Combining composites and factors in PLS-SEM models: A multi-algorithm technique

The article below presents a multi-algorithm technique for combining latent variables estimated as composites or factors into a single model, in the context of structural equation modeling via partial least squares (PLS-SEM).

Kock, N. (2024). Combining composites and factors in PLS-SEM models: A multi-algorithm technique. Data Analysis Perspectives Journal, 5(4), 1-8.

Link to full-text file for this and other DAPJ articles:

https://scriptwarp.com/dapj/#Published_Articles

Abstract:

A multi-algorithm technique is presented for combining latent variables estimated as composites or factors into a single model, in the context of structural equation modeling via partial least squares. The multi-algorithm technique consists of three key steps: selecting composite-based or factor-based outer model analysis algorithms to be used for latent variable estimation; adding the latent variables estimated with the chosen composite-based or factor-based algorithms as new standardized variables; and creating and estimating a final model with the new variables added as single indicators of latent variables.

Best regards to all!

A comparison of data analyses with WarpPLS and Stata: A study of trust and its role regarding internet use and subjective well-being

The article below provides a comparative assessment of analyses using the software packages WarpPLS and Stata, in the context of structural equation modeling via partial least squares (PLS-SEM), based on an illustrative study of trust and its role regarding internet use and subjective well-being.

Samak, A., Islam, M. R., & Hanke, D. (2024). A comparison of data analyses with WarpPLS and Stata: A study of trust and its role regarding internet use and subjective well-being. Data Analysis Perspectives Journal, 5(3), 1-6.

Link to full-text file for this and other DAPJ articles:

https://scriptwarp.com/dapj/#Published_Articles

Abstract:

This study investigates the mediating roles of social and institutional trust in the relationship between internet use and subjective well-being, using partial least squares (PLS)-based structural equation modeling (SEM). We compare WarpPLS 8.0 and Stata’s PLS-SEM package, utilizing data from the European Social Survey (ESS), round 8. Our results show consistent model fit and path coefficients across both tools, confirming the significant mediating effects of trust. WarpPLS stands out for its advanced model diagnostics, while Stata’s PLS-SEM excels in integrating with Stata’s broader data management and statistical analysis tools. This comparative analysis contributes to the SEM methodological literature.

Best regards to all!

Model-driven data analytics (MDDA): Resources for teachers and students

The technique of model-driven data analytics (MDDA) involves the creation of a path model expressing an applied theory, and testing the model using path analysis with latent variables. The latter, path analysis with latent variables, is generally known as structural equation modeling (SEM).

MDDA emerged from the work of a special category of users of the software WarpPLS – data analysis consultants, who regularly work with organizations to provide data-driven recommendations.

While MDDA can be implemented through a variety of software tools, it has found wide adoption among WarpPLS users, because of the many powerful features of this software that can be used in this context. Moreover, in WarpPLS all analyses are model-driven, which makes this software much more user-friendly than other software tools that rely on extensive scripting to conduct analyses.

The website linked below provides several resources for teachers and students, including: a textbook, which may be used by teachers of university courses on MDDA, as a free online document; datasets, which include not only data, but also scenarios, questions, and variables (these are used to illustrate how MDDA can be used to address the needs of various organizations); and YouTube videos, which provide step-by-step illustrations of how to analyze data in the context of scenarios, questions, and variables.

https://scriptwarp.com/mdda

Best regards to all!

Common method bias in PLS-SEM: Video, three articles, and sample dataset

The video linked below provides an overview on how to test for common method bias, in the context of structural equation modeling via partial least squares (PLS-SEM).

https://youtu.be/r5p0zHBqfBs

The articles below explain how one can conduct tests for common method bias in PLS-SEM. The first two articles (particularly the second) discuss the highest full collinearity variance inflation factor (FCVIF) test. The third article discusses Harman’s single factor test.

Kock, N., & Lynn, G.S. (2012). Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. Journal of the Association for Information Systems, 13(7), 546-580.

Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1-10.

Kock, N. (2021). Harman’s single factor test in PLS-SEM: Checking for common method bias. Data Analysis Perspectives Journal, 2(2), 1-6.

Links to PDF files are available (from the "Publications" area of WarpPLS.com):

https://scriptwarp.com/warppls/#Publications

Finally, the site area below (the "Resources" area of WarpPLS.com) provides a sample dataset available to users interested in trying the tests discussed above: "Dataset with and without common method bias".

https://scriptwarp.com/warppls/#Resources

Enjoy!

