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

WarpPLS 4.0 upgraded to stable

Dear colleagues:

Version 4.0 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/HjSknv

Enjoy!

WarpPLS 4.0 now available: 3D graphs, new fit indices, causality assessment coefficients, and more!

Dear colleagues:

Version 4.0 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 4.0 for the remainder of their license periods.

- Project files generated with previous versions are automatically converted to version 4.0 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 2.0-3.0. 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 5,000 users in over 33 countries.

Some of its most distinguishing features are the following:

- It is very easy to use, with a step-by-step user interface guide.

- It identifies nonlinear relationships, and estimates path coefficients accordingly.

- It also models linear relationships, using standard PLS algorithms.

- It models reflective and formative variables, as well as moderating effects.

- It calculates P values, model fit indices, and collinearity estimates.

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

- Users can now set inner and outer model algorithms separately, and are also allowed to set inner model algorithms for individual paths.

- New causality assessment coefficients are now reported, which can be used in the assessment of the plausibility and direction of hypothesized cause-effect relationships.

- Seven new model fit and quality indices have been added to the three previously available, bringing the total number of indices to ten.

- Many new graphs and related features are now available, including 3D graphs. Both multivariate and bivariate relationship graphs are now provided, for linear and nonlinear relationships, using standardized and unstandardized scales.

- Users can now segment curves based on increments in the first derivative of the predictor latent variables on each of their criteria latent variables. This provides an alternative to data segmentation approaches such as FIMIX-PLS, without any reduction in sample size.

Enjoy!

Using WarpPLS in E-Collaboration Studies: What if I Have Only One Group and One Condition?

A new article discussing methodological issues based on WarpPLS is available. The article is titled “Using WarpPLS in E-Collaboration Studies: What if I Have Only One Group and One Condition?” A full text version of the article is available here as a PDF file. Below is the abstract of the article.

What if a researcher obtains empirical data by asking questions to gauge the effect of an e-collaboration technology on task performance, but does not obtain data on the extent to which the e-collaboration technology is used? This characterizes what is referred to here as a scenario with one group and one condition, where the researcher is essentially left with only one column of data to be analyzed. When this happens, often researchers do not know how to analyze the data, or analyze the data making incorrect assumptions and using unsuitable techniques. Some of WarpPLS’s features make it particularly useful in this type of scenario, such as its support for small samples and the use of data that does not meet parametric assumptions. The main goal of this paper is to help e-collaboration researchers use WarpPLS to analyze data in this type of scenario, where only one group and one condition are available. Two other scenarios are also discussed – a typical scenario, and a scenario with one group and two before-after technology introduction conditions. While the focus here is on e-collaboration, the recommendations apply to many other fields.

Multi-group analyses with WarpPLS: The pooled standard error and Satterthwaite methods (and more)

The WarpPLS menu options “Explore multi-group analyses” and “Explore measurement invariance” allow you to conduct analyses where the data is segmented in various groups, all possible combinations of pairs of groups are generated, and each pair of groups is compared. In multi-group analyses normally path coefficients are compared, whereas in measurement invariance assessment the foci of comparison are loadings and/or weights. The grouping variables can be unstandardized indicators, standardized indicators, and labels. These types of analyzes can also be conducted via the menu option “Explore full latent growth”, which presents several advantages (as discussed in the WarpPLS User Manual).

Related YouTube videos:

Explore Multi-Group Analyses in WarpPLS
https://youtu.be/m2VKQGET-K8

Explore Measurement Invariance in WarpPLS
https://youtu.be/29VqsAjhzqQ

I have also been asked how the standard errors reported by WarpPLS can be used in manual multi-group analyses (i.e., not automated) using the pooled standard error and Satterthwaite methods. These issues, as well as other related issues, are now addressed in one single publication:

Kock, N. (2014). Advanced mediating effects tests, multi-group analyses, and measurement model assessments in PLS-based SEM. International Journal of e-Collaboration, 10(3), 1-13.

http://www.scriptwarp.com/warppls/pubs/Kock_2014_UseSEsESsLoadsWeightsSEM.pdf

This publication includes all of the equations used, and also addresses other tests, such as a test of mediating effects using the Sobel method. It is also a good reference for the automated approach employed in WarpPLS.

While using the WarpPLS menu options “Explore multi-group analyses” and “Explore measurement invariance” is recommended (and much less time-consuming), revised Excel spreadsheets are available from www.warppls.com to partially automate the calculations for mediating effects tests and multi-group analyses, respectively:

http://www.scriptwarp.com/warppls/rscs/Kock_2013_MediationSobel.xls

http://www.scriptwarp.com/warppls/rscs/Kock_2013_MultiGroup.xls