Hello. I am testing a model fit in AMOS. The data follow a normal distribution and meet the assumptions of: linearity, multicollinearity and homocestality. I have used maximum likelihood (ML).
The problem is that in the standardized weights, the correlations and the R-squared, no information about the significance and confidence intervals are provided.
I have seen that this information can be accessed using ML together with bootstrapping, also the confidence intervals. Can ML be used together with bootstrapping when the data follows a normal distribution?
I don't see why not. If you're meeting the assumptions, then I would expect that your asymptotic/theoretical ML standard errors are similar to the robust/bootstrap standard errors (you should verify this for the unstandardized solution--simply compare the ML and bootstrap standard errors and p values).
It is unlikely that you are "perfectly" meeting the multivariate normality assumption anyway. The fact that the normality hypothesis is not rejected in your sample does not mean it is true. It could just be a sign of "insufficient" power.
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