I am trying to calculate the wealth index of a rural community of Nepal. For this, I used 10 household assets variables after conducting a descriptive analysis. I used the principal component analysis. I got the first four components have more than 1 eigenvalue, those four component explains 54% of the variance (the first component explains only 17.9%). The KMO measure of sampling adequacy is 0.619, Bartlett's test of sphericity is
Hi!
1. I think, that your PCA include all component (this is standard behavior, e.g. https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html), but your need the documentation for software.
2. I assume, than you can't accept KMO as 0.6 if it is border value for your statistic. Your need another model to prove you theory (which give your more confidence), non linear for example (MDS or TSNE).
But, this is only my point of view.
Dmitry.
Yes you are right for your very interesting and correct analysis of PCA and KMO
- Badges
- Science topic
- Similar topics
- Indexes