유통과학분야에서 탐색적 연구를 위한 요인분석
Factor Analysis for Exploratory Research in the Distribution Science Field
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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 PAFwill 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.
Abstract
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 PAFwill 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.
참고문헌 (34)
- Alii, L. K., "Sample Size and Subject to Item Ratio in Principal Components Analysis and Exploratory Factor Analysis", Journal of Biomerics & Biostatistics, 1(2), pp.1~6 (2010)
- Arrindell, W. A., "An Empirical Test of the Utility of the Observations-to-Variables Ratio in Factor & Components Analysis", Applied Psychological Measurement, 9, pp.165~178 (1985)
- Omid Asgari, "Exploring the Antecedents Affecting Attitude, Satisfaction, and Loyalty towards Korean Cosmetic Brands", 유통과학연구, 13(6), pp.45~70 (2015), 한국유통과학회
- Bartlett, M. S., "Tests of Significance in Factor Analysis", British Journal of Psychology, 3, pp.77~85 (1950)
- Beavers, A. S., "Practical Considerations for Using Exploratory Factor Analysis in Educational Research", Practical Assessment, Research &Evaluation, 18(6), pp.1~13 (2013)
- Bryant, F. B., "Reading and Understanding Multivariate Statistics", pp.99~136 (1997), American Psychological Association
- Budaev, S. V., "Using Principal Components & Factor Analysis in Animal Behaviour Research: Caveats and Guidelines", Ethology : International Journal of Behavioural Biology, 116(5), pp.472~480 (2010)
- 진유, "한국과 중국 20대 여성들의 의복소비가치가 럭셔리 패션 브랜드 선호도와 구매의도에 미치는 영향: 대도시 패션마켓을 중심으로", 유통과학연구, 12(12), pp.107~118 (2014), 한국유통과학회
- Comrey, A. L., "A First Course in Factor Analysis" (1992), Erlbaum
- Conway, J. M., "A Review & Evaluation of Exploratory Factor Analysis Practices in Organizational Research", Organizational Research Methods, 6(2), pp.147~168 (2003)
- Costello, A. B., "Best Practices in Exploratory Factor Analysis : Four Recommendations for Getting the Most from Your Analysis. Practical Assessment", Research & Evaluation, 10(7), pp.1~9 (2005)
- Fabrigar, L. R., "Evaluating Use of Exploratory Factor Analysis in Psychological Research", Psychological Methods, 4(3), pp.272~299 (1999)
- Floyd, F. J., "Factor Analysis in the Development & Refinement of Clinical Assessment Instruments", Psychological Assessment, 7(3), pp.286~299 (1995)
- Ford, J. K., "The Application of Exploratory Factor Analysis in Applied Psychology : A Critical Review & Analysis", Personnel Psychology, 39, pp.291~314 (1986)
- Fouladi, R. T., "Tests of Multivariate Independence : A Critical Analysis of"Monte Carlo Study of Testing the Significance of Correlation Matrices" : Comment", Educational and Psychological Measurement, 53, pp.927~932 (1993)
- Gefen, D., "Practical Guide to Factorial Validity Using PLS-Graph : Tutorial & Annotated Example", Communications of the Association for Information Systems, 16, pp.91~109 (2005)
- Gorsuch, R. L., "Factor Analysis" (1983), Erlbaum
- Guadagnoli, E., "Relation of Sample Size to the Stability of Component Patters", Psychological Bulletin, 103, pp.265~275 (1988)
- Horn, J. L., "A Rationale and Test for the Number of Factors in Factor Analysis", Psychometrika, 30, pp.179~185 (1965)
- Hutcheson, G., "The Multivariate Social Scientist: Introductory Statistics Using Generalized Linear Models" (1999), Sage Publications
- Kass, R. A., "Factor Analysis", Journal of Research, 11, pp.120~138 (1979)
- 김종락, "종합슈퍼마켓(GSM)에서 서비스품질, 보증 및 서비스가치의 영향요인에 관한 연구", 유통과학연구, 13(1), pp.93~103 (2015), 한국유통과학회[DOI]
- Lawley, D. N., "Factor Analysis in a Statistical Method" (1971), Butter-worth
- Ledesma, R. D., "Determining the Number of Factors to Retain in EFA: An Easy-to-Use Computer Program for Carrying Out Parallel Analysis", Practical Assessment, Research & Evaluation, 12(2), pp.1~11 (2007)
- MacCallum, R. C., "Sample Size in Factor Analysis", Psychological Methods, 4(1), pp.84~99 (1999)
- Norušis, M., "Advanced Statistical Procedures Companion" (2005), Prentice Hall
- Nunnally, J. C., "Psychometric Theory" (1978), McGraw Hill
- Osborne, J. W., "Replication Analysis in Exploratory Factor Analysis: What It Is & Why It Makes Your Analysis Better", Practical Assessment, Research & Evaluation, 17(15), pp.1~8 (2012)
- Snook, S. C., "Component Analysis Versus Common Factor Analysis: A Monte Carlo Study", Psychological Bulletin, 106(1), pp.148~154 (1989)
- Streiner, D. L., "Figuring Out Factors : The Use & Misuse of Factor Analysis", Canadian Journal of Psychiatry, 39, pp.135~140 (1994)
- Suhr, D., "Exploratory or Confirmatory Factor Analysis", SAS Users Group International Conference, pp.1~17 (2006), SAS Institute, Inc
- Tinsly, H. E. A., "Uses of Factor Analysis in Counseling Psychology Research", Journal of Counseling Psychology, 34(4), pp.414~424 (1987)
- Treiblmaier, H., "Exploratory Factor Analysis Revisited : How Robust Methods Support the Detection of Hidden Multivariate Data Structures in IS Research", Information & Management, 47, pp.197~207 (2010)
- Velicer, W. F., "Determining the Number of Components from the Matrix of Partial Correlations", Psychomerika, 41, pp.321~327 (1976)