# CFA: scaling of measured variables/indicators

I'm running a latent variable analysis with:

• 166 observations
• 21 continuous variables
• using the R package lavaan

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-max method, the optimizer can't find a solution;
• using the cfa function with the correlation matrix obtained from the raw dataset as input (sample.cov and sample.nobs as explained by Beaujean (2014)) and it gives striclty the same result as the analysis that considers the standardised dataset.

I have the same problem. I have some variables with different scales. I searched on Mplus Discussion forums and apparently, they standardise continuous variables (transformation to z score) prior to modelling. see: http://www.statmodel.com/discussion/messages/9/2090.html?1515716898

• thanks for sharing your findings! – edith Aug 25 '19 at 17:56