In the second video Jaime creates a model with new LVs, some of which include the previously generated LV scores as indicators. Standardized data, when used as input, will not be affected by standardization (since they are already standardized). That is fine because WarpPLS always standardizes the raw data before proceeding to an SEM analysis.

7, and significant.) If not, then the LVs should be set as formative.ДЄlso, note that Jaime combined the LV scores in standardized format with indicator data from the original dataset, which were not standardized. (In reflective LVs the loadings are expected to be all high, ideally greater than. That is okay if the LVs are reflective that is, if the indicators of the LVs are highly correlated. Note that Jaime does not set the LVs in the first video as formative before generating the scores. The LV scores generated are then combined with indicators from the original dataset. This generates the LV scores for the LVs, which Jaime then saves into a. In the first video Jaime includes only LVs in the model, without any links among them, and then runs the SEM analysis. Latent variable (LV) scores are generated, saved, and then used in a subsequent SEM analysis. The goal of those steps is to use second order latent variables (LVs) in an SEM analysis.

They illustrate how steps 1 and 2, described in this post, can be implemented in WarpPLS. The YouTube videos below have been created by WarpPLS user and blog commenter Jaime LeГіn.

Smartpls 2.0 manual