Browse > Article
http://dx.doi.org/10.6109/jkiice.2018.22.7.978

A Comparison Analysis among Structural Equation Modeling (AMOS, LISREL and PLS) Using the Same Data  

Nam, Soo-tai (Div. of Information and EC (Institute of Convergence and Creativity), Wonkwang University)
Kim, Do-goan (College of Liberal Arts (Institute of Convergence and Creativity), Wonkwang University)
Jin, Chan-yong (Div. of Information and EC (Institute of Convergence and Creativity), Wonkwang University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.22, no.7, 2018 , pp. 978-984 More about this Journal
Abstract
Structural equation modeling is pointing to statistical procedures that simultaneously perform path analysis and confirmatory factor analysis. Today, this statistical procedure is an essential tool for researchers in the social sciences. There are as AMOS, LISREL and PLS representative tools that can perform structural equation modeling analysis. AMOS provides a convenient graphical user interface for beginners to use. PLS has the advantage of not having a constraint on normal distribution as well as a graphical user interface. Therefore, we compared and analyzed the three most commonly used tools (applications) in social sciences. Based on structural equation modeling, confirmatory factor analysis was performed using the IBM AMOS Ver. 23, the LISREL 8.70 and the SmartPLS 2.0. The comparative results show that LISREL has the highest explanatory power of dependent variables than other analytical tools. The path coefficients and T-values presented by the analysis results showed similar results for all three analysis tools. This study suggests practical and theoretical implications based on the results.
Keywords
Analysis of moment structures; Linear structural relations; Partial least squares; Path analysis; Structural equation modeling;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 W. W. Chin, The Partial Least Squares Approach to Structural Equation Modeling, In G. A. Marcoulides (Ed.), New Jersey, NJ: Lawrence Erlbaum, 2015.
2 S. T. Nam and C. Y. Jin, "Factors Influencing on Continuous Usage Intention of Smartphone Based on the TAM (Technology Acceptance Model)," Journal of the Korea Institute of Information and Communication Engineering, vol. 21, no. 11, pp. 2076-2082, Nov. 2017.   DOI
3 V. Sujatha, "Auditing of Storage Security on Encryption TORAGE SECURITY ON ENCRYPTION," Asia-pacific Journal of Convergent Research Interchange, vol. 3, no. 2, pp. 1-9, Jun. 2017.
4 I. J. Kim, G. Y. Min and H. S. Shim, "The Structural Equation Modeling in MIS: The Perspectives of Lisrel and PLS Applications," Journal of Information Technology Services, vol. 10, no. 2, pp. 203-221, Jun. 2011.
5 H. Baumgartner, and C. Homburg, "Applications of Structural Equation Modeling in Marketing and Consumer Research: A Review," International Journal cf Research in Marketing, vol. 13, no. 2, pp. 139-161, Apr. 1996.   DOI
6 W. W. Chin, The Partial Least Squares Approach to Structured Equation Modeling. G. Marcoidids (Ed), New Jersey, NJ: Erlbaum Associates, 2015.
7 C. H. Choi and Y. Y. You, "The Study on Comparative Analysis of the Same Data through Regression Analysis Model and Structural Equation Model," Journal of Digital Convergence, vol. 14, no. 6, pp. 67-175, Jun. 2016.   DOI
8 C. D. Stapleton, Basic Concepts and Procedures of Confirmatory Factor Analysis, Paper Presented at the Annual Meeting of the Southwest Educational Research Association, Austin, :TX, 1997.
9 R. P. Bagozzi, and C. Fornell, Theoretical Concepts, Measurements, and Meaning, In C. Fornell (Ed.), A Second Generation of Multivariate Analysis: Measurement and Evaluation, New York, NY: Praeger, 1982.