I'm running a latent variable analysis with:
- 166 observations
- 21 continuous variables
- using the R package
A simple run of
cfa() function with on factor failed because of the high range of variances (from 3e-6 to 2e-1).
My first reaction was to standardise the dataset using the
z-score and it worked.
Question: since the cfa is looking at reproducing a covariance matrix, doesn't it biase the analysis to have all variables variance equal to one?
NB: to tackle the issue I've also looked at:
- running it on a dataset scaled using the
min-maxmethod, the optimizer can't find a solution;
- using the cfa function with the correlation matrix obtained from the raw dataset as input (
sample.nobsas explained by Beaujean (2014)) and it gives striclty the same result as the analysis that considers the standardised dataset.