Suvarna Garge (Editor)

SmartPLS

Updated on
Edit
Like
Comment
Share on FacebookTweet on TwitterShare on LinkedInShare on Reddit
Developer(s)
  
SmartPLS GmbH

Operating system
  
Windows & Mac

Initial release
  
2005 (2005)

Platform
  
Java

SmartPLS

Original author(s)
  
Christian M. Ringle, Sven Wende, Jan-Michael Becker

Stable release
  
SmarPLS 3.2.6 / November 13, 2016; 4 months ago (2016-11-13)

SmartPLS is a software with graphical user interface for variance-based structural equation modeling (SEM) using the partial least squares (PLS) method. The software can be used in empirical research to analyse collected data (e.g. from surveys) and test hypothesized relationships. Since it is programmed in Java, it can be executed and run on Windows and macOS operating systems.

Contents

Downloads

  • Commercial: SmarPLS 3, which offers different licensing options such as a free trial license (one month), single user and network license options, and considerable discounts for academic users.
  • Freeware: SmartPLS 2
  • Data

    SmartPLS uses raw data. The data import uses the text (.txt) and comma separated values (.csv) file format. The columns of the data file represent the variables; the rows represent observations and responses. Only the first row of the data matrix, which becomes the header in SmartPLS, can contain text. Otherwise, only numbers are allowed. Empty cells (e.g., if a missing value occurs) are not allowed. A number that is not used otherwise in the dataset (e.g., -999,999) must be used to fill empty cells. After data import, the user can indicate that this number represents a missing value. SmartPLS automatically identifies the data and number format (e.g., Europe or the USA). If a problem occurs, the user can later determine file format specifications and the number format in the software. In addition to the variables used as indicators for PLS path modeling, the data matrix can contain a weighting vector of the observations and responses (e.g., for carrying out weighted PLS path modeling analyses) and grouping variables (e.g., for conducting a PLS multigroup analysis, PLS-MGA).

    Graphical User Interface

    The graphical interface allows users to create a PLS path model. Circles represent latent variables and rectangular their indicators. The indicators stem from the variables of the imported data set. Drag and drop allows assigning indicators to a latent variable and, thereby, to establish the measurement model. In the structural model, an arrow drawing option allows the user to connect the latent variables with each other.

    Algorithms

    The primary basic partial least squares algorithm (Wold 1982, Lohmöller 1989) is the primary algorithm used in SmartPLS. The following list provides an overview of implemented algorithms and analytical options:

  • Partial least squares (PLS) path modeling algorithm (including consistent PLS, PLSc)
  • Weighted PLS (and PLSc) path modeling algorithm
  • Ordinary least squares regression based on sumscores
  • Advanced bootstrapping options
  • Blindfolding: Blindfolding is a sample re-use technique that calculates the Stone-Geisser’s Q² value as a criterion of predictive relevance.
    Blindfolding is only applied to constructs with a reflective measurement model specification.
  • Importance-performance map (or matrix) analysis (IPMA)
  • Multi-group analysis (MGA): A technique to test for differences between identical models estimated for different groups.
  • Hierarchical component models (second-order models)
  • Mediation: Estimation of indirect effects and their bootstrap-based significance testing.
  • Moderation: Estimation of interaction effects and their bootstrap-based significance testing.
  • Nonlinear relationships: Estimation of quadratic effects and their bootstrap-based significance testing.
  • Confirmatory tetrad analysis (CTA): A statistical technique which allows for empirical testing the measurement model setup.
  • Finite mixture (FIMIX) segmentation: A latent class approach which allows identifying and treating unobserved heterogeneity in path models.
  • Prediction-oriented segmentation (POS)
  • PLS Predict
  • Documentation

    In-built Documentation. For every algorithm, an in-built documentation gives a short explanation. It also provides information regarding the parameter setting of the algorithms. References to key articles allow the user to access more detailed information about the specific algorithms and analysis options.

    Results Report. For every analysis, SmartPLS offers a results report, which contains information about the model and data used for the analysis, key results, quality criteria (if relevant for a certain algorithm) and graphical results presentation (if applicable). The default offers a results representation within the SmartPLS software. In addition, SmartPLS allows exporting the results as a Microsoft Excel file, an HTML file, and in a file format that can be used by the statistical software R.

    References

    SmartPLS Wikipedia