Possible installation problems and the MATLAB Compiler Runtime

The vast majority of WarpPLS users do not have any installation problems, but some users do. One possible cause is an incompatibility between the MATLAB Compiler Runtime and their computer's operating system setup. This is explored in more detail below.

Another possible cause of installation problems is one or more software applications that interfere with the proper running of WarpPLS. There have been reports from users suggesting that the following software applications may do that: Panda Antivirus, Norton Antivirus, and XLSTAT.

Yet another possible cause of installation problems are security software tools (to stop malware) that prevent users from making modifications in the folders in their computers that store data about programs. Closely aligned with this cause are security restrictions placed on computers by their organizations' IT offices.

The MATLAB Compiler Runtime

The MATLAB Compiler Runtime is for MATLAB programs what the Java Runtime is for Java programs, and what the Microsoft .NET Framework is for .NET-based programs. That is, it is a set of executable modules that are called by executable files compiled using MATLAB.

WarpPLS is an executable file compiled using MATLAB, and thus requires the MATLAB Compiler Runtime (version 7.14) to run properly. Like many other runtime libraries, the MATLAB Compiler Runtime has originally been developed in C and C++.

MATLAB does not have to be installed for WarpPLS to run

The MATLAB Compiler Runtime is provided in the self-extracting executable file used for the installation of WarpPLS. It is free of charge. MATLAB does not have to be installed for WarpPLS to run, only the specific MATLAB Compiler Runtime that accompanies WarpPLS.

In theory, the MATLAB Compiler Runtime should allow for a “compile once, run everywhere” approach to programming. That is, code that uses the MATLAB Compiler Runtime would be developed on one operating system, compiled, and then deployed, together with the MATLAB Compiler Runtime, to computers running any operating system.

This approach works well in theory, but not always in practice. This comment applies not only to MATLAB but also to Java and .NET applications – you are probably well aware of this if you are a Java or .NET programmer.

Seek professional IT support if you are using an organizational computer

It is possible that a specific user’s computer configuration will prevent the proper installation of the MATLAB Compiler Runtime, by blocking certain operating system configuration changes (e.g., Windows registry changes), as a security measure. This is often the case when organizational computers are used.

Also, a user may not have administrator rights on a computer, or have limited administrator/power user rights, which may prevent certain operating system configuration changes necessary for the proper installation of the MATLAB Compiler Runtime. Having professional IT support in this type of scenario is a must.

Here are a few steps to take if you are having problems installing and running WarpPLS on a Windows computer:

1) Run WarpPLS as administrator.

Some users solved their installation problems by simply doing this: Right-clicking on the WarpPLS icon and choosing the option to run it as administrator.

2) Reinstall WarpPLS using the larger file containing the MATLAB Compiler Runtime (approximately 170 MB), choosing the option “Repair”.

There have been reported cases in which users cannot start WarpPLS or move beyond WarpPLS’s first screen. This may happen even if the user has a valid license, with the software behaving as though it is not licensed at all. This may also happen before the user acquires a valid license, while trying to use WarpPLS within the trial license period.

A possible solution here that has worked well in the past is to reinstall WarpPLS using the larger file containing the MATLAB Compiler Runtime. When the MATLAB Compiler Runtime installation software pops up, choose the option “Repair”, and proceed with the full reinstallation.

3) Do the above, but change the folder where the WarpPLS program is installed, choosing a folder that is not in a protected area.

As a possible variation to the above, you may change the folder where the WarpPLS program is installed, choosing a folder that is not in a protected area. For example, you may choose the folder “C:\WarpPLS” or the folder “C:\WarpPLS [version]”. Being outside a protected area prevents certain software, such as antivirus software and malware, from interfering with WarpPLS’s execution.

4) Completely uninstall the MATLAB Compiler Runtime and WarpPLS, disable any antivirus software currently running, reinstall the MATLAB Compiler Runtime and WarpPLS, and then re-enable the antivirus software.

To uninstall the MATLAB Compiler Runtime, follow the following procedure (or a similar procedure, depending on the version of Windows you are using): go the “Control Panel”, click on “Add or Remove Programs” or “Programs and Features”, and uninstall the MATLAB Compiler Runtime.

To uninstall the main software program (i.e., WarpPLS), simply delete the main software installation folder. This folder is usually “C:\Program Files\WarpPLS [version]” or “C:\Program Files (x86)\WarpPLS [version]”, unless you chose a different folder for the main software program during the installation process. Then delete the shortcut created by the software from the desktop.

