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!

Multilevel analyses in PLS-SEM: Article, video, and sample dataset

The article below explains how one can conduct a multilevel analysis, in the context of structural equation modeling via partial least squares (PLS-SEM).

Kock (2020). Multilevel analyses in PLS-SEM: An anchor-factorial with variation diffusion approach. Data Analysis Perspectives Journal, 1(2), 1-6.

A link to a PDF file is available ().

Abstract:

A multilevel analysis, in the context of structural equation modeling via partial least squares (PLS-SEM), can be seen as an analysis in which: (a) data is collected at the individual level from multiple groups, and (b) group membership is expected to influence data analysis results. In this paper we illustrate such an analysis employing WarpPLS, a leading PLS-SEM software tool. The analysis employs an anchor-factorial with variation diffusion approach.

The short video linked below provides an overview on how to conduct multilevel analyses in PLS-SEM.

https://youtu.be/pNXI1Cz-Qkk

Finally, the site below provides a sample dataset: "Job performance in three companies dataset". This dataset is available under the "Resources" area.

https://warppls.com

Enjoy!

Multilevel analyses in PLS-SEM

Dear colleagues:

The short video linked below provides an overview on how to conduct multilevel analyses in PLS-SEM.

https://youtu.be/pNXI1Cz-Qkk

Enjoy!

Using WarpPLS in E-Collaboration Studies: Mediating Effects, Control and Second Order Variables, and Algorithm Choices

A new article discussing WarpPLS is available. The article is titled “Using WarpPLS in E-Collaboration Studies: Mediating Effects, Control and Second Order Variables, and Algorithm Choices”. It has been recently published in the International Journal of e-Collaboration. A full text version of the article is available here as a PDF file. Below is the abstract of the article.

This is a follow-up on two previous articles on WarpPLS and e-collaboration. The first discussed the five main steps through which a variance-based nonlinear structural equation modeling analysis could be conducted with the software WarpPLS (Kock, 2010b). The second covered specific features related to grouped descriptive statistics, viewing and changing analysis algorithm and resampling settings, and viewing and saving various results (Kock, 2011). This and the previous articles use data from the same e-collaboration study as a basis for the discussion of important WarpPLS features. Unlike the previous articles, the focus here is on a brief discussion of more advanced issues, such as: testing the significance of mediating effects, including control variables in an analysis, using second order latent variables, choosing the right warping algorithm, and using bootstrapping and jackknifing in combination.

How do I control for the effects of one or more demographic variables in an SEM analysis?

As part of an SEM analysis using WarpPLS, a researcher may want to control for the effects of one ore more variables. This is typically the case with what are called “demographic variables”, or variables that measure attributes of a given unit of analysis that are (usually) not expected to influence the results of the SEM analysis.

For example, let us assume that one wants to assess the effect of a technology, whose intensity of use is measured by a latent variable T, on a behavioral variable measured by B. The unit of analysis for B is the individual user; that is, each row in the dataset refers to an individual user of the technology. The researcher hypothesizes that the association between T and B is significant, so a direct link between T and B is included in the model.

If the researcher wants to control for age (A) and gender (G), which have also been collected for each individual, in relation to B, all that is needed is to include the variables A and G in the model, with direct links pointing at B. No hypotheses are made. For that to work, gender (G) has to be included in the dataset as a numeric variable. For example, the gender "male" may be replaced with 1 and "female" with 2, in which case the variable G will essentially measure the "degree of femaleness" of each individual. Sounds odd, but works.

After the analysis is conducted, let us assume that the path coefficient between T and B is found to be statistically significant, with the variables A and G included in the model as described above. In this case, the researcher can say that the association between T and B is significant, “regardless of A and G” or “when the effects of A and G are controlled for”.

In other words, the technology (T) affects behavior (B) in the hypothesized way regardless of age (A) and gender (B). This conclusion would remain the same whether the path coefficients between A and/or G and B were significant, because the focus of the analysis is on B, the main dependent variable of the model.

The discussion above is expanded in the publication below, which also contains a graphical representation of a model including control variables.

Kock, N. (2011). Using WarpPLS in e-collaboration studies: Mediating effects, control and second order variables, and algorithm choices. International Journal of e-Collaboration, 7(3), 1-13.

http://www.scriptwarp.com/warppls/pubs/Kock_2011_IJeC_WarpPLSEcollab3.pdf

Some special considerations and related analysis decisions usually have to be made in more complex models, with multiple endogenous variables, and also regarding the fit indices.