Lesa Hoffman posted on Friday, September 01, 2006 - 10:19 am
I am recalling a conversation I had a few years ago with Linda at a workshop in Alexandria regarding the assumption of normality within SEM. My recollection is that she told me that this assumption only applies to endogenous variables in the model, and thus that normality in exogeneous variables is not a problem for the model (assuming there is no missing data on the predictor, and thus that it would not be treated as an outcome for that reason). Is my recollection correct? Do you have suggestions for references that refer to this idea explicitly?
Thank you for your quick response! Just so I'm absolutely clear, is it true that the lack of distributional assumptions made about exogneous variables holds for latent OR observed variables? I thought latent variables are assumed to be normally distributed - or is that only latent outcome variables?
The model is estimated conditioned on observed exogenous variables not latent exogenous variables. The latent exogenous variables are part of the model and as such have distributional assumptions made about them.