 제13권2호
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• pp.86-92
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• 2017

Objective : This study evaluated the factor structure of a Korean version of the Penn State Worry Questionnaire (K-PSWQ) with exploratory factor analysis in healthy adult subjects, and confirmatory factor analysis of subjects who have received psychiatric treatment. Methods : Exploratory principal component analysis was conducted with data from 318 non-psychiatric subjects, and 118 psychiatric patients were subjected to confirmatory factor analysis (maximum likelihood estimation). Participants were voluntary visitors at the booth who agreed to undergo screening for anxiety disorder at 2013 & 2014 Korea Mental Health Exhibitions. Results : Exploratory analysis revealed a two factor structure of the scale with total variance of 56.3%. Factor 1 was considered 'Worry engagement', and factor 2 was considered 'Absence of worry'. However, the results of the confirmatory factor analysis supported that both one factor model with method factor and two factor model are fit to structure of the scale considering fit indices. Internal consistency of total questions was good (Cronbach's ${\alpha}=0.899$). Conclusion : Our results supported the previously suggested factor structure of the PSWQ, and proved factorial validity of the K-PSWQ in both populations.

• 복식문화연구
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• 제22권2호
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• pp.273-289
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• 2014

The purpose of this study is to identify the determinants of the susceptibility of global consumer culture. As determinants, materialism and self monitoring as psychological variables and fashion clothing product knowledge as clothing-related variable were included. It was hypothesized that both psychological variables and clothing-related variable influence susceptibility of global consumer culture. Data were gathered by surveying university students in Seoul metropolitan area, using convenience sampling, and 311 questionnaires were used in the statistical analysis. In analyzing data, exploratory factor analysis using SPSS and confirmatory factor analysis and path analysis using AMOS were conducted. Factor analysis of susceptibility of global consumer culture revealed four dimensions, 'social prestige' factor, 'quality perception' factor, 'conformity to others' factor, and 'conformity to consumption trend' factor. In addition, factor analysis of self monitoring revealed three dimensions, 'center-oriented attention' factor, 'situation-appropriate self-presentation' factor, and 'strategic displays of self-presentation' factor. The results showed that all the fit indices for the variable measures were quite acceptable. In addition, the overall fit of the model suggests that the model fits the data well. Tests of the hypothesized path show that all variables except for the one factor of self monitoring, 'center-oriented attention', and materialism influence all the factors of susceptibility of global consumer culture. The implications of these findings and suggestions for future study are also discussed.

• 한국지능시스템학회논문지
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• 제25권3호
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• pp.293-298
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• 2015

해상교통상황에서 수집된 선박 조우(Encounter) 데이터 변수는 선박 충돌 및 근접사고(Near-Collision) 위험도를 통계적인 방법에 의한 분석이 가능하다. 본 연구에서는 선박 조우 데이터에서 추출되는 다수의 선박충돌위험도 평가 변수들을 요인분석(Factor Analysis)하여, 선박 조우데이터에서 충돌위험에 영향을 미치는 주요 요인을 결정하고자 한다. 각 요인 결정을 위해 선박조우데이터 변수 정규분포화 및 표준화를 수행한 후 주성분 분석(Principal Component Analysis)으로 요인을 결정하였다. 요인분석결과 선박 근접도 요인과 충돌회피변화요인으로 요약하였다.