SmartPLS

Last updated
SmartPLS
Original author(s) Christian M. Ringle, Sven Wende, Jan-Michael Becker
Developer(s) SmartPLS GmbH
Initial release2005 (2005)
Stable release
Smart PLS 4.0.9.5 / June 23, 2023;15 months ago (2023-06-23)
Operating system Windows and Mac
Platform Java
Available in English (default language), Arabic, Chinese, French, German, Indonesian, Italian, Japanese, Korean, Malay, Persian, Polish, Portuguese, Romanian, Spanish, Urdu, Bengali, Czech, Hebrew, Hindi, Croatian, Kurdish, Norwegian, Russian, Swedish, Thai, Turkish, Vietnamese
Type Statistical analysis, multivariate analysis, structural equation modeling, partial least squares path modeling
License SmartPLS 4: Proprietary software
Website www.smartpls.com

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] [5] Users can estimate models with their data by using basic PLS-SEM, weighted PLS-SEM (WPLS), consistent PLS-SEM (PLSc-SEM), and sumscores regression algorithms. [6] [7] The software computes standard results assessment criteria (e.g., for the reflective and formative measurement models and the structural model, including the HTMT criterion, bootstrap based significance testing, PLSpredict, and goodness of fit) [8] and it supports additional statistical analyses (e.g., confirmatory tetrad analysis, higher-order models, importance-performance map analysis, latent class segmentation, mediation, moderation, measurement invariance assessment, multigroup analysis, regression analysis, logistic regression, path analysis, PROCESS, confirmatory factor analysis, and covariance-based structural equation modeling). [9] [10] [11] Since SmartPLS is programmed in Java, it can be executed and run on different computer operating systems such as Windows and Mac. [12]

Contents

SmartPLS4

The Newest addition is the SmartPLS4. The software released to the general public in 2022 is an easy to use tool for Structural Equation Modelling. To estimate the model in SmartPLS, the model has to be estimated at two levels that include the measurement model assessment and structural model assessment.

Measurement Model assessment involves several steps [13] that includes the assessment of quality criteria that includes the evaluation of factor loadings, construct reliability, construct validity. The criteria for factor loadings is 0.70, any items with loadings less than 0.70 may be considered for removal, if removing the items can improve the reliability and validity over the required threshold. Further Construct reliability is assessed using Cronbach Alpha and Composite Reliability, the required value for both is 0.70. [14] Further, construct validity is assessed using convergent validity (AVE > 0.50) and Discriminant validity (Fornell & Larcker Criterion and Heterotrait-Monotrait Ratio).

Next, after measurement model assessment structural model is assessed to substantiate the proposed hypotheses. This can include direct, indirect, or moderating relationships. SmartPLS4 is an increasingly used tool for SEM that can help model simple and complex model. [15]

See also

Related Research Articles

Multivariate statistics is a subdivision of statistics encompassing the simultaneous observation and analysis of more than one outcome variable, i.e., multivariate random variables. Multivariate statistics concerns understanding the different aims and background of each of the different forms of multivariate analysis, and how they relate to each other. The practical application of multivariate statistics to a particular problem may involve several types of univariate and multivariate analyses in order to understand the relationships between variables and their relevance to the problem being studied.

Psychological statistics is application of formulas, theorems, numbers and laws to psychology. Statistical methods for psychology include development and application statistical theory and methods for modeling psychological data. These methods include psychometrics, factor analysis, experimental designs, and Bayesian statistics. The article also discusses journals in the same field.

In statistics, path analysis is used to describe the directed dependencies among a set of variables. This includes models equivalent to any form of multiple regression analysis, factor analysis, canonical correlation analysis, discriminant analysis, as well as more general families of models in the multivariate analysis of variance and covariance analyses.

<span class="mw-page-title-main">Structural equation modeling</span> Form of causal modeling that fit networks of constructs to data

Structural equation modeling (SEM) is a diverse set of methods used by scientists doing both observational and experimental research. SEM is used mostly in the social and behavioral sciences but it is also used in epidemiology, business, and other fields. A definition of SEM is difficult without reference to technical language, but a good starting place is the name itself.

In psychology, discriminant validity tests whether concepts or measurements that are not supposed to be related are actually unrelated.

In statistics, confirmatory factor analysis (CFA) is a special form of factor analysis, most commonly used in social science research. It is used to test whether measures of a construct are consistent with a researcher's understanding of the nature of that construct. As such, the objective of confirmatory factor analysis is to test whether the data fit a hypothesized measurement model. This hypothesized model is based on theory and/or previous analytic research. CFA was first developed by Jöreskog (1969) and has built upon and replaced older methods of analyzing construct validity such as the MTMM Matrix as described in Campbell & Fiske (1959).

LISREL is a proprietary statistical software package used in structural equation modeling (SEM) for manifest and latent variables. It requires a "fairly high level of statistical sophistication".

Psychometric software refers to specialized programs used for the psychometric analysis of data obtained from tests, questionnaires, polls or inventories that measure latent psychoeducational variables. Although some psychometric analyses can be performed using general statistical software such as SPSS, most require specialized tools designed specifically for psychometric purposes.

