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Sunday, April 18, 2021

A thank you note to the participants in the 2021 PLS Applications Symposium


This is just a thank you note to those who participated, either as presenters or members of the audience, in the 2021 PLS Applications Symposium:

https://plsas.net

As in previous years, it seems that it was a good idea to run the Symposium as part of the Western Hemispheric Trade Conference. This allowed attendees to take advantage of a subsidized registration fee, and also participate in other Conference sessions.

I have been told that the proceedings will be available soon, if they are not available yet, from the Western Hemispheric Trade Conference web site, which can be reached through the Symposium web site (link above).

Also, we had a nice full-day workshop on PLS-SEM using the software WarpPLS. This workshop, conducted by Dr. Jeff Hubona and myself, was fairly hands-on and interactive. Some participants had quite a great deal of expertise in PLS-SEM and WarpPLS. It was a joy to conduct the workshop!

As soon as we define the dates, we will be announcing next year’s PLS Applications Symposium. Like this years’ Symposium, it will take place in Laredo, Texas (hopefully face-to-face!), probably in the first half of April as well.

Thank you and best regards to all!

Ned Kock
Symposium Chair
https://plsas.net

Tuesday, April 13, 2021

Multilevel analyses in PLS-SEM: Video, article, and sample dataset


The video linked below provides an overview on how to conduct a multilevel analysis, in the context of structural equation modeling via partial least squares (PLS-SEM).

https://youtu.be/pNXI1Cz-Qkk

The article below explain how one can conduct a multilevel analysis in PLS-SEM. It employs a dataset that is very similar to the one used in the video above.

Kock, N. (2020). Multilevel analyses in PLS-SEM: An anchor-factorial with variation diffusion approach. Data Analysis Perspectives Journal, 1(2), 1-6.

A link to a PDF file is 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 procedures discussed above: "Job performance in three companies dataset".

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

Enjoy!

Tuesday, April 6, 2021

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!

Thursday, March 25, 2021

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.

Thursday, February 4, 2021

Assessing reciprocal relationships in PLS-SEM: An illustration based on a job crafting study


The article below explains how one can test reciprocal relationships in the context of structural equation modeling via partial least squares (PLS-SEM).

Morrow, D. L., & Conger, S. (2021). Assessing reciprocal relationships in PLS-SEM: An illustration based on a job crafting study. Data Analysis Perspectives Journal, 2(1), 1-5.

A link to a PDF file is available ().

Abstract:

Over the last 25 years two types of job crafting have emerged with similar quantitative measurement scales. This paper describes the process used in determining the presence of reciprocal relationships between the two job crafting constructs using WarpPLS.

Thursday, January 21, 2021

Using indicator correlation fit indices in PLS-SEM: Selecting the algorithm with the best fit


The article below explains how one can use indicator correlation fit indices for selecting the analysis algorithm with the best fit in the context of structural equation modeling via partial least squares (PLS-SEM).

Kock, N. (2020). Using indicator correlation fit indices in PLS-SEM: Selecting the algorithm with the best fit. Data Analysis Perspectives Journal, 1(4), 1-4.

A link to a PDF file is available ().

Abstract:

Upon completion of a PLS-SEM analysis, one can obtain the model-implied indicator correlation matrix and compare it with the actual indicator correlation matrix. The latter is obtained directly from the data being analyzed. Indicator correlation fit indices are quantifications of the differences among these two matrices. Our focus in this paper is on the use of indicator correlation fit indices in PLS-SEM for selecting the analysis algorithm with the best fit.

Tuesday, November 10, 2020

PLS Applications Symposium; 14 - 16 April 2021; Laredo, Texas


PLS Applications Symposium; 14 - 16 April 2021; Laredo, Texas

(Abstract submissions accepted until 15 February 2021)

*** 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:

https://plsas.net

*** Workshop on PLS-SEM ***

On 14 April 2021 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