Smart PLS

Get deep insights into your data easily!

Smart PLS 3 is a milestone in latent variable modeling. It combines state of the art methods (e.g., PLS-POS, IPMA, complex bootstrapping routines) with an easy to use and intuitive graphical user interface.

Smart PLS is the workhorse for all PLS-SEM analyses – for beginners as well as experts alike.

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Smart PLS GmbH.


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    • SmartPLS is the workhorse for all PLS-SEM analyses.
    • Running your SmartPLS analyses is fun and hassle-free.
    • Get deep insights into your data easily!
    • The powerful modeling environment lets you create a path model in minutes.
    • The project manager helps you to keep track of all your analyzes and files.
    • Customize your model with colors, borders and fonts to underline your ideas individually!
    • In-built explanations of the algorithms and meaningful defaults give you an easy start into the PLS-SEM world.
    • Well-organized reports provide full insights into your results.
    • Save your results permanently as HTML report or Excel file.
    • Create data groups to run multi-group analyses effortlessly.
    • Create interaction terms and run moderator analyses without any problems.
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    • Research Institution
    • Researcher
    • Analyst
    • Students

    Get to Know PLS-SEM and SmartPLS - Some Resources for You !

    Video Tutorials for SmartPLS

    The heterotrait-monotrait ratio of correlations (HTMT) is a new method for assessing discriminant validity in partial least squares structural equation modeling, which is one of the key building blocks of model evaluation. If discriminant validity is not established, researchers cannot be certain that the results confirming hypothesized structural paths are real, or whether they are merely the result of statistical discrepancies. The HTMT criterion clearly outperforms classic approaches to discriminant validity assessment such as Fornell-Larcker criterion and (partial) cross-loadings, which are largely unable to detect a lack of discriminant validity.

    This video shows how to apply the HTMT discriminant validity assessment criterion on the corporate reputation PLS path model example by using the SmartPLS software.

    The importance-performance map analysis (IPMA) — also called importance-performance matrix, impact-performance map, or priority map analysis — is a useful analysis approach in PLS-SEM that extends the results of the estimated path coefficient (importance) by adding a dimension that considers the average values of the latent variable scores (performance). More precisely, the IPMA contrasts the unstandardized total effects (importance) in the structural and the average values of the latent variable scores on a scale from 0 to 100 (performance) in a graphical representation. The resulting importance-performance map permits the identification of determinants with a relatively high importance and relatively low performance. These become major and high priority improvement areas with the goal to in turn increase the performance of the selected key target construct in the PLS path model. The IPMA facilitates more elaborated interpretations of PLS-SEM results.

    Use SmartPLS 3 with More Memory

    PLS-MGA is a multi-group analysis method that has been developed for partial least squares structural equation modeling (PLS-SEM). In this video, we explain the PLS-MGA method as introduced by Hair at al. (2017) and Sarstedt et al. (2011). As an alternative, you may use the permutation test (Chin and Dibbern, 2010).