5) Check the "Program Files" and the "Program Files (x86)" directories (assuming that the MATLAB Compiler Runtime is installed on the C drive), to see if one of the following folders is there.

C:\Program Files\MATLAB\MATLAB Compiler Runtime\v714\runtime\win32

C:\Program Files (x86)\MATLAB\MATLAB Compiler Runtime\v714\runtime\win32

If not, make sure that you are logged into your computer with full administrator rights, and reinstall the MATLAB Compiler Runtime. You can do that running the self-installing .exe file (approximately 170 MB) for WarpPLS, which includes the MATLAB Compiler Runtime. Or, contact your local IT support, and ask them to help you do so.

6) Go to the Command Prompt and type “PATH”, to see if one of the following paths shows on the list provided.

C:\Program Files\MATLAB\MATLAB Compiler Runtime\v714\runtime\win32

C:\Program Files (x86)\MATLAB\MATLAB Compiler Runtime\v714\runtime\win32

If not, on the Command Prompt, type one of the following commands, depending on the folder in which the MATLAB Compiler Runtime is installed:

set PATH=C:\Program Files\MATLAB\MATLAB Compiler Runtime\v714\runtime\win32;%PATH%

set PATH=C:\Program Files (x86)\MATLAB\MATLAB Compiler Runtime\v714\runtime\win32;%PATH%

Then type “PATH” again, and make sure that the new path has been added. This will change the Windows registry; a minor and pretty harmless change. If you are concerned about making registry changes yourself, or cannot do that due to limited rights or any other reason, please contact your local IT support, and ask them to help you do so.

7) Try to install WarpPLS on a different computer, and see if it runs well there.

This last step is annoying but important because there are certain computer-specific configuration setups, or even malware allowed in by those setups, that may prevent the MATLAB Compiler Runtime from properly installing or executing. This is rare, but does happen sometimes. Comparing computers can help solve problems like these.

If you can install and run WarpPLS on one computer, but not on another, there may be a computer configuration or malware problem that is preventing you from doing so. If you have access to good-quality local IT support, you should contact it, and ask them to help you identify and solve the problem.

A thank you note to the participants in the 2022 PLS Applications Symposium

This is just a thank you note to those who participated, either as presenters or members of the audience, in the 2022 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 and the Conference's social event.

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, probably in the first half of April as well.

Thank you and best regards to all!

-----------------------------------------------------------
Ned Kock
Symposium Chair
https://plsas.net/

WarpPLS 8.0 upgraded to stable: Logistic regression, full latent growth graphs, HTMT2 ratios, and more!

Dear colleagues:

Version 8.0 of WarpPLS is now available as a stable version. You can download and install it for a free trial from:

https://warppls.com

Each new version of the software incorporates features that aim at achieving an important end goal: to allow users to employ SEM to conduct any of the major statistical tests; from relatively simple tests such as comparisons of means, to more sophisticated ones such as nonlinear SEM tests employing logistic regression. Among the community of users of this software, there are very sophisticated SEM experts that constantly challenge us to implement new data analysis features, as well as to make the existing features as easy to use as possible. Because of the constant input from our users, including those who are very knowledgeable about SEM, the software now arguably provides the most extensive set of features of any SEM software. We hope to continue in this path as the SEM field evolves. Below we outline new features added to the current version of the software.

Logistic regression variables. The menu option “Explore logistic regression” now allows you to create a logistic regression variable as a new indicator that has both unstandardized and standardized values. Logistic regression is normally used to convert an endogenous variable on a non-ratio scale (e.g., dichotomous) into a variable reflecting probabilities. You need to choose the variable to be converted, which should be an endogenous variable, and its predictors. The new logistic regression variable is meant to be used as a replacement for the endogenous variable on which it is based. Two algorithms are available: probit and logit. The former is recommended for dichotomous variables; the latter for non-ratio variables where the number of different values (a.k.a. “distinct observations”) is greater than 2 but still significantly smaller than the sample size; e.g., 10 different values over a sample size of 100. The unstandardized values of a logistic regression variable are probabilities; going from 0 to 1. Since a logistic regression variable can be severely collinear with its predictors, you can set a local full collinearity VIF cap for the logistic regression variable. Predictor-criterion collinearity, or lateral collinearity (Kock & Lynn, 2012), is rarely assessed or controlled in classic logistic regression algorithms.

