Question
Asked 10th Jun, 2017

How much is acceptable for VAF (Variance Accounted for) in CATPCA analysis?

I used optimal scaling analysis and set 2 dimensions for the variables, with VAF of 36.6% obtained.
I set 3 dimensions, it's around 46%.
I don't know how much VAF is acceptable.
To me, 2 dimensions is easier to explain and define the dimension.
Thanks.

All Answers (3)

26th Jun, 2020
Najm Najm
Al-Zaytoonah University of Jordan
The strength of the mediation can be determined from the value of Variance
Accounted For (VAF). VAF value represents the ratio of the Beta Coefficient of
the indirect effect to the total effect. A VAF value bigger than 80% represents full mediation, a VAF value of between 20% and 80% means a partial mediation, while a value below 20% means no mediation (Hair, Ringle & Sarstedt, 2011).
1 Recommendation
20th Jun, 2021
Shuangxi Zhang
SEGi University College
VAF<20%表示無中介效果 20%<VAF<80%表示部份中介效果 VAF>80%表示完全中介效果
19th Jan, 2022
Muhammad S. Abu-Salih
Amman Arab University
VAF= (indirect effect)/total effect
VAF<0.20 No mediation
0.20<VAF<0.80 partial mediation
VAF>0.80 full mediation
For more details see (Hair, Ringle & Sarstedt, 2011).

Similar questions and discussions

Related Publications

Article
Statistical analyses of 104 natural hailpatterns recorded on the dense hailpad network of the Grossversuch IV experiment in Switzerland (1975-78) were carried out using standardized principal component analysis. Each hailpattern is considered to be a particular realization of a random phenomenon characterized by a set of variables, measured experim...
Article
Discrete choice models in general and random utility models in particular may be intractable when the number of alternatives is large. In the transportation context, it typically happens for route choice and destination choice models. In the specific case of the widely used multinomial logit model, it has been shown that the model could be estimate...
Article
Systematic and rational studies concerning the water quality of the Brates Lake were performed for the period 1995-1996. Cluster analysis and principal component analysis enabled more rational and objective estimation and comparison of the samples and characteristics of water quality as determined by traditional statistical methods. It also afforde...
Got a technical question?
Get high-quality answers from experts.