Testing mediation via indirect effects in PLS-SEM: A social networking site illustration

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

Moqbel, M., Guduru, R., & Harun, A. (2020). Testing mediation via indirect effects in PLS-SEM: A social networking site illustration. Data Analysis Perspectives Journal, 1(3), 1-6.

A link to a PDF file is available ().

Abstract:

Mediation analysis, in the context of structural equation modeling via partial least squares (PLS-SEM), affords a better understanding of the relationships among independent and dependent variables, when the variables seem to not have a definite connection. In this paper, we demonstrate such an analysis in the context of social networking sites, using WarpPLS, a leading PLS-SEM software tool.

Conducting a nonlinear robust path analysis

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

This would call for a new nonlinear robust multivariate analysis approach – a nonlinear robust path analysis. Through this approach the variables in the structural model would not be “latent”, strictly speaking, and thus other assessments would have to be performed in place of a confirmatory factor analysis. That is, without multiple indicators per latent variable measurement, quality assessments must deviate somewhat from what would be used in a traditional structural equation modeling analysis.

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

Kock, N. (2015). Wheat flour versus rice consumption and vascular diseases: Evidence from the China Study II data. Cliodynamics, 6(2), 130–146.

PDF file:

http://escholarship.org/uc/item/7hk1254d

Why does wheat flour consumption appear to be significantly associated with vascular diseases? To answer this question we analyzed data on rice consumption, wheat flour consumption, total calorie consumption, and mortality from vascular diseases obtained from the China Study II dataset. This dataset covers the years of 1983, 1989 and 1993; with data related to biochemistry, diet, lifestyle, and mortality from various diseases in 69 counties in China. Our analyses point at a counterintuitive conclusion: it may not be wheat flour consumption that is the problem, but the culture associated with it, characterized by: decreased levels of physical activity, decreased exposure to sunlight, increased consumption of processed foods, and increased social isolation. Wheat flour consumption may act as a proxy for the extent to which this culture is expressed in a population. The more this culture is expressed, the greater is the prevalence of vascular diseases.

While this is an academic article, I think that the main body of the article is fairly easy to read; which was one of the expectations communicated to us by the Editor and the reviewers. WarpPLS users may find themselves in this same situation – having to prevent more technical statistical material from “spoiling” the reading experience of a non-technical audience. In this case, more technical readers may want to check under “Supporting material”, which is one of the links on the left, where they will find a detailed description of the data used and the results of some specialized statistical tests.

Enjoy!

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.

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!