• Title/Summary/Keyword: factor analysis

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A Study of Validity in Tripartite Model of "Attitudes towards Science" using Exploratory and Confirmatory Factor Analyses (탐색적 확인적 요인 분석을 통한 "과학에 대한 태도" 3요소 모델의 타당도 연구)

  • Lee, Kyung-Hoon
    • 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.

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Resistant Principal Factor Analysis

  • Park, Youg-Seok;Byun, Ho-Seon
    • Journal of the Korean Statistical Society
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    • v.25 no.1
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    • pp.67-80
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    • 1996
  • Factor analysis is a multivariate technique for describing the in-terrelationship among many variables in terms of a few underlying but unobservable random variables called factors. There are various approaches for this factor analysis. In particular, principal factor analysis is one of the most popular methods. This follows the mathematical algorithm of the principal component analysis based on the singular value decomposition. But it is known that the singular value decomposition is not resistant, i.e., it is very sensitive to small changes in the input data. In this article, using the resistant singular value decomposition of Choi and Huh (1994), we derive a resistant principal factor analysis relatively little influenced by notable observations.

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An Analytic Method for CRM Performance's Measurement Factors of Hotel Management (호텔기업의 CRM 운용성과 측정요인의 분석 방법)

  • Oh, Sang-Young
    • 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.

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Factor Analysis for Exploratory Research in the Distribution Science Field (유통과학분야에서 탐색적 연구를 위한 요인분석)

  • Yim, Myung-Seong
    • 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.

Minimax Eccentricity Estimation for Multiple Set Factor Analysis

  • Hyuncheol Kang;Kim, Keeyoung
    • Journal of the Korean Statistical Society
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    • v.31 no.2
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    • pp.163-175
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    • 2002
  • An extended version of the minimax eccentricity factor estimation for multiple set case is proposed. In addition, two more simple methods for multiple set factor analysis exploiting the concept of generalized canonical correlation analysis is suggested. Finally, a certain connection between the generalized canonical correlation analysis and the multiple set factor analysis is derived which helps us clarify the relationship.

A Study on the Factor Analysis of the Encounter Data in the Maritime Traffic Environment (해상교통 조우데이터 요인분석에 관한 연구)

  • Kim, Kwang-Il;Jeong, Jung Sik;Park, Gyei-Kark
    • Journal of the Korean Institute of Intelligent Systems
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    • v.25 no.3
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    • pp.293-298
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    • 2015
  • The vessel encounter data collected from the vessel trajectories in the maritime traffic situation is possible to analyze vessel collision and near-collision risk using statistical method. In this study, analyzing variables extracted from the vessel encounter data using factor analysis, we determine main factors effecting vessel collision risk from vessel encounter data. In order to calculate each factor, it used principal component analysis for factor analysis after normalization and standardization of vessel encounter variables. As a result of the factor analysis, main effect factors are summarized into the vessel approach factor and collision avoidance variance factor.

A Guide on the Use of Factor Analysis in the Assessment of Construct Validity (구성타당도 평가에 있어서 요인분석의 활용)

  • Kang, Hyuncheol
    • Journal of Korean Academy of Nursing
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    • v.43 no.5
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    • pp.587-594
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    • 2013
  • Purpose: The purpose of this study is to provide researchers with a simplified approach to undertaking exploratory factor analysis for the assessment of construct validity. Methods: All articles published in 2010, 2011, and 2012 in Journal of Korean Academy of Nursing were reviewed and other relevant books and articles were chosen for the review. Results: In this paper, the following were discussed: preliminary analysis process of exploratory factor analysis to examine the sample size, distribution of measured variables, correlation coefficient, and results of KMO measure and Bartlett's test of sphericity. In addition, other areas to be considered in using factor analysis are discussed, including determination of the number of factors, the choice of rotation method or extraction method of the factor structure, and the interpretation of the factor loadings and explained variance. Conclusion: Content validity is the degree to which elements of an assessment instrument are relevant to and representative of the targeted construct for a particular assessment purpose. This measurement is difficult and challenging and takes a lot of time. Factor analysis is considered one of the strongest approaches to establishing construct validity and is the most commonly used method for establishing construct validity measured by an instrument.

Combination of Research Methods for Factor Analysis (결합적 요인분석 연구방법)

  • Oh, Sang-Young;Hong, Hyun-Gi
    • The Journal of the Korea Contents Association
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    • v.7 no.10
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    • pp.202-210
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    • 2007
  • Factor analysis, an important research method, has been conducted as a pre-analysis of empirical researches. Sometimes, the researchers expect more information than factor analysis' limitation as well. Therefore, I suggest a method to overcome this problem. This study presents a combined research method which allow to solve the limitation of the factor analysis in social science. The combined research method consist of factor analysis, AHP(analytic hierarchy process) analysis, diffusion theory and system dynamics, which is able to explain causalities.

Comparison of Dimension Reduction Methods for Time Series Factor Analysis: A Case Study (Value at Risk의 사후검증을 통한 다변량 시계열자료의 차원축소 방법의 비교: 사례분석)

  • Lee, Dae-Su;Song, Seong-Joo
    • The Korean Journal of Applied Statistics
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    • v.24 no.4
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    • pp.597-607
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    • 2011
  • Value at Risk(VaR) is being widely used as a simple tool for measuring financial risk. Although VaR has a few weak points, it is used as a basic risk measure due to its simplicity and easiness of understanding. However, it becomes very difficult to estimate the volatility of the portfolio (essential to compute its VaR) when the number of assets in the portfolio is large. In this case, we can consider the application of a dimension reduction technique; however, the ordinary factor analysis cannot be applied directly to financial data due to autocorrelation. In this paper, we suggest a dimension reduction method that uses the time-series factor analysis and DCC(Dynamic Conditional Correlation) GARCH model. We also compare the method using time-series factor analysis with the existing method using ordinary factor analysis by backtesting the VaR of real data from the Korean stock market.