Advanced SearchSearch Tips
Utilization of R Program for the Partial Least Square Model: Comparison of SmartPLS and R
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
  • Journal title : Journal of Digital Convergence
  • Volume 13, Issue 12,  2015, pp.117-124
  • Publisher : The Society of Digital Policy and Management
  • DOI : 10.14400/JDC.2015.13.12.117
 Title & Authors
Utilization of R Program for the Partial Least Square Model: Comparison of SmartPLS and R
Kim, Yong-Tae; Lee, Sang-Jun;
  PDF(new window)
As the acceptance of statistical analysis has been increased because of Big Data, the needs for an advanced second generation of statistical analysis method like Structural Equation Model are also increasing. This study suggests how R-Program, as open software, can be utilized when Partial Least Square Model, one of the SEMs, is applied to statistical analysis. R is a free software as a part of GNU projects as well as a powerful and useful tool for statistical analysis including Big Data. The study utilized R and SmartPLS, a representative statistical package of PLS-SEM, and analyzed internal consistency reliability, convergent validity, and discriminant validity of the measurement model. The study also analyzed path coefficients and moderator effects of the structural model and compared the results, respectively. The results indicated that R showed the same results with SmartPLS on the measurement model and the structural model. Therefore, the study confirmed that R could be a powerful tool that is alternative to a commercial statistical package in the future.
Structural Equation Model;Partial Least Square;R;SmartPLS;Quantitative Convergence Analysis;
 Cited by
J. F. Hair Jr, G. T. M. Hult, C. Ringle, and M. Sarstedt, "A Primer on Partial Least Squares Structural Equation Modeling(PLS-SEM)", Sage Publications, 2013.

W. W. Chin, "The Partial Least Squares Approach to Structural Equation Modeling", Modern Methods for Business Research, Vol. 295, No. 2, pp. 295-336, 1998.

J. F. Hair, C. M. Ringle, and M. Sarstedt, "PLS-SEM: Indeed a Silver Bullet", Journal of Marketing Theory and Practice, Vol. 19, No. 2, pp. 139-152, 2011. crossref(new window)

M. Haenlein and A. M. Kaplan, "A Beginner's Guide to Partial Least Squares Analysis", Understanding Statistics, Vol. 3, No. 4, pp. 283-297, 2004. crossref(new window)

J. Verzani, "Using R for Introductory Statistics", Chapman & Hall/CRC, 2004.

W. W. Chin, "Issues and Opinion on Structural Equation Modeling", MIS Quarterly, Vol. 22, No. 1, pp. 7-16, 1998.

B. R. Bae, "Analyses of Moderating and Mediating Effects with SPSS/AMOS/LISREL/ Smartpls", Ckbooks, 2015.

C. Cassel, P. Hackl, and A. H. Westlund, "Robustness of Partial Least-Squares Method for Estimating Latent Variable Quality Structures", Journal of Applied Statistics, Vol. 26, No. 4, pp. 435-446, 1999. crossref(new window)

H. Kim, K. H. Park, "The Impact of Collaboration Process and Capabilities on Innovation Performance in Convergence Environment", Journal of Digital Convergence, Vol. 13, No. 5, pp. 151-158, 2015.

J. Henseler, C. M. Ringle, and R. R. Sinkovics, "The Use of Partial Least Squares Path Modeling in International Marketing", Advances in International Marketing(AIM), Vol. 20, pp. 277-320, 2009.

Y. B. Yang, M. C. Kim, "Effect of HPM Factors on Adoption Attitude of u-Health System: Moderating Effects of Gender", Journal of Digital Convergence, Vol. 13, No. 7, pp. 213-221, 2015.

L. A. Pace, "Beginning R: An Introduction to Statistical Programming", Apress, 2012.

G. Grolemund, "Hands-on Programming with R: Write Your Own Functions and Simulations", O'Reilly Media, 2014.

G. Sanchez, "PLS Path Modeling with R", Online, 2013.

S. Y. Kim, S. J. Lee, "Effects of Smart Phone's Brand Images on Customer's Satisfaction and Loyalty: Focused on Galaxy and iPhone User Groups", Journal of Digital Convergence, Vol. 12, No. 10, pp. 223-233, 2014.

E. G. Carminesa and R. A. Zeller, "Reliability and Validity Assessment", Sage Publications, 1979.

C. Fornell and D. F. Larcker, "Evaluating Structural Equation Models with Unobservable Variables and Measurement Error", Journal of Marketing Research, Vol. 18, No. 1, pp. 39-50, 1981. crossref(new window)