Absolute and relative variation measures. You can now view the number of different values (a.k.a. “distinct observations”) for all indicators and latent variables, as well as the ratio between the number of different values and sample size. The first is an absolute and the second a relative variation measure. These are available under the menu options “View or save correlations and descriptive statistics for indicators” and “View latent variable coefficients”, respectively. These measures can help inform decisions about whether to use logistic regression, particularly in connection with endogenous latent variables. If the number of different values is significantly smaller than the sample size (e.g., 10 different values over a sample size of 100) for an endogenous latent variable, that means that a new logistic regression variable could be created and used as a replacement for the endogenous variable. If several predictors are available, the new logistic regression variable will incorporate more variation than the endogenous variable on which it is based, which will typically be reflected in larger coefficients of association (e.g., path coefficients) when the logistic regression variable is used in the model.

Graphs for full latent growth coefficients. You can now view several graphs for each of the full latent growth coefficients provided under the menu option “Explore full latent growth”. Full latent growth coefficients have a number of applications, such as: moderating effects analyses, nonlinearity tests, multi-group and measurement invariance tests, and the assessment of moderated mediation effects. Each of the graphs is made up of several plots, which refer to changes in the coefficients selected (e.g., path coefficients) for the relationship between the variables shown in the X and Y axes, as the latent growth variable goes from low to high. The following graph menu options are available: “Full sample splits (megaphones)”, “Partial sub-samples splits (megaphones)”, “Full sample splits (bars)”, “Partial sub-samples splits (bars)”, “Full sample splits (lines)”, and “Partial sub-samples splits (lines)”.

HTMT2 ratios. The sub-option “'Discriminant validity coefficients (extended set)”, under the menu option “Explore additional coefficients and indices”, now allows you to inspect the newest version of the set of heterotrait-monotrait (HTMT) ratios calculated by the software. These have been dubbed HTMT2 ratios. The HTMT and HTMT2 ratios have been proposed for discriminant validity assessment, particularly in the context of composite-based SEM via classic PLS algorithms; as opposed to factor-based SEM via modern algorithms that estimate factors (which have been available from this software for quite some time now). Discriminant validity is a measure of the quality of a measurement instrument; the instrument itself is typically a set of question-statements. A measurement instrument has good discriminant validity if the question-statements (or other measures) associated with each latent variable are not confused by the respondents, in terms of their meaning, with the question-statements associated with other latent variables.

Incremental interface improvement. This is conducted in each new version of the software. At several points the code has been modified so that the user interface experiences are improved. This has led in several cases to what appears to be a smoother flow through the several steps and procedures guided by the user interface. Several elements of the graphical user interface, such as screens and warning messages, have been optimized so that users can perform SEM analysis tasks with only a few clicks – and in a straightforward fashion. Nevertheless, care is always taken to ensure that the user interfaces do not change too much, otherwise users would have to re-learn how to use the interface whenever a new version is released.

Incremental code optimization. This is also conducted in each new version of the software. At several points the code has been optimized for speed, stability, and coefficient estimation precision. In some cases, the optimization has led to lesser propagation of sampling error, making the software reach accurate results at lower sample sizes – that is, increasing the statistical efficiency of the software. These incremental code optimization changes have led to incremental gains in speed even as new features have been added. More often than not, new features require additional computational steps and often complex calculations, mostly to generate internal checks and coefficients that were not available before.

Take a look at the following videos, which have been created using this new version. They illustrate the new features outlined above.

Explore Logistic Regression in WarpPLS

View the Number of Different Values for Variables in WarpPLS

View Full Latent Growth Graphs in WarpPLS

Holistic Measurement Model Assessment in SEM with WarpPLS

Reduce Common Structural Variation with WarpPLS

Assessing Multiple Reciprocal Relationships in SEM with WarpPLS

Choose the Correlation Signs for Anchor Variables in a Multilevel Analysis with WarpPLS

Using Logistic Regression in PLS-SEM with Composites and Factors

Conducting a What-if Analysis in PLS-SEM with Analytic Composites

Reference:

Kock, N., & Lynn, G. (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.

Best regards to all!

Minimum sample size estimation in SEM: Contrasting results for models using composites and factors

The article below discusses how one can conduct a minimum sample size estimation, contrasting results for models using composites and factors, in the context of structural equation modeling via partial least squares (PLS-SEM).

