Links to specific topics

Sunday, September 30, 2018

Introduction to PLS-SEM using WarpPLS 6.0


Check out the online webinar series - Introduction to PLS-SEM using WarpPLS 6.0:

https://goo.gl/PWxZsF

The live webinar series will be presented in six weekly 90-minute onine sessions from 11:30AM EDT to 1:00PM EDT on (mostly) consecutive 2018 Fridays: August 17th and 24th; and September 7th, 14th, 21st and 28th. The audio and video for all of the live webinar sessions will be recorded and those recordings will be made available for permanent download by each registered webinar participant. Each participant who successfully completes the 6-session webinar series will also receive an Introduction to PLS-SEM using WarpPLS Certificate of Completion signed by both Dr. Ned Kock, the original developer of WarpPLS, and by Dr. Geoffrey Hubona, the instructor of this webinar series.

Tuesday, July 10, 2018

Single missing data imputation in PLS-based structural equation modeling


An important source of bias in structural equation modeling (SEM) employing the partial least squares method (PLS) is missing data. Deletion methods, such as listwise and pairwise deletion, have traditionally been used to deal with missing data. These methods are perceived as leading to selective loss of data and significant related biases. Missing data imputation methods, on the other hand, do not resort to deletion.  Our study suggests that single missing data imputation methods perform better with PLS-SEM than expected based on past research on their performance with other multivariate analysis techniques such as multiple regression and covariance-based SEM:

http://cits.tamiu.edu/kock/pubs/journals/2018/Kock_2018_JMASM_MissDataImputationPLS.pdf

Saturday, April 14, 2018

A thank you note to the participants in the 2018 PLS Applications Symposium


This is just a thank you note to those who participated, either as presenters or members of the audience, in the 2018 PLS Applications Symposium:


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 and the Conference's social event.

I have been told that the proceedings will be available soon from the Western Hemispheric Trade Conference web site, which can be reached through the Symposium web site (link above).

Also, the full-day workshop on PLS-SEM using the software WarpPLS was well attended. This workshop 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, probably in mid-April as well.

Thank you and best regards to all!

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Ned Kock
Symposium Chair
http://plsas.net

Friday, April 13, 2018

PLS Applications Symposium; 11 - 13 April 2018; Laredo, Texas


PLS Applications Symposium; 11 - 13 April 2018; Laredo, Texas
(Abstract submissions accepted until 15 February 2018)

*** 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 11 April 2018 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

Monday, December 4, 2017

Data labels


In WarpPLS data labels can be added through the menu options “Add data labels from clipboard” and “Add data labels from file”. Data labels are text identifiers that are entered by you through these options, one column at a time.

Like the original numeric dataset, the data labels are stored in a table. Each column of this table refers to one data label variable, and each row to the corresponding row of the original numeric dataset.



Data labels can be shown on graphs (as illustrated above), either next to each data point that they refer to, or as part of the legend for a graph. The short video linked below illustrates this.

https://youtu.be/i5-_WIMXVl4

Once they have been added, data labels can be viewed or saved using the “View or save data labels” option.

Data labels can also be used to discover moderating effects, as discussed in the blog post linked below.

http://warppls.blogspot.com/2014/02/using-data-labels-to-discover.html

This can be done in conjunction with the “Explore full latent growth” option, which provides a powerful alternative for the identification of moderating effects:

https://warppls.blogspot.com/2017/10/full-latent-growth.html


Thursday, October 5, 2017

True composite and factor reliabilities


The menu option “Explore additional coefficients and indices”, available in WarpPLS starting in version 6.0,  allows you to obtain an extended set of reliabilities. The extended set of reliabilities includes the classic reliability coefficients already available in the previous version of this software, plus the following, for each latent variable in your model: Dijkstra's PLSc reliability (also available via the new menu option “Explore Dijkstra's consistent PLS outputs”), true composite reliability, and factor reliability. When factor-based PLS algorithms are used in analyses, the true composite reliability and the factor reliability are produced as estimates of the reliabilities of the true composites and factors. They are calculated in the same way as the classic composite reliabilities available from the previous version of this software, but with different loadings. When classic composite-based (i.e., non-factor-based) algorithms are used, both true composites and factors coincide, and are approximated by the composites generated by the software. As such, true composite and factor reliabilities equal the corresponding composite reliabilities whenever composite-based algorithms are used.

Related YouTube video:

Explore True Composite and Factor Reliabilities in WarpPLS

http://youtu.be/DwslOCEvOd4

Fit indices comparing indicator correlation matrices


The new menu option “Explore additional coefficients and indices”, available in WarpPLS starting in version 6.0, allows you to obtain an extended set of model fit and quality indices. The extended set of model fit and quality indices includes the classic indices already available in the previous version of this software, as well as new indices that allow investigators to assess the fit between the model-implied and empirical indicator correlation matrices. These new indices are the standardized root mean squared residual (SRMR), standardized mean absolute residual (SMAR), standardized chi-squared (SChS), standardized threshold difference count ratio (STDCR), and standardized threshold difference sum ratio (STDSR). As with the classic model fit and quality indices, the interpretation of these new indices depends on the goal of the SEM analysis. Since these indices refer to the fit between the model-implied and empirical indicator correlation matrices, they become more meaningful when the goal is to find out whether one model has a better fit with the original data than another, particularly when used in conjunction with the classic indices. When assessing the model fit with the data, several criteria are recommended. These criteria are discussed in the WarpPLS User Manual.

Related YouTube video:

Explore Indicator Correlation Matrix Fit Indices in WarpPLS

http://youtu.be/YutkhEPW-CE