Reflective-Formative Construct - Unique Proportion of Variance

PLS is broadly applied in modern business research. This forum is the right place for discussions on the use of PLS in the fields of Marketing, Strategic Management, Information Technology etc.
Post Reply
PLS Junior User
Posts: 4
Joined: Wed Feb 13, 2019 8:13 am
Real name and title: Fabian Kreitner

Reflective-Formative Construct - Unique Proportion of Variance

Post by fkreitne » Fri Apr 05, 2019 1:33 pm

Hello together,

according to MacKenzie et al. (2011) the reliability of formative first-order constructs can be assessed by excluding one of the first-order constructs one after another to calculate the unique proportion of variance they explain in the second-order constrct. However, this recommendation is for CB-SEM only. Does a similar technique exist for PL-SEM?
The only comparable method that comes to my mind would be a redundancy analysis.
Since always 100 percent of the variance is explained in the second-order construct, when using the repeated indicator approach it is probably not possible to just exclude the first-order constructs, since the secondary loadings would also have to be deleted in the second-order construct, which then result again in an R-Square of 1.

Best regards,

SmartPLS Developer
Posts: 971
Joined: Tue Mar 28, 2006 11:09 am
Real name and title: Dr. Jan-Michael Becker

Re: Reflective-Formative Construct - Unique Proportion of Variance

Post by jmbecker » Fri Apr 05, 2019 5:27 pm

I think it is confusing to talk about reliability in terms of formative measures, because reliability is a concept that only works well for reflective items.

You are right that the idea of MacKenzie et al. (2011) relates best to the redundancy analysis. You are also right that in PLS it does not work to delete items and assess R² as the composite is always a complete function of the indicators and hence its R² from measurement model is always 1. But in a redundancy analysis you could delete dimensions/items and see how R² in the target construct changes. That would give you an intuition of the items relevance. However, that should also be closely connected to the strength and significance of the item weights in the original model.
Dr. Jan-Michael Becker, University of Cologne, SmartPLS Developer
Researchgate: ... v=hdr_xprf
GoogleScholar: ... AAAJ&hl=de

Post Reply