Ezeugwa, B., Talukder, M. F., Amin, M. R., Hossain, S. I., & Arslan, F. (2022). Minimum sample size estimation in SEM: Contrasting results for models using composites and factors. Data Analysis Perspectives Journal, 3(4), 1-7.

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

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

Abstract:

Estimating the minimum required sample size is an essential issue for studies that use structural equation modeling employing partial least squares (PLS-SEM). Several PLS-SEM-based studies ignore this critical step or use simple techniques, which lead to inaccurate sample size estimations. This paper illustrates two effective heuristic methods to estimate the minimum required sample size using WarpPLS, a leading PLS-SEM software tool.

Best regards to all!

Testing and controlling for endogeneity in PLS-SEM with stochastic instrumental variables

The article below discusses a procedure that can be used to simultaneously test and control for endogeneity, in the context of structural equation modeling via partial least squares (PLS-SEM).

Kock, N. (2022). Testing and controlling for endogeneity in PLS-SEM with stochastic instrumental variables. Data Analysis Perspectives Journal, 3(3), 1-6.

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

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

Abstract:

We discuss a procedure that can be used to simultaneously test and control for endogeneity in models analyzed with structural equation modeling via partial least squares (PLS-SEM). It relies on the creation of stochastic instrumental variables for endogenous latent variables, and their use as control variables. The procedure can be seen as an implementation of the Durbin–Wu–Hausman test, often referred to as the Hausman test, with stochastic instrumental variables. It can also be seen as a generalization of the two-stage least squares procedure. We illustrate the procedure with WarpPLS, a leading PLS-SEM tool.

Best regards to all!

WarpPLS 8.0 beta now available: Logistic regression, full latent growth graphs, HTMT2 ratios, and more!

Dear colleagues:

Version 8.0 of WarpPLS is now available, as a beta version. You can download and install it for a free trial from:

https://warppls.com

Each new version of the software incorporates features that aim at achieving an important end goal: to allow users to employ SEM to conduct any of the major statistical tests; from relatively simple tests such as comparisons of means, to more sophisticated ones such as nonlinear SEM tests employing logistic regression. Among the community of users of this software, there are very sophisticated SEM experts that constantly challenge us to implement new data analysis features, as well as to make the existing features as easy to use as possible. Because of the constant input from our users, including those who are very knowledgeable about SEM, the software now arguably provides the most extensive set of features of any SEM software. We hope to continue in this path as the SEM field evolves. Below we outline new features added to the current version of the software.

Logistic regression variables. The menu option “Explore logistic regression” now allows you to create a logistic regression variable as a new indicator that has both unstandardized and standardized values. Logistic regression is normally used to convert an endogenous variable on a non-ratio scale (e.g., dichotomous) into a variable reflecting probabilities. You need to choose the variable to be converted, which should be an endogenous variable, and its predictors. The new logistic regression variable is meant to be used as a replacement for the endogenous variable on which it is based. Two algorithms are available: probit and logit. The former is recommended for dichotomous variables; the latter for non-ratio variables where the number of different values (a.k.a. “distinct observations”) is greater than 2 but still significantly smaller than the sample size; e.g., 10 different values over a sample size of 100. The unstandardized values of a logistic regression variable are probabilities; going from 0 to 1. Since a logistic regression variable can be severely collinear with its predictors, you can set a local full collinearity VIF cap for the logistic regression variable. Predictor-criterion collinearity, or lateral collinearity (Kock & Lynn, 2012), is rarely assessed or controlled in classic logistic regression algorithms.

Absolute and relative variation measures. You can now view the number of different values (a.k.a. “distinct observations”) for all indicators and latent variables, as well as the ratio between the number of different values and sample size. The first is an absolute and the second a relative variation measure. These are available under the menu options “View or save correlations and descriptive statistics for indicators” and “View latent variable coefficients”, respectively. These measures can help inform decisions about whether to use logistic regression, particularly in connection with endogenous latent variables. If the number of different values is significantly smaller than the sample size (e.g., 10 different values over a sample size of 100) for an endogenous latent variable, that means that a new logistic regression variable could be created and used as a replacement for the endogenous variable. If several predictors are available, the new logistic regression variable will incorporate more variation than the endogenous variable on which it is based, which will typically be reflected in larger coefficients of association (e.g., path coefficients) when the logistic regression variable is used in the model.

