• 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 Data and Information Science Society
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• v.19 no.3
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• pp.751-759
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• 2008

Mixtures of factor analyzers(MFA) is useful to model the distribution of high-dimensional data on much lower dimensional space where the number of observations is very large relative to their dimension. Mixtures of common factor analyzers(MCFA) can reduce further the number of parameters in the specification of the component covariance matrices as the number of classes is not small. Moreover, the factor scores of MCFA can be displayed in low-dimensional space to distinguish the groups. We propose the factor scores of MCFA as new low-dimensional features for classification of high-dimensional data. Compared with the conventional dimension reduction methods such as principal component analysis(PCA) and canonical covariates(CV), the proposed factor score was shown to have higher correct classification rates for three real data sets when it was used in parametric and nonparametric classifiers.

• IE interfaces
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• v.25 no.4
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• pp.382-392
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• 2012

Reactor protection system to keep nuclear safety and operational economy of plants requires high reliability. Such a high reliability of the system can be achieved through the redundant design of components. However, common cause failures of components reduce the benefits of redundant design. Thus, the common cause failure analysis, to accurately calculate the reliability of the reactor protection system, is carried out using alpha-factor model. Analysis results to 24 operating months are that 1) the system reliability satisfies the reliability goal of EPRI-URD and 2) the common cause failure contributes 90% of the system unreliability. The uncertainty analysis using alpha factor parameters of 0.05 and 0.95 quantile values shows significantly large difference in the system unreliability.

• 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.

• Communications for Statistical Applications and Methods
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• v.22 no.5
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• pp.415-434
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• 2015

In this paper we overview the literature on common features analysis of economic time series. Starting from the seminal contributions by Engle and Kozicki (1993) and Vahid and Engle (1993), we present and discuss the various notions that have been proposed to detect and model common cyclical features in macroeconometrics. In particular, we analyze in details the link between common cyclical features and the reduced-rank regression model. We also illustrate similarities and differences between the common features methodology and other popular types of multivariate time series modelling. Finally, we discuss some recent developments in this area, such as the implications of common features for univariate time series models and the analysis of common autocorrelation in medium-large dimensional systems.

• The Journal of Fisheries Business Administration
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• v.51 no.2
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• pp.107-121
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• 2020

It is important to understand the factors in which fishing village experiences have on family relationships. The purpose of this study is to derive the variables related to the family stemmed from the analysis of previous studies, and to find and present common factors that specifically influence the fishing experience in the family relationship. Through this, we intend to find it out in addition to the visible results such as income and experience, it can be an effective policy as a means to improve family relations through mutual efforts and understanding among families in promoting fishing-related policies. As a result of the analysis of the study, two common factors that the fishing experience had on family relationships were extracted. The first common factor is 'mutual effort,' which results in trying for each other's emotions, communication, understanding, and unity. The second common factor is 'mutual sharing' so fishing experiences are generally related to each other in family relationships. It can be seen as a result of sharing the memories, pleasures, and bonds of the people.

• Fashion & Textile Research Journal
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• v.11 no.2
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• pp.210-218
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• 2009

The Purpose of this study is to demonstrate Storytelling as an effective device for Fashion Design by establishing relationship between Fashion Design Process and Storytelling. Through researching a social background and a concept of storytelling, found that story used interactively is a powerful tool for attention, understanding and change in both individuals and communities. Analysed the elements and the structure of storytelling and Fashion Design Process, by researching preceding researches. Therefore, we proposed a new four elements -text factor, visual factor, audio factor and virtual factor- and four steps (1)exploring stories, (2)planning a story, (3)building the story, (4)do storytelling- of storytelling and four steps-(1)gathering and analysing informations, (2)building a concept, (3)planning and developing a design, (4)do evaluation and make decision- of fashion design process. Through comparative analysis, we found a closeness between two structures, a use of common factors and also found characteristics to be considered in each stage. In the first stage, we found text, visual and audio factor as common factors. In the second stage, we suggested text and visual factor as common factors and also suggested clarity, realism and probability as characteristics. In the third stage, we found text, visual and virtual factor and also found dynamism, immersion and continuity. In the last stage, we suggested text, visual, virtual and audio factor and also suggested presence and interactivity as characteristics.

• Korean Journal of Child Studies
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• v.4
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• pp.37-51
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• 1983

The purpose of this study was the investigation of theoretical and empirical relationships between Piagetian and psychometric assessments of intelligence. Specifically, the factor structure of Piagetian-type scales, the relationship between Piagetian scales and psychometric intelligence tests, and differences in the factor structure of Piagetian and psychometric assessments of intelligence were studied. The subjects of this stuby were 70 children (35 boys and 35 girls) in the 1st grade of an elementary school in Seoul The Piagetian-type scales and the K-WISC were administered individually, and the General Intelligence Test was administered to groups of children. Statistical analysis of the obtained data consisted of the SPSS Computer program including factor analysis and Pearson's product moment correlation coefficient. The Piagetian-type scales were found to consist of three factors, which accounted for 55 percent of the total common-factor variance. Factor-I was a factor indicating "conservation". Factor-II was a factor indicating "moral judgements". Factor-III was a factor indicating "classification and identity". Correlations between subtests of psychometric tests and Piagetian scales were relatively low or moderate. Relations between IQs assessed by the psychometric tests and Piagetian scales were also relativeyly low or moderate. Eight factors were extracted from the joint factor analysis of psychometric intelligence tests and Piagetian scales, and they accounted for 67 percent of the total common-factor variance. Factors-I, II, III, and V consisted of subtests of psychometric assessments, and Factors-IV, VI, VII and VIII were composed of Piagetian scales. Factor-I was a factor for "reasoning ability based upon language". Factor-II was a factor for "performance ability". Factor-III was a factor for "grouping ability". Factor-IV was a factor for "conservation". Factor-V was a factor indicating "symbol and language usage ability". Factor- VI was a factor indicating "moral judgments". Factor-VII was a factor indicating "length consevation". Factor-VIII was a factor indicating "classification and identity".

• Transactions of Materials Processing
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• v.19 no.2
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• pp.88-94
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• 2010

The next generation of diesel engine can operate at high injection pressure up to 1,800bar. The common rail pipe must have higher internal strength because it is directly influenced by the high-pressure fuel. Folding defects in the Common rail pipe can not ensure the structural safety. Therefore, Preform design and fatigue-life analysis are very important for preventing the head of the common rail pipe from folding in the heading process and for predicting fatigue life according to the amount of folding. In this study, a closed form equation to predict fatigue life was suggested by Goodman theory and pressure vessels theory in ASME Code in order to develop an optimization technique of the heading process and verified its reliability through fatigue-structural coupled field analysis. The results calculated by the theory were in good agreement with those obtained by the finite element analysis.

• The Korean Journal of Applied Statistics
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• v.33 no.4
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• pp.365-377
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• 2020

We discuss latent factor regression when constructing a common structure inherent among explanatory variables to solve multicollinearity and use them as regressors to construct a linear model of a response variable. Bayesian estimation with LASSO prior of a large penalty parameter to construct a significant factor loading matrix of intrinsic interests among infinite latent structures. The estimated factor loading matrix with estimated other parameters can be inversely transformed into linear parameters of each explanatory variable and used as prediction models for new observations. We apply the proposed method to Product Service Management data of HBAT and observe that the proposed method constructs the same factors of general common factor analysis for the fixed number of factors. The calculated MSE of predicted values of Bayesian latent factor regression model is also smaller than the common factor regression model.