Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations

A new article discussing methodological issues based on WarpPLS is available. The article is titled “Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations”. It has been recently published in the Journal of the Association for Information Systems. A full text version of the article is available here as a PDF file. Below is the abstract of the article.

Variance-based structural equation modeling is extensively used in information systems research, and many related findings may have been distorted by hidden collinearity. This is a problem that may extend to multivariate analyses, in general, in the field of information systems as well as in many other fields. In multivariate analyses, collinearity is usually assessed as a predictor-predictor relationship phenomenon, where two or more predictors are checked for redundancy. This type of assessment addresses vertical, or “classic”, collinearity. However, another type of collinearity may also exist, here called “lateral” collinearity. It refers to predictor-criterion collinearity. Lateral collinearity problems are exemplified based on an illustrative variance-based structural equation modeling analysis. The analysis employs WarpPLS 2.0, with the results double-checked with other statistical analysis software tools. It is shown that standard validity and reliability tests do not properly capture lateral collinearity. A new approach for the assessment of both vertical and lateral collinearity in variance-based structural equation modeling is proposed and demonstrated in the context of the illustrative analysis.

View indirect and total effects in WarpPLS: YouTube video

A new YouTube video for WarpPLS is available; please see link below.

http://youtu.be/D9m4K_fv2vI

The video shows how to view and interpret indirect and total effects, as well as various related coefficients s (e.g., P values), calculated through a structural equation modeling (SEM) analysis using the software WarpPLS.

Enjoy!

Create and use second order latent variables in WarpPLS: YouTube video

A new YouTube video for WarpPLS is available; please see link below.

http://youtu.be/bkO6YoRK8Zg

The video shows how to create and use second (and higher) order latent variables with the structural equation modeling (SEM) analysis software WarpPLS.

Enjoy!

Simpson’s paradox and unexpected results

The algorithms used in version 3.0 and later versions of WarpPLS have been revised so as to pick up instances of what is known as “Simpson’s paradox”. As a result, there may be changes in some coefficients and P values, when compared with previous versions.

Often the P value of the ARS fit index will go up, if instances of Simpson’s paradox are present in the model.

Simpson’s paradox is characterized by the path coefficient and correlation for a pair of variables having different signs. In this situation, the contribution of a predictor variable to the explained variance of the criterion variable in a latent variable block is negative.

In other words, if the predictor latent variable were to be removed from the block, the R-squared for the criterion latent variable would go up. A similar effect would be observed if the direction of the causality was reversed.

One widely held interpretation is that Simpson’s paradox could be an indication that the direction of a hypothesized relationship is reversed, or that the hypothesized relationship is nonsensical/improbable.

In the context of WarpPLS analyses, this is more likely to occur when nonlinear algorithms are used and/or full collineary VIFs are high, but may also occur under other conditions.

Version 3.0 of WarpPLS is now available!

Version 3.0 of WarpPLS is now available! You can download and install it for a free 90-day 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:

- Version 2.0 users can use the same license information that they already have; it will work for version 3.0 for the remainder of their license periods.

- Project files generated with previous versions are automatically converted to version 3.0 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 version 2.0. Therefore, if you already have WarpPLS 2.0 installed on your computer, you should uncheck the Runtime component on the installer (i.e., the self-installing .exe file). The same Runtime cannot be installed twice on the same computer.

WarpPLS is a powerful PLS-based structural equation modeling (SEM) software. Since its first release in 2009, its user base has grown steadily, with more than 5,000 users worldwide today.

Some of its most distinguishing features are the following:

- It is 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 a standard PLS regression algorithm.

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

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

Below is a list of new features in this version. The User Manual has more details on how these new features can be useful in SEM analyses.

- Addition of latent variables as indicators. Users now have the option of adding latent variable scores to the set of standardized indicators used in an SEM analysis.

- Blindfolding. Users now have the option of using a third resampling algorithm, namely blindfolding, in addition to bootstrapping and jackknifing.

- Effect sizes. Cohen’s f-squared effect size coefficients are now calculated and shown for all path coefficients.

- Estimated collinearity. Collinearity is now estimated before the SEM analysis is run. When collinearity appears to be too high, users are warned about it.

- Full collinearity VIFs. VIFs are now shown for all latent variables, separately from the VIFs calculated for predictor latent variables in individual latent variable blocks.

- Indirect and total effects. Indirect and total effects are now calculated and shown, together with the corresponding P values, standard errors, and effect sizes.

- P values for all weights and loadings. P values are now shown for all weights and loadings, including those associated with indicators that make up moderating variables.

- Predictive validity. Stone-Geisser Q-squared coefficients are now calculated and shown for each endogenous variable in an SEM model.

- Ranked data. Users can now select an option to conduct their analyses with only ranked data, whereby all the data is automatically ranked prior to the SEM analysis (the original data is retained in unranked format).

- Restricted ranges. Users can now run their analyses with subsamples defined by a range restriction variable, which may be standardized or unstandardized.

- Standard errors for all weights and loadings. Standard errors are now shown for all loadings and weights.

- VIFs for all indicators. VIFs are now shown for all indicators, including those associated with moderating latent variables.

Enjoy!

Exploring free questionnaire data with anchor variables: An illustration based on a study of IT in healthcare

A new article discussing methodological issues based on WarpPLS is available. The article is titled “Exploring free questionnaire data with anchor variables: An illustration based on a study of IT in healthcare”. It has been recently published in the International Journal of Healthcare Information Systems and Informatics. A full text version of the article is available here as a PDF file. Below is the abstract of the article.

This paper makes an important methodological contribution regarding the use of free questionnaires, illustrated through a study that shows that a healthcare professional’s propensity to use electronic communication technologies creates opportunities for interaction with other professionals, which would not otherwise be possible only via face-to-face interaction. This in turn appears to increase mutual trust, and eventually improve the quality of group outcomes. Free questionnaires are often used by healthcare information management researchers. They yield datasets without clear associations between constructs and related indicators. If such associations exist, they must first be uncovered so that indicators can be grouped within latent variables referring to constructs, and structural equation modeling analyses be conducted. A novel methodological contribution is made here through the proposal of an anchor variable approach to the analysis of free questionnaires. Unlike exploratory factor analyses, the approach relies on the researcher’s semantic knowledge about the variables stemming from a free questionnaire. The use of the approach is demonstrated using the multivariate statistical analysis software WarpPLS 2.0. The study leads to a measurement model that passes comprehensive validity, reliability, and collinearity tests. It also appears to yield practically relevant and meaningful results.

New PLS-based SEM email distribution list

A new email distribution list is available for those who share a common interest in partial least squares (PLS) regression and its use in structural equation modeling (SEM). To check it out click here.