• Title, Summary, Keyword: Exploratory 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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Exploratory Factor Analysis of SME Internationalization: Factor Differences between AEO and Non-AEO Authorized Companies

  • Son, Sung-Kyun;Kim, Tae-Joong;Kim, So-Hyung
    • Journal of Distribution Science
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    • v.12 no.7
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    • pp.5-12
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    • 2014
  • Purpose - This study identified internationalization factors forKorean SMEs and explored factor differences between AEO and non-AEO authorized companies. Research design, data, and methodology - The study was designed to assess internationalization factors for AEO authorization in Korea through a questionnaire survey and an empirical analysis. The questionnaires were conducted for AEO and Non-AEO authorized companies that were undergoing AEO authorization. The study was conducted through e-mail and AEO manager education classes. Ninety-five questionnaires were collected. We employed the exploratory factor analysis methodology to derive internationalization factors for KoreanSMEs, and explored the factor differences between AEO and Non-AEO authorized companies. Results - AEO authorized companies outperformed Non-AEO authorized companies in R&D and technology. This indicated that AEO authorized companies were recognized as reliable and safe companies by the Korea Customs Service and other Customs services in trade facilitation and customs clearance processes. Conclusions - This study has some implications for AEO authorization and internationalization processes, and involved the empirical analysis of SMEs and the exploratory factor analysis in the internationalization process.

Exploratory and Confirmatory Factor Analysis of the Korean version of the Penn State Worry Questionnaire (한글판 펜실베니아 걱정 질문지의 탐색적 및 확인적 요인 분석)

  • Jeon, Jun Won;Kim, Daeho;Kim, Eunkyung;Roh, Sungwon
    • 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.

Identifying Variables that Affect Learners' Preference Toward E-Learning Program (e-러닝 프로그램 선호 영향변인에 관한 탐색적 요인분석)

  • Lee, Youngmin
    • The Journal of Korean Association of Computer Education
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    • v.9 no.3
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    • pp.67-74
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    • 2006
  • The purpose of this study is identifying variables that affect to learners' preference toward specific e-learning programs, using an exploratory factor analysis(EFA) method. We extract common factors that explain the correlations among variables. In the result, 8 factors were identified as main influential factors: e-learning program design(1st factor), the purpose of e-learning use(2nd factor), social and cultural issues(3rd factor), demographics(4th factor), organizational needs(5th factor), impacts of e-learning(6th factor), e-learning management(7th factor), and technical issue(8th factor).

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A Study on Road Characteristic Classification using Exploratory Factor Analysis (탐색적 요인분석을 이용한 도로특성분류에 관한 연구)

  • Cho, Jun-Han;Kim, Seong-Ho;Rho, Jeong-Hyun
    • Journal of Korean Society of Transportation
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    • v.26 no.3
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    • pp.53-66
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    • 2008
  • This research is to the establishment of a conceptual framework that supports road characteristic classification from a new point of view in order to complement of the existing road functional classification and examine of traffic pattern. The road characteristic classification(RCC) is expected to use important performance criteria that produced a policy guidelines for transportation planning and operational management. For this study, the traffic data used the permanent traffic counters(PTCs) located within the national highway between 2002 and 2006. The research has described for a systematic review and assessment of how exploratory factor analysis should be applied from 12 explanatory variables. The optimal number of components and clusters are determined by interpretation of the factor analysis results. As a result, the scenario including all 12 explanatory variables is better than other scenarios. The four components is produced the optimal number of factors. This research made contributions to the understanding of the exploratory factor analysis for the road characteristic classification, further applying the objective input data for various analysis method, such as cluster analysis, regression analysis and discriminant analysis.

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.

Development and Validation of TPACK Measurement Tool for Mathematics Teachers (수학교사의 테크놀로지 교수 내용 지식(TPACK) 측정 도구 개발 및 타당화)

