WarpPLS users can assess measurement invariance in PLS-SEM analyses in a way analogous to a multi-group analysis. That is, WarpPLS users can compare pairs of measurement models to ascertain equivalence, using one of the multi-group comparison techniques building on the pooled and Satterthwaite standard error methods discussed in the article below. By doing so, they will ensure that any observed between-group differences in structural model coefficients, particularly in path coefficients, are not due to measurement model differences.

Kock, N. (2014). Advanced mediating effects tests, multi-group analyses, and measurement model assessments in PLS-based SEM.

*International Journal of e-Collaboration*, 10(3), 1-13.

For measurement invariance assessment, the techniques discussed in the article should be employed with weights and/or loadings. While with path coefficients researchers may be interested in finding statistically significant differences, with weights/loadings the opposite is typically the case – they will want to ensure that differences are not statistically significant. The reason is that significant differences between path coefficients can be artificially induced by significant differences between weights/loadings in different models.

A spreadsheet with formulas for conducting a multi-group analysis building on the pooled and Satterthwaite standard error methods is available from WarpPLS.com, under “Resources”. As indicated in the article linked above, this same spreadsheet can be used in the assessment of measurement invariance in PLS-SEM analyses.

The menu options “Explore multi-group analyses” and “Explore measurement invariance”, available in WarpPLS starting in version 6.0, allow you to automatically conduct analyses like the ones above. Through these the data is segmented in various groups, all possible combinations of pairs of groups are generated, and each pair of groups is compared. As noted above, 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 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

http://youtu.be/m2VKQGET-K8

Explore Measurement Invariance in WarpPLS

http://youtu.be/29VqsAjhzqQ