The Unscrambler X is a commercial software product for multivariate data analysis, used for calibration of multivariate data which is often in the application of analytical data such as near infrared spectroscopy and Raman spectroscopy, and development of predictive models for use in real-time spectroscopic analysis of materials. The software was originally developed in 1986 by Harald Martens and later by CAMO Software.

One application of multilevel modeling (MLM) is the analysis of repeated measures data. Multilevel modeling for repeated measures data is most often discussed in the context of modeling change over time ; however, it may also be used for repeated measures data in which time is not a factor.

Measurement invariance or measurement equivalence is a statistical property of measurement that indicates that the same construct is being measured across some specified groups. For example, measurement invariance can be used to study whether a given measure is interpreted in a conceptually similar manner by respondents representing different genders or cultural backgrounds. Violations of measurement invariance may preclude meaningful interpretation of measurement data. Tests of measurement invariance are increasingly used in fields such as psychology to supplement evaluation of measurement quality rooted in classical test theory.

The partial least squares path modeling or partial least squares structural equation modeling is a method for structural equation modeling that allows estimation of complex cause-effect relationships in path models with latent variables.

<span class="mw-page-title-main">Christian M. Ringle</span> German academic

Christian M. Ringle is a professor of management and decision sciences at the Hamburg University of Technology, Germany. He is one of the co-founders and co-developers of SmartPLS, a Java-based software package for composite-based structural equation modeling using the partial least squares path modeling method. Since 2018, Christian Ringle has been included in the Clarivate Analytics' highly cited researchers list.

<span class="mw-page-title-main">WarpPLS</span>

WarpPLS is a software with graphical user interface for variance-based and factor-based structural equation modeling (SEM) using the partial least squares and factor-based methods. The software can be used in empirical research to analyse collected data and test hypothesized relationships. Since it runs on the MATLAB Compiler Runtime, it does not require the MATLAB software development application to be installed; and can be installed and used on various operating systems in addition to Windows, with virtual installations.

<span class="mw-page-title-main">Average variance extracted</span>

In statistics (classical test theory), average variance extracted (AVE) is a measure of the amount of variance that is captured by a construct in relation to the amount of variance due to measurement error.

In statistics, confirmatory composite analysis (CCA) is a sub-type of structural equation modeling (SEM). Although, historically, CCA emerged from a re-orientation and re-start of partial least squares path modeling (PLS-PM), it has become an independent approach and the two should not be confused. In many ways it is similar to, but also quite distinct from confirmatory factor analysis (CFA). It shares with CFA the process of model specification, model identification, model estimation, and model assessment. However, in contrast to CFA which always assumes the existence of latent variables, in CCA all variables can be observable, with their interrelationships expressed in terms of composites, i.e., linear compounds of subsets of the variables. The composites are treated as the fundamental objects and path diagrams can be used to illustrate their relationships. This makes CCA particularly useful for disciplines examining theoretical concepts that are designed to attain certain goals, so-called artifacts, and their interplay with theoretical concepts of behavioral sciences.

<span class="mw-page-title-main">Marko Sarstedt</span> German academic and a marketing researcher

Marko Sarstedt is a German academic and a marketing researcher. He is a Full Professor at the Ludwig Maximilian University of Munich and Adjunct Research Professor at Babeș-Bolyai-University.

Necessary condition analysis (NCA) is a research approach and tool employed to discern "necessary conditions" within datasets. These indispensable conditions stand as pivotal determinants of particular outcomes, wherein the absence of such conditions ensures the absence of the intended result. For example, the admission of a student into a Ph.D. program necessitates a prior degree; the progression of AIDS necessitates the presence of HIV; and organizational change necessitates communication.

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, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.), Thousand Oaks, CA: Sage Publications.
  3. Hair Jr, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2018). 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. Mumtaz Ali Memona, T. Ramayah, Jun-Hwa Cheah, Hiram Ting, Francis Chuah and Tat Huei Cham (2021). "PLS-SEM Statistical Programs: A Review" (PDF). Journal of Applied Structural Equation Modeling. 5(i): i–xiv.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  6. Lohmöller, J.-B. (1989). Latent variable path modeling with partial least squares. Physica: Heidelberg, p. 29.
  7. Wold, H. (1982). Soft modeling: The basic design and some extensions, in: K. G. Jöreskog and H. Wold (eds.), Systems under indirect observations: Part II, North-Holland: Amsterdam, pp. 1-54, pp. 2-3.
  8. Ramayah, T., Cheah, J., Chuah, F., Ting, H., and Memon, M. A. (2018). Partial least squares structural equation modeling (PLS-SEM) using SmartPLS 3.0: An updated and practical guide to statistical analysis (2nd ed.), Singapore et al.: Pearson.
  9. Garson, G. D. (2016). Partial least squares regression and structural equation models, Statistical Associates: Asheboro, pp. 122-188.
  10. 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. S2CID   198334897.
  11. Hair, Joseph F.; Risher, Jeffrey J.; Sarstedt, Marko; Ringle, Christian M. (2019). "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. S2CID   158782424.
  12. 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.
  13. "Steps in Data Analysis". ResearchWithFawad. Retrieved 2023-10-09.
  14. "A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)". SAGE India. 2023-10-08. Retrieved 2023-10-09.
  15. "Recommended videos - SmartPLS". www.smartpls.com. Retrieved 2023-10-09.