  • Lee, Da-Hee;Whang, Woo-Hyun
    • The Mathematical Education
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    • v.56 no.4
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    • pp.407-434
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    • 2017
  • The purpose of this study is to develop and verify the TPACK measurement tool for middle and high school mathematics teachers in the Korean context. Also, by clarifying the relationship between subordinate factors of Mathematics teachers' TPACK, an attempt was made to provide suggestions on the designs and directions for the in-service and pre-service teacher education and the programs for improving mathematics teachers' TPACK in the future. In order to achieve this goal, TPACK factors of mathematics teachers were extracted by reviewing literature on PCK, MKT, and TPACK. Then, content validity, basic statistical survey, reliability verification, exploratory factor analysis, confirmatory factor analysis, and structural equation model verification were conducted sequentially. At first, preliminary analysis was carried out on 79 mathematics teachers, and 76 items excluding the items with extreme value and reliability were included in the basic statistical analysis. And secondly, an exploratory factor analysis was conducted on 376 mathematics teachers, and this instrument consisted of 7 subordinate factors(CK, PK, TK, PCK, TCK, TPK, TPACK) and 61 items. Also by conducting confirmatory factor analysis and structural equation model test with 254 mathematics teachers, the measurement tool was confirmed the validity and reliability through statistically significant analysis. Then, the importance of integrated knowledge was confirmed by looking at the relationship between the TPACK factors of in-service mathematics teachers. The integrated knowledge(PCK, TCK, TPK) has played a crucial role in the formation of TPACK rather than the knowledge of CK, PK, and TK alone. Finally, the validity of TCK was confirmed through the structural equation modeling of TPACK. TCK not only directly affected TPACK, but also indirectly through TPK. According to these affirmative results, this measurement tool is claimed to be suitable for measuring the factors of Mathematics teachers' TPACK, and also the structural equation model can be regarded as a suitable model for analyzing the structural relationship of mathematics teachers' TPACK.

A Study on Factors Affecting the Use of Ambulatory Physician Services (의사방문수 결정요인 분석)

  • 박현애;송건용
    • Health Policy and Management
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    • v.4 no.2
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    • pp.58-76
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    • 1994
  • In order to study factors affecting the use of the ambulatory physician services. Andersen's model for health utilization was modified by adding the health behavior component and examined with three different approaches. Three different approaches were the multiople regression model, logistic regression model, and LISREL model. For multiple regression, dependent variable was reported illness-related visits to a physician during past one year and independent variables are variaous variables measuring predisposing factor, enabling factor, need factor and health behavior. For the logistic regression, dependent variable was visit or no-visit to a physician during past one year and independent variables were same as the multiple regression analysis. For the LISREL, five endogenous variables of health utiliztion, predisposing factor, enabling factor, need factor, and health behavior and 20 exogeneous variables which measures five endogenous variables were used. According to the multiple regression analysis, chronic illness, health status, perceived health status of the need factor; residence, sex, age, marital status, education of the predisposing factor ; health insurance, usual source for medical care of enabling factor were the siginificant exploratory variables for the health utilization. Out of the logistic regression analysis, health status, chronic illness, residence, marital status, education, drinking, use of health aid were found to be significant exploratory variables. From LISREL, need factor affect utilization most following by predisposing factor, enabling factor and health behavior. For LISREL model, age, education, and residence for predisposing factor; health status, chronic illess, and perceived health status for need factor; medical insurance for enabling factor; and doing any kind of health behavior for the health behavior were found as the significant observed variables for each theoretical variables.

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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.

Exploratory & Confirmatory Factor Analysis for Developing a Good Secondary School Scale based on the Factors of the Effective Schooling (효과적인 학교교육요소에 근거한 좋은 중등학교 척도개발을 위한 탐색적 확인적 요인분석)

  • Jung, Soon-Mo;Baek, Hyeon-Gi
    • Journal of Digital Convergence
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    • v.6 no.2
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    • pp.41-53
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    • 2008
  • This research is to redefine the concept of Good School and to validate an effective Good Secondary School Scale in Kyung-gi Province and Seoul. As statistical methods, SPSS 13.0 and AMOS 5.0 were used. Item Analysis and Exploratory Factor Analysis(EFA) were conducted to test the reliability of items and the factor structure. And Confirmatory Factor Analysis(CFA) was conducted to test the validity and fitness of the Good School Scale. The outcomes are as follows: First, six factors(school environment, curriculum, teacher, school-based management system, director) will increase the good schooling effectiveness. Second, As a result of Confirmatory Factor Analysis(CFA), the goodness of fit indices(GFI AGFI, CFI, RMSEA) demonstrate statistically significance and fitness of the model. The final Good School Scale supports 6 Good School Factors obtained in main test. Therefore, we can say that this scale can be used as a valid instrument to measure a real Good Schooling Effectiveness at the secondary school situation in Korea.

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