Graphs for full latent growth coefficients. You can now view several graphs for each of the full latent growth coefficients provided under the menu option “Explore full latent growth”. Full latent growth coefficients have a number of applications, such as: moderating effects analyses, nonlinearity tests, multi-group and measurement invariance tests, and the assessment of moderated mediation effects. Each of the graphs is made up of several plots, which refer to changes in the coefficients selected (e.g., path coefficients) for the relationship between the variables shown in the X and Y axes, as the latent growth variable goes from low to high. The following graph menu options are available: “Full sample splits (megaphones)”, “Partial sub-samples splits (megaphones)”, “Full sample splits (bars)”, “Partial sub-samples splits (bars)”, “Full sample splits (lines)”, and “Partial sub-samples splits (lines)”.

HTMT2 ratios. The sub-option “'Discriminant validity coefficients (extended set)”, under the menu option “Explore additional coefficients and indices”, now allows you to inspect the newest version of the set of heterotrait-monotrait (HTMT) ratios calculated by the software. These have been dubbed HTMT2 ratios. The HTMT and HTMT2 ratios have been proposed for discriminant validity assessment, particularly in the context of composite-based SEM via classic PLS algorithms; as opposed to factor-based SEM via modern algorithms that estimate factors (which have been available from this software for quite some time now). Discriminant validity is a measure of the quality of a measurement instrument; the instrument itself is typically a set of question-statements. A measurement instrument has good discriminant validity if the question-statements (or other measures) associated with each latent variable are not confused by the respondents, in terms of their meaning, with the question-statements associated with other latent variables.

Incremental interface improvement. This is conducted in each new version of the software. At several points the code has been modified so that the user interface experiences are improved. This has led in several cases to what appears to be a smoother flow through the several steps and procedures guided by the user interface. Several elements of the graphical user interface, such as screens and warning messages, have been optimized so that users can perform SEM analysis tasks with only a few clicks – and in a straightforward fashion. Nevertheless, care is always taken to ensure that the user interfaces do not change too much, otherwise users would have to re-learn how to use the interface whenever a new version is released.

Incremental code optimization. This is also conducted in each new version of the software. At several points the code has been optimized for speed, stability, and coefficient estimation precision. In some cases, the optimization has led to lesser propagation of sampling error, making the software reach accurate results at lower sample sizes – that is, increasing the statistical efficiency of the software. These incremental code optimization changes have led to incremental gains in speed even as new features have been added. More often than not, new features require additional computational steps and often complex calculations, mostly to generate internal checks and coefficients that were not available before.

Take a look at the following videos, which have been created using this new version. They illustrate the new features outlined above.

Explore Logistic Regression in WarpPLS

View the Number of Different Values for Variables in WarpPLS

View Full Latent Growth Graphs in WarpPLS

Holistic Measurement Model Assessment in SEM with WarpPLS

Reduce Common Structural Variation with WarpPLS

Assessing Multiple Reciprocal Relationships in SEM with WarpPLS

Choose the Correlation Signs for Anchor Variables in a Multilevel Analysis with WarpPLS

Using Logistic Regression in PLS-SEM with Composites and Factors

Conducting a What-if Analysis in PLS-SEM with Analytic Composites

Reference:

Kock, N., & Lynn, G. (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.

Best regards to all!

Discriminant validity assessment in PLS-SEM: A comprehensive composite-based approach

The article below puts forth a comprehensive composite-based perspective on how one can conduct discriminant validity assessment, in the context of structural equation modeling via partial least squares (PLS-SEM).

Rasoolimanesh, S. M. (2022). Discriminant validity assessment in PLS-SEM: A comprehensive composite-based approach. Data Analysis Perspectives Journal, 3(2), 1-8.

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

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

Abstract:

This paper aims to discuss modern approaches to assess discriminant validity in the context of structural equation modeling via partial least squares (PLS-SEM). It illustrates the application of these approaches using the WarpPLS 7.0 software. The Fornell-Larcker criterion, crossloadings method, heterotrait-monotrait (HTMT) ratio, and full collinearity test have been discussed in this paper. A step-by-step guide is provided to assess discriminant validity using these four tests in WarpPLS 7.0. The first three criteria are applicable for reflective constructs, while the full collinearity test can be applied for both reflective and formative constructs. In different social science disciplines, a combination of reflective and formative constructs is a common practice, therefore reporting the full collinearity test for the assessment of discriminant validity can be an advantage.

Best regards to all!