• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.933-942
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• 2013

Common factor analysis and principal component analysis represent two technically distinctive approaches to exploratory factor analysis. Much of the psychometric literature recommends the use of common factor analysis instead of principal component analysis. Nonetheless, factor analysts use principal component analysis more frequently because they believe that principal component analysis could yield (relatively) less accurate estimates of factor loadings compared to common factor analysis but most often produce similar pattern of factor loadings, leading to essentially the same factor interpretations. A simulation study is conducted to evaluate the relative performance of these two approaches in terms of factor pattern recovery under different experimental conditions of sample size, overdetermination, and communality.The results show that principal component analysis performs better in factor recovery with small sample sizes (below 200). It was further shown that this tendency is more prominent when there are a small number of variables per factor. The present results are of practical use for factor analysts in the field of marketing and the social sciences.

• Journal of The Korean Association For Science Education
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• v.17 no.4
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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.

• Journal of the Korean Society of Food Culture
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• v.31 no.2
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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.

• Culinary science and hospitality research
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• v.13 no.1 s.32
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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.

• Journal of Distribution Science
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• v.13 no.9
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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.

• Korean Journal of Human Ecology
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• v.17 no.6
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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).

• The Research Journal of the Costume Culture
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• v.22 no.2
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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.

• Anxiety and mood
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• v.13 no.2
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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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.3
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• pp.654-659
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• 2007

This study suggests a measure method for measuring variables that are used for hotel corporations' CRM performance. For this purpose, I present a combined method between factor analysis and AHP(Analytic Hierarchy Process) analysis. Factor analysis gives us a result that shows a group of highly correlated variables and another group of less correlated variables. Thus, factor analysis can only give information of factor categorization. Although researchers add ANOVA analysis or regression analysis, these efforts can not connect its results with factor analysis. Therefore, In hotel CRM performance analysis, calculation of each factor's importance is strongly required. For that reason, I suggest a method that combines AHP analysis with factor analysis for Hotel CRM performance measurement.

• Journal of the Korean Data and Information Science Society
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• v.19 no.2
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• pp.451-461
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• 2008

In this study, factor and cluster analysis have been conducted to group the differences in Korean view on national economic policy in the sample of the 2006 Korean General Social Survey (KGSS). According to the 2006 KGSS, the 6 items with a 5-point Likert scale include the questions about whether or the extent to which each respondent supports the specific types of governmental economic policy. In our study, at first, the factor analysis has converted the original 6 items into the 3 composite variables that account for 81% in the total variability. As the second step of factor analysis, factor scores have been computed. Then, the K-means cluster analysis based on the factor scores has been conducted to group the survey respondents into the 3 clusters. In particular, the cross-tabulation analysis has shown that the distribution of the 3 clusters varies with the respondents' socio-demographic characteristics.