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

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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!

Multi-group analysis with WarpPLS: Comparing means of two or more groups

Comparing means of two groups is something that behavioral researchers often do. In fact, this is the most widely used quantitative analysis approach. This is also a form of multi-group analysis, where the number of groups is two. Common tests for comparing means are the t and and one-way ANOVA tests.

There is an arguably much better way of doing comparison of means test, with WarpPLS. Follow the steps below. This is a two-group test. Multi-group tests can be done in a similar way, through multiple two-group tests where conditions (i.e., groups) are compared pair by pair.

- Create a dummy variable (G) with numbers associated with each of the two groups - e.g., 0 for one group, and 1 for the other group. This dummy variable should be implemented as a LV with 1 indicator.

- Define your dependent (or criterion) construct (T) as you would normally do; in this case, I think that would be as a reflective LV with 3 indicators.

- Create a link between G and T, with G pointing at T.

- Estimate the model parameters with WarpPLS; this will calculate the beta and P values for the link. The P value is analogous to the P value you would obtain with a t test.

The following article goes into some detail about this procedure, contrasting it with other approaches:

Kock, N. (2013). Using WarpPLS in e-collaboration studies: What if I have only one group and one condition?  International Journal of e-Collaboration, 9(3), 1-12.

This type of WarpPLS test has a number of advantages over a standard t test or a one-way ANOVA test (which are essentially the same thing). For example, it allows for the use of LVs as dependent variables; and it is a robust test, which does not require that the dependent variables be normally distributed.

Multi-group tests, with more than two groups, can also be conducted by assigning different values to each of the groups. The key here is to decide what values to assign to each group. This choice is often somewhat arbitrary in exploratory analyses. The “Save grouped descriptive statistics into a tab-delimited .txt file” option may be helpful in this respect. This is a special option that allows you to save descriptive statistics (means and standard deviations) organized by groups defined based on certain parameters. For more details, see the user manual for WarpPLS, which is available from Warppls.com.

Starting in version 6.0 of WarpPLS, the menu options “Explore multi-group analyses” and “Explore measurement invariance” allow you to conduct analyses where the data is segmented in various groups, all possible combinations of pairs of groups are generated, and each pair of groups is compared. In multi-group analyses normally path coefficients are compared, whereas in measurement invariance assessment the foci of comparison are loadings and/or weights. The grouping variables can be unstandardized indicators, standardized indicators, and labels. These types of analyzes can now also be conducted via the new menu option “Explore full latent growth”, which presents several advantages (as discussed in the WarpPLS User Manual).

Related YouTube videos:

Explore Multi-Group Analyses in WarpPLS

Explore Measurement Invariance in WarpPLS

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.