SmartPLS

SmartPLS is a software with graphical user interface for variance-based structural equation modeling (SEM) using the partial least squares (PLS) path modeling method.[1][2][3][4] Besides estimating path models with latent variables using the PLS-SEM algorithm,[5][6] the software computes standard results assessment criteria (e.g., for the reflective and formative measurement models, the structural model, and the goodness of fit)[7] and it supports additional statistical analyses (e.g., confirmatory tetrad analysis, importance-performance map analysis, segmentation, multigroup).[8][9][10] Since SmartPLS is programmed in Java, it can be executed and run on different computer operating systems such as Windows and Mac.[11]


SmartPLS
Original author(s)Christian M. Ringle, Sven Wende, Jan-Michael Becker
Developer(s)SmartPLS GmbH
Initial release2005 (2005)
Stable release
Smart PLS 3.3.3 / January 11, 2021 (2021-01-11)
Operating systemWindows and Mac
PlatformJava
Available inEnglish (default language), Arabic, Chinese, French, German, Indonesian, Italian, Japanese, Korean, Malay, Persian, Polish, Portuguese, Romanian, Spanish, Urdu
TypeStatistical analysis, multivariate analysis, structural equation modeling, partial least squares path modeling
LicenseSmartPLS 2: Freeware, SmartPLS 3: Proprietary software
Websitewww.smartpls.com/smartpls2

See also

References
  1. Wong, K. K. K. (2013). Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS. Marketing Bulletin, 24(1), pp. 1-32, p. 1, p. 15, and p. 30.
  2. Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM), Thousand Oaks, CA: Sage Publications.
  3. Hair Jr, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2017). Advanced issues in partial least squares structural equation modeling (PLS-SEM),Thousand Oaks, CA: Sage Publications.
  4. Wong, Ken Kwong-Kay (2019-02-22). Mastering Partial Least Squares Structural Equation Modeling (Pls-Sem) with Smartpls in 38 Hours. iUniverse. ISBN 9781532066481.
  5. Lohmöller, J.-B. (1989). Latent Variable Path Modeling with Partial Least Squares. Physica: Heidelberg, p. 29.
  6. Wold, H. O. A. (1982). Soft Modeling: The Basic Design and Some Extensions, in: K. G. Jöreskog and H. O. A. Wold (eds.), Systems Under Indirect Observations: Part II, North-Holland: Amsterdam, pp. 1-54, pp. 2-3.
  7. Ramayah, T., Cheah, J., Chuah, F., Ting, H., and Memon, M. A. (2016). Partial Least Squares Structural Equation Modeling (PLS-SEM) Using SmartPLS 3.0: An Updated and Practical Guide to Statistical Analysis, Singapore et al.: Pearson, pp. 59-148.
  8. Garson, G. D. (2016). Partial Least Squares Regression and Structural Equation Models, Statistical Associates: Asheboro, pp. 122-188.
  9. Sarstedt, Marko; Cheah, Jun-Hwa (2019-06-27). "Partial least squares structural equation modeling using SmartPLS: a software review" (PDF). Journal of Marketing Analytics. 7 (3): 196–202. doi:10.1057/s41270-019-00058-3. ISSN 2050-3318.
  10. Hair, Joseph F.; Risher, Jeffrey J.; Sarstedt, Marko; Ringle, Christian M. (2019-01-01). "When to use and how to report the results of PLS-SEM". European Business Review. 31 (1): 2–24. doi:10.1108/EBR-11-2018-0203. ISSN 0955-534X.
  11. Temme, D., Kreis, H., and Hildebrandt, L. (2010). A Comparison of Current PLS Path Modeling Software: Features, Ease-of-Use, and Performance, in: V. Esposito Vinzi, W. W. Chin, J. Henseler, and H. Wang (eds.), Handbook of Partial Least Squares: Concepts, Methods and Applications, Springer: Berlin-Heidelberg, pp. 737-756, p.745.
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