PLS Applications Symposium; 15 - 17 April 2015; Laredo, Texas

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).

As an emerging method, its users often face challenges in successfully publishing PLS-based research, hence the theme of this year's Symposium: Successfully publishing PLS-based research.

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

Ned Kock
Symposium Chair
http://plsas.net

WarpPLS 5.0 upgraded to stable

Dear colleagues:

Version 5.0 of WarpPLS is now available as a stable version. You can download and install it for a free trial from:

http://warppls.com

This version was initially released as a beta version and was later upgraded to stable. It has undergone extensive testing in-house prior to its release as a beta version, and has been in the hands of users for several months prior to its upgrade to stable.

The full User Manual is also available for download from the web site above separately from the software. See this document, and the link below to a previous post, for more details about this new version.

http://bit.ly/1xkfjoN

Enjoy!

WarpPLS 5.0 now available: Factor-based PLS-SEM algorithms, rotating 3D graphs, normality tests, missing data imputation methods, and more!

Dear colleagues:

Version 5.0 of WarpPLS is now available, as a beta version. You can download and install it for a free trial from:

http://warppls.com

The full User Manual is also available for download from the web site above separately from the software.

Some important notes for users of previous versions:

- There is no need to uninstall previous versions of WarpPLS to be able to install and use this new version.

- Users of previous versions can use the same license information that they already have; it will work for version 5.0 for the remainder of their license periods.

- Project files generated with previous versions are automatically converted to version 5.0 project files. Users are notified of that by the software, and given the opportunity not to convert the files if they so wish.

- The MATLAB Compiler Runtime 7.14, used in this version, is the same as the one used in versions 2.0-4.0. Therefore, if you already have one of those versions of WarpPLS installed on your computer, you should not reinstall the Runtime.

WarpPLS is a powerful PLS-based structural equation modeling (SEM) software. Since its first release in 2009, its user base has grown steadily, now comprising more than 7,000 users in over 33 countries.

Some of its most distinguishing features are the following:

- Very easy to use, with a step-by-step user interface guide.

- Implements classic (composite-based) as well as factor-based PLS algorithms.

- Identifies nonlinear relationships, and estimates path coefficients accordingly.

- Also models linear relationships, using classic and factor-based PLS algorithms.

- Models reflective and formative variables, as well as moderating effects.

- Calculates P values, model fit and quality indices, and full collinearity coefficients.

- Calculates indirect effects for paths with 2, 3 etc. segments; as well as total effects.

- Calculates several causality assessment coefficients.

- Provides a number of graphs, including zoomed 2D graphs, and 3D graphs.

At the beginning of the User Manual you will see a list of new features in this version, some of which are listed below together with related YouTube videos. The User Manual has more details on how these new features can be useful in SEM analyses.

- There has been a long and in some instances fairly antagonistic debate among proponents and detractors of the use of Wold’s original PLS algorithms in the context of SEM. This debate has been fueled by one key issue: Wold’s original PLS algorithms do not deal with actual factors, as covariance-based SEM algorithms do; but with composites, which are exact linear combinations of indicators. The new factor-based algorithms provided in this version have been developed specifically to address this perceived limitation of Wold’s original PLS algorithms.

Related YouTube video:
Conduct a Factor-Based PLS-SEM Analysis with WarpPLS
http://youtu.be/PvXuD5COezU

- An extended set of descriptive statistics is now provided for both indicators and latent variables. The descriptive statistics provided include: minimum and maximum values, medians, modes, skewness and excess kurtosis coefficients, as well as results of unimodality and normality tests. These are now complemented by histograms, which can be viewed on the screen and saved as files.

- Often the use of PLS-based SEM methods is justified based on them making no data normality assumptions, but typically without any accompanying test of normality! This is addressed in this version through various outputs of unimodality and normality tests, which are now provided for all indicators and latent variables.

Related YouTube video:
View Skewness and Kurtosis in WarpPLS
http://youtu.be/6p1LXxZR-Vg

- Rocky and smooth 3D graphs can now be viewed with data points excluded. Corresponding graphs with data points included are also available. The 3D graph displays with data points excluded are analogous to those used in the focused 2D graphs. Additionally, users can now incrementally rotate 3D graphs in the following directions: up, down, left, and right.

Related YouTube video:
View Moderating Effects via 3D and 2D Graphs in WarpPLS
http://youtu.be/XEC2a3paJ98

- An extended set of “stable” P value calculation methods is now available to users: Stable1, Stable2, and Stable3. The Stable1 method was the software’s default up until version 4.0, when it was called simply the “stable” method. The Stable2 and Stable3 methods have been developed as alternatives to the Stable1 method that rely on the direct application of exponential smoothing formulas, and that can thus be more easily implemented and tested by methodological researchers.

- Several missing data imputation methods are now available to users: Arithmetic Mean Imputation (the software’s default), Multiple Regression Imputation, Hierarchical Regression Imputation, Stochastic Multiple Regression Imputation, and Stochastic Hierarchical Regression Imputation. Extensive simulations suggest that these methods perform fairly well in the context of PLS-based SEM (including Arithmetic Mean Imputation), even with as much as 30 percent of data missing.

Related YouTube video:
View and Change Missing Data Imputation Settings in WarpPLS
http://youtu.be/uJ9SWhtBObQ

Enjoy!

Minimum sample size in PLS-SEM, regression, and path analyses

Based on Monte Carlo simulations, the minimum sample size in PLS-SEM can be reliably and conservatively estimated based on the inequality below:

N > ( 2.48 / Abs(bm) ) ^ 2

Extensive tests suggest that this also applies to multiple regression, and path analyses. In the latter, only single-indicator variables are included in the model, even though it looks a lot like an SEM model.

