On the validity assessment of formative measurement models in PLS-SEM

The article below discusses how one can conduct a validity assessment of formative measurement models, in the context of structural equation modeling via partial least squares (PLS-SEM).

Amora, J. T. (2023). On the validity assessment of formative measurement models in PLS-SEM. Data Analysis Perspectives Journal, 4(2), 1-7.

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

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

Abstract:

Structural equation modeling via partial least squares (PLS-SEM) is the preferred approach when a research model includes formative measurement models. In this paper, the validity assessment of first-order and higher-order measurement models is illustrated using real data employing the WarpPLS, a prominent software tool for PLS-SEM.

Best regards to all!

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!

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.

Dealing with country-specific number punctuation systems

WarpPLS users in countries that adopt number punctuation systems different from that adopted in the USA may have problems when using Excel to manipulate WarpPLS files.

For instance, in Brazil a comma is used to separate the integer from the fractional part of a real number (e.g., 1,431), whereas in the USA a period is used for that purpose (e.g., 1.431).

Because of that, a coefficient calculated by WarpPLS and exported into a .txt file as “1.431” may be read by a Brazilian version of Excel as one thousand four hundred and thirty-one, and not as one plus the 431/1000 fraction.

This tends to happen in certain types of analyses, such as second order latent variable analyses, where WarpPLS outputs are used as inputs after manipulation with country-specific versions of Excel.

A simple way to solve this problem is to use Excel, Notepad, or another simple text editing tool and replace the offending punctuation items, all points with commas (or vice-versa) for example, before using the inputs for other purposes.

Second order latent variables in WarpPLS: YouTube videos by Jaime León

The blog post below refers to a procedure employed with earlier versions of WarpPLS. For a more recent, and less time-consuming, approach see the video linked immediately below. The video shows how to create and use second (and higher) order latent variables with WarpPLS.

http://youtu.be/bkO6YoRK8Zg

*** 

The YouTube videos below have been created by WarpPLS user and blog commenter Jaime León. They illustrate how steps 1 and 2, described in this post, can be implemented in WarpPLS. The goal of those steps is to use second order latent variables (LVs) in an SEM analysis. Latent variable (LV) scores are generated, saved, and then used in a subsequent SEM analysis.

Step 1: YouTube video 1.

Step 2: YouTube video 2.

In the first video Jaime includes only LVs in the model, without any links among them, and then runs the SEM analysis. This generates the LV scores for the LVs, which Jaime then saves into a .txt file. The LV scores generated are then combined with indicators from the original dataset.

Note that Jaime does not set the LVs in the first video as formative before generating the scores. That is okay if the LVs are reflective; that is, if the indicators of the LVs are highly correlated. (In reflective LVs the loadings are expected to be all high, ideally greater than .7, and significant.) If not, then the LVs should be set as formative.

Also, note that Jaime combined the LV scores in standardized format with indicator data from the original dataset, which were not standardized. That is fine because WarpPLS always standardizes the raw data before proceeding to an SEM analysis. Standardized data, when used as input, will not be affected by standardization (since they are already standardized).

In the second video Jaime creates a model with new LVs, some of which include the previously generated LV scores as indicators. These are frequently referred to as second order LVs. (Although sometimes the original LVs, shown in the first video, are the ones called second order LVs.) Jaime then builds a model by creating several direct links among the LVs.

Cool example, with a Bob Marley song in the background; thanks Jaime!

Using second order latent variables in WarpPLS

The blog post below refers to a procedure employed with earlier versions of WarpPLS. For a more recent, and less time-consuming, approach see the video linked immediately below. The video shows how to create and use second (and higher) order latent variables with WarpPLS.

http://youtu.be/bkO6YoRK8Zg

***

Second order latent variables (LVs) can be implemented in WarpPLS 1.0 through two steps. These steps are referred to as Step 1 and Step 2 in the paragraphs below. Higher order LVs can also be implemented, following a similar procedure, but with additional steps.

With second order LVs, a set of LV scores are used as indicators of a LV. Often second order LVs are decompositions of a formative LV into a few reflective LVs. The scores of the component reflective LVs are used as indicators of the original formative LV.

In Step 1, you will create models that relate LVs to their indicators. Only the LVs and their indicators should be included. No links between LVs should be created. This will allow you to calculate the LV scores for the LVs, based on the indicators. You will then save the LV scores using the option “Save factor scores into a tab-delimited .txt file”, available from the “Save” option of the “View and save results” window menu.

In Step 2, you will create a new model where the saved LV scores are indicators of a new LV. This LV is usually called the second order LV, although sometimes the indicators (component LVs) are referred to as second order LVs. The rest of the data will be the same. Note that you will have to create and read the raw data used in the SEM analysis again, for this second step.