I am a new user of the function sem in package sem and lavaan for structural equation modeling 1. I don’t know what is the difference between this function and CFA function, I know that cfa for confirmatory analysis but I don’t know what is the difference between confirmatory analysis and structural equation modeling in the package lavaan. 2. I have data that I want to analyse but I have some missing data I must to impute these missing data and I use this package or there is a method that can handle missing data (I want to avoid to delete observations where I have some missing data) 3. I have to use variables that arn’t normally distributed , even if I tried to do some transformation to theses variables t I cant success to have normally distributed data , so I decide to work with these data non normally distributed, my question my result will be ok even if I have non normally distributd data. 4. If I work with the package ggm for separation d , without latent variables we will have the same result as SEM function I guess 5. How about when we have the number of observation is small n, and what is the method to know that we have the minimum of observation required??
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