Harman’s single factor test in PLS-SEM: Checking for common method bias

The article below explains how one can check for common method bias using Harman’s single factor test in the context of structural equation modeling via partial least squares (PLS-SEM).

Kock, N. (2021). Harman’s single factor test in PLS-SEM: Checking for common method bias. Data Analysis Perspectives Journal, 2(2), 1-6.

A link to a PDF file is available ().

Abstract:

Common method bias can be defined, in the context of structural equation modeling via partial least squares (PLS-SEM), as a phenomenon that is caused by the measurement method used in a study, and not by the network of causes and effects connecting the latent variables in the study. We illustrate how Harman’s single factor test of common method bias can be conducted with WarpPLS, a leading PLS-SEM software tool.

Interview video: Conference on Information Systems in Latin America

Recently an interview was conducted for the 3rd Conference on Information Systems in Latin America. In it, Dr. Ned Kock was interviewed by Dr. Alexandre Graeml. The topics covered include: structural equation modeling (SEM), partial least squares (PLS) and related techniques, PLS-based SEM, covariance-based SEM, factors versus composites, nonlinear analyses, and WarpPLS.

WarpPLS and its application to research in business and information systems

The link below is for the Conference’s web site.

ISLA 2016 - Information Systems in Latin America Conference

Enjoy!

PLS-SEM performance with non-normal data

Many claims have been made in the past about the advantages of structural equation modeling employing the partial least squares method (PLS-SEM). While some claims may have been exaggerated, we are continuously finding that others have not. One of such claims, falling in the latter category (i.e., not an exaggeration), is that PLS-SEM is robust to deviations from normality. In other words, PLS-SEM performs quite well with non-normal data.

An article illustrating this advantage of PLS-SEM is available. Its reference, abstract, and link to full text are available below.

Kock, N. (2016). Non-normality propagation among latent variables and indicators in PLS-SEM simulations. Journal of Modern Applied Statistical Methods, 15(1), 299-315.

Structural equation modeling employing the partial least squares method (PLS-SEM) has been extensively used in business research. Often the use of this method is justified based on claims about its unique performance with small samples and non-normal data, which call for performance analyses. How normal and non-normal data are created for the performance analyses are examined. A method is proposed for the generation of data for exogenous latent variables and errors directly, from which data for endogenous latent variables and indicators are subsequently obtained based on model parameters. The emphasis is on the issue of non-normality propagation among latent variables and indicators, showing that this propagation can be severely impaired if certain steps are not taken. A key step is inducing non-normality in structural and indicator errors, in addition to exogenous latent variables. Illustrations of the method and its steps are provided through simulations based on a simple model of the effect of e-collaboration technology use on job performance.

The article’s main goal is actually to discuss a method to create non-normal data where the data creator has full access to all data elements, including factor or composite scores and all error terms, and where severe non-normality is extended to error terms. In the process of achieving this goal, the article actually demonstrates that PLS-SEM is very robust to severe deviations from normality, even when these deviations apply to all error terms. This is an issue that is often glossed over in PLS-SEM performance tests with non-normal data.

Readers may also find the YouTube video linked below useful in the context of this discussion.

View Skewness and Kurtosis in WarpPLS

Enjoy!

New PLS-based SEM email distribution list

A new email distribution list is available for those who share a common interest in partial least squares (PLS) regression and its use in structural equation modeling (SEM). To check it out click here.

Two new WarpPLS workshops in April and May of 2011

PLS-SEM.com will conduct two new online workshops on WarpPLS in April and May of 2011!

For more information on these and other WarpPLS workshops please visit:

http://pls-sem.com

Two new WarpPLS workshops in March and April 2010

PLS-SEM.com will conduct two new online workshops on WarpPLS in March and April 2010!

For more information on these and other WarpPLS workshops please visit:

http://pls-sem.com/cgi-bin/p/awtp-custom.cgi?d=plssem&page=10403

March 2010 online workshop on WarpPLS

PLS-SEM.com will conduct an online workshop on WarpPLS in March 2010!

The direct link to the workshop site is:

http://www.regonline.com/builder/site/Default.aspx?eventid=811252

The list of upcoming workshops is on:

http://pls-sem.com/cgi-bin/p/awtp-custom.cgi?d=plssem&page=10403