How time series data can be analyzed with PLS-SEM

Dear colleagues:

The short video linked below, on how to predict the price of bitcoin, is for those who want to understand how time series data can be analyzed with PLS-SEM.

https://youtu.be/8nTLYH-4uWM

Enjoy!

Cancelled: PLS Applications Symposium; 15 - 17 April 2020; Laredo, Texas

This event has been cancelled (COVID-19)

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PLS Applications Symposium; 15 - 17 April 2020; Laredo, Texas

(Abstract submissions accepted until 15 February 2020)

*** Only abstracts are needed for the submissions ***

The partial least squares (PLS) method has increasingly been used in a variety of fields of research and practice, particularly in the context of PLS-based structural equation modeling (SEM). The focus of this Symposium is on the application of PLS-based methods, from a multidisciplinary perspective. For types of submissions, deadlines, and other details, please visit the Symposium’s web site:

http://plsas.net

*** Workshop on PLS-SEM ***

On 15 April 2020 a full-day workshop on PLS-SEM will be conducted by Dr. Ned Kock and Dr. Geoffrey Hubona, using the software WarpPLS. Dr. Kock is the original developer of this software, which is one of the leading PLS-SEM tools today; used by thousands of researchers from a wide variety of disciplines, and from many different countries. Dr. Hubona has extensive experience conducting research and teaching topics related to PLS-SEM, using WarpPLS and a variety of other tools. This workshop will be hands-on and interactive, and will have two parts: (a) basic PLS-SEM issues, conducted in the morning (9 am - 12 noon) by Dr. Hubona; and (b) intermediate and advanced PLS-SEM issues, conducted in the afternoon (2 pm - 5 pm) by Dr. Kock. Participants may attend either one, or both of the two parts.

The following topics, among others, will be covered - Running a Full PLS-SEM Analysis - Conducting a Moderating Effects Analysis - Viewing Moderating Effects via 3D and 2D Graphs - Creating and Using Second Order Latent Variables - Viewing Indirect and Total Effects - Viewing Skewness and Kurtosis of Manifest and Latent Variables - Viewing Nonlinear Relationships - Solving Collinearity Problems - Conducting a Factor-Based PLS-SEM Analysis - Using Consistent PLS Factor-Based Algorithms - Exploring Statistical Power and Minimum Sample Sizes - Exploring Conditional Probabilistic Queries - Exploring Full Latent Growth - Conducting Multi-Group Analyses - Assessing Measurement Invariance - Creating Analytic Composites.

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Ned Kock

Symposium Chair

http://plsas.net

WarpPLS 7.0 upgraded to stable

Dear colleagues:

Version 7.0 of WarpPLS is now available as a stable version.

Please use this link () to go to the WarpPLS blog post describing this version’s new features.

You can download and install it for a free trial of from warppls.com ().

Enjoy!

Full latent growth and its use in PLS-SEM: Testing moderating relationships

The article below explains how one can conduct a full latent growth analysis, in the context of structural equation modeling via partial least squares (PLS-SEM). This type of analysis can be viewed as a comprehensive analysis of moderating effects where the moderating variable is “latent”, not “disrupting” the model in any way.

Kock (2020). Full latent growth and its use in PLS-SEM: Testing moderating relationships. Data Analysis Perspectives Journal, 1(1), 1-5.

A link to a PDF file is available ().

Abstract:

A full latent growth analysis, in the context of structural equation modeling via partial least squares (PLS-SEM), can be viewed as a comprehensive analysis of moderating effects where the moderating variable is “latent”, not “disrupting” the model in any way. In this paper we illustrate such an analysis employing WarpPLS, a leading PLS-SEM software tool.

WarpPLS 7.0 beta now available: HTMT, new moderating effects features, and more!

Dear colleagues:

Version 7.0 of WarpPLS is now available, as a beta version. You can download and install it for a free trial from:

https://warppls.com

Below is a summary of this version’s new features.

HTMT ratios. The sub-option “'Discriminant validity coefficients (extended set)”, under the menu option “Explore additional coefficients and indices”, now allows you to inspect the heterotrait-monotrait (HTMT) ratios calculated by the software. These ratios have been proposed for discriminant validity assessment, particularly in the context of composite-based SEM via classic PLS algorithms; as opposed to factor-based SEM via modern algorithms that estimate factors (which have been available from this software for quite some time now). Discriminant validity is a measure of the quality of a measurement instrument; the instrument itself is typically a set of question-statements. A measurement instrument has good discriminant validity if the question-statements (or other measures) associated with each latent variable are not confused by the respondents, in terms of their meaning, with the question-statements associated with other latent variables.

Discriminant validity coefficients (extended set). The HTMT ratios are provided along with other coefficients that are useful for discriminant validity assessment, in one single combined set of outputs. These other coefficients are correlations among latent variables and square roots of AVEs, structure loadings and cross-loadings, and full collinearity VIFs. For the HTMT ratios, the following coefficients are also provided: P values, and 90% confidence intervals.