The inequality above is discussed in the article titled: "Minimum sample size estimation in PLS‐SEM: The inverse square root and gamma‐exponential methods" (). It refers to the inverse square root method. The gamma‐exponential method, also discussed in the article, is a refinement of the inverse square root method that relies on equations that are much more complex.

In the inequality above, N is the required sample size, and Abs(bm) is the absolute value of the path coefficient with the minimum expected magnitude in the model. This inequality assumes that:

- One-tailed P values are used for hypothesis testing. A previous post discusses this issue in more detail ().

- The threshold for P values is .05. That is, P values should be equal to or lower than .05.

- Effect sizes (ESs), as calculated by WarpPLS, are also used for hypothesis testing ().

- The threshold for ESs is .02. That is, ESs should be equal to or greater than .02.

- Acceptable statistical power is equal to or greater than .8.

- The latent variables in the model are not collinear, when both lateral and vertical collinearity are considered. That is, the full collinearity VIFs calculated by WarpPLS for all latent variables are equal to or lower than 3.3 ().

This inequality highlights the fact that path coefficient strength is a much stronger determinant of statistical power in Monte Carlo simulations than the configuration of the structural model.

The inequality is proposed as an alternative to the widely used (and discredited) "10 times rule". It yields minimum sample sizes that are consistent with Cohen's power tables for multiple regression.

For example, let us say one has a model where the path coefficient with the minimum expected magnitude is .3. Then the required sample size is:

N > ( 2.48 / .3 ) ^ 2 = 68.34

The minimum required sample size is thus:

Nm = 69

The above assumes a pre-analysis minimum sample size estimation, where the path coefficient with the minimum expected magnitude is set prior to the analysis.

A post-analysis minimum sample size estimation, on the other hand, would be based on the results of a full PLS-SEM analysis. Generally pre-analysis estimation is recommended over post-analysis estimation.

The latter, post-analysis estimation, can only confirm that an appropriate sample size was used.

PLS Applications Symposium; 30 May - 1 June 2014; Montreal, Canada

PLS Applications Symposium; 30 May - 1 June 2014; Montreal, Canada
(Abstract submissions accepted: 1 August 2013 - 1 February 2014)

*** 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).

As an emerging method, its users often face challenges in successfully publishing PLS-based research, hence the theme of this year's Symposium: Successfully publishing PLS-based research.

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

Ned Kock, Ph.D.
WarpPLS Developer
http://nedkock.com

How do I conduct a robust path analysis?

What if a researcher has only one measure for each latent variable, and still wants to perform a “robust” analysis where no parametric assumptions (e.g., univariate or multivariate normality) are made beforehand?

This would call for a new robust multivariate analysis approach – a robust path analysis. In it, the variables in the structural model would not be “latent”, and thus other assessments would have to be performed in place of a confirmatory factor analysis.

An article illustrating a robust path analysis with WarpPLS is available. To the best of our knowledge, this is the first published article employing this type of analysis. The full reference, link to full text PDF file, and abstract for the article are available below.

Kock, N., & Gaskins, L. (2014). The mediating role of voice and accountability in the relationship between Internet diffusion and government corruption in Latin America and Sub-Saharan Africa. Information Technology for Development, 20(1), 23-43.

PDF file:

http://www.scriptwarp.com/warppls/pubs/Kock_Gaskins_2014_ITD_NetCorrup.pdf

We examine relationships among Internet diffusion, voice and accountability, and government corruption based on data from 24 Latin American and 23 sub-Saharan African countries from 2006 to 2010. Our study suggests that greater levels of Internet diffusion are associated with greater levels of voice and accountability and that greater levels of voice and accountability are associated with lower levels of government corruption. Also, there seems to be an overall relationship between Internet diffusion and government corruption, which is primarily indirect and mediated by voice and accountability. Our study builds on modernization theory, and employs the method of robust path analysis, implemented through the software WarpPLS. Policy-makers in developing countries aiming at increasing voice and accountability at the national level, and thus the degree to which their citizens participate in the country’s governance, should strongly consider initiatives that broaden Internet access in their countries.

Using data labels to discover moderating effects in PLS-based structural equation modeling

How can one discover moderating effects with data labels? This question is addressed through the article below:

Kock, N. (2014). Using data labels to discover moderating effects in PLS-based structural equation modeling. International Journal of e-Collaboration, 10(4), 1-14.

http://cits.tamiu.edu/kock/pubs/journals/2014JournalIJeC2/Kock_2014_IJeC_UsingDataLabelsMod.pdf

This publication refers to a sample dataset, with data and data labels, illustrating a moderating effect. This dataset is linked below as a .xlsx file. The data was created based on a Monte Carlo simulation.

http://www.scriptwarp.com/warppls/data/Kock_2014_ECollabModStudyData.xlsx

Another approach to discover moderating effects is a full latent growth analysis.

Sometimes the actual inclusion of moderating variables and corresponding links in a model leads to problems; e.g., increases in collinearity levels, and the emergence of instances of Simpson’s paradox. The WarpPLS menu option “Explore full latent growth”, available starting in version 6.0, allows you to completely avoid these problems, and estimate the effects of a latent variable or indicator on all of the links in a model (all at once), without actually including the variable in the model. Moreover, growth in coefficients associated with links among different latent variables and between a latent variable and its indicators, can be estimated; allowing for measurement invariance tests applied to loadings and/or weights.

Related YouTube video:

Explore Full Latent Growth in WarpPLS

http://youtu.be/x_2e8DVyRhE