There is a short video that illustrates the discriminant validity coefficients (extended set) ().

Graphical user interface optimization. Several elements of the graphical user interface, such as screens and warning messages, have been optimized so that users can perform SEM analysis tasks with only a few clicks – and in a straightforward fashion. For example, automatic re-analyses are now conducted whenever any of the several SEM analysis settings are changed, with the results becoming immediately available to users. Also, new menu options are now available to facilitate tasks; e.g., users can now open or create a project through the “Open or create project (Step 1)” menu option available under the “Project” menu option. This new “Open or create project (Step 1)” option allows users to open or create a project file, providing an alternative path for executing Step 1.

Fractional splits for 2D moderating effects graphs. Users can now set “fractional” splits for 2D moderating effects graphs, through a new “Split” menu option. The default is 0.5, which splits the sample in the middle when drawing the lines for the effects and the “low” and “high” values of the moderating variable. For instance, if you set the fractional split to 0.1, the software splits the sample in 10% (of the sample) to the left and 90% to the right, respectively for the “low” and “high” values of the moderating variable. This new and powerful fractional split feature enables users to significantly expand their options for illustrating moderating effects in 2D graphs. In the previous version of the software the split was set at 0.5 (i.e., in the middle), with no way to modify it.

There is a short video that illustrates the fractional splits for 2D moderating effects graphs ().

New moderating effects calculation options. Users can now choose among three options for moderating effects calculation: “Two Stages”, “Variable Orthogonalization”, and “Indicator Products”. This is done through the new “View or change moderating effects settings” menu option, under the “Settings” menu option on the software’s main window. The default moderating effects calculation option is “Two Stages”, whereby latent variable scores are calculated first and then used in a second stage for the creation of the interaction variable that implements the moderating effect. The “Variable Orthogonalization” option implements a similar procedure, but stochastically departs from a random variable, which is fully orthogonal to the latent variables in the model, for the creation of the interaction variable that implements the moderating effect. The “Indicator Products” option employs indicator products for the creation of the interaction variable that implements the moderating effect; this was the only option available in the previous version of the software.

There is a short video that illustrates the new moderating effects calculation options ().

Incremental code optimization. This is conducted in each new version of this software. At several points the code was optimized for speed, stability, and coefficient estimation precision. This led to incremental gains in speed even as a number of new features were added. Several of these new features required new and complex calculations, mostly to generate coefficients that were not available before.

Enjoy!

Individual paths can be set as linear or nonlinear in PLS-SEM analyses

In WarpPLS, the “View or change individual inner model analysis algorithm settings” option allows you to set inner model algorithms for individual paths in PLS-SEM analyses; whether these analyses are composite-based or factor-based. That is, for each path a user can select a different algorithm from among the following choices: “Linear”, “Warp2”, “Warp2 Basic”, “Warp3”, and “Warp3 Basic”.

This option is particularly useful in empirical investigations where researchers have solid theoretical reasons to expect certain paths to be associated with nonlinear relationships of particular types. Those researchers may also have solid theoretical reasons to expect certain paths to be associated with linear relationships. Given that one of the main goals of SEM is to test theory, theoretical considerations should be given a very high priority in the selection of algorithms to be used for each path in a model.

There is a short video that illustrates this ().

On a related note - since classic moderating effects analyses already capture nonlinearity, it is usually advisable for users to set moderating paths as linear. They can do this even as they set direct paths as nonlinear. This leads to 2D graphs that are easier to interpret. If users set moderating paths as nonlinear, their interpretation becomes more difficult, as more complex types of moderating relationships are captured.

PLS-SEM with factor estimation (PLSF-SEM): An applied discussion in the field of marketing

The article below (mentioned as forthcoming in a recent post; now published!) explains how one can conduct a factor-based PLS structural equation modeling (PLSF-SEM) analysis, with an illustration in the field of marketing, as well as the advantages of using PLSF-SEM in terms of avoidance of type I and II errors.

Kock, N. (2019). Factor-based structural equation modeling with WarpPLS. Australasian Marketing Journal, 27(1), 57-63.

A link to a PDF file is available ().

Abstract:

Structural equation modeling (SEM) is extensively used in marketing research. For various years now, there has been a somewhat heated debate among proponents and detractors of the use of the partial least squares (PLS) method for SEM. The classic PLS design, originally proposed by Herman Wold, has a number of advantages over covariance-based SEM; e.g., minimal model identification demands. However, that design does not base its model parameter recovery approach on the estimation of factors, but on composites, which are exact linear combinations of indicators. This leads to adverse consequences, primarily in the form of unacceptable levels of type I and II errors. Recently a new factor-based method for SEM has been developed, called PLSF, which we discuss in this paper. This method has the advantages of classic PLS, but without the problems inherent in the use of composites. For readers interested in trying it, the PLSF method is implemented in the SEM software WarpPLS.