Dr. Muthen, What is the difference between STDY Standardization and STDYX Standardization in the standardized model results? Which of the two should I interpret when I have direct and indirect effects unto one Dependent latent variable? Thank you, Linda
Unless you have a preponderance of zeroes, estimators like MLR that are robust to non-normality should handle skewness for continuous indicators. Floor or ceiling effects for categorical indicators are handled by categorical data methodology.
I am estimating a logistic regression (MLR) with some covariates that are continuous and others that are binary. I am using exclusively observed variables and no indirect effects. It is a simple logistic regression as you use to estimate in Stata or SPSS.
Should I use StdYX for continous and StdY for the binary covariates even when the two types of covariates are in one and the same model? I have a strange feeling by using for some covariates the coefficients from one part of the output and for the other covariates the coefficients from an other part of the output. Is this really advisable?
If I understand right, I should use the coefficients under the output block titled StdYX for the continuous covariates while for the binary covariates I should use the non-standardized coefficients at the top of the output?
Is it really advisable to use for some covariates one specific part of the output while for other covariates a different part of the output is used?
I am trying to make sense of differences in the p-values and meanings of the unstandardized vs. standardized (STDYX) covariances in the following output. All the variables in the model are continuous and both have missing data, so I am using FIML. Estimator = MLR. Typically, I like to report the STDYX results because they’re in an easily understood metric, but I am unnerved by such differences. I have several similar examples in regression models.
If the only difference is that STDYX is calculating the covariance having rescaled each variable to have M=0 and SD=1, I would expect the p-values to agree perfectly. Since that’s not the case, I’m unsure what Mplus is doing…how these inconsistent p-values occurred. Also, which results are “more correct.” And is the answer any different if I’m doing regression models with continuous DVs and predictors?
Thanks for your input!
Y WITH X 0.031 0.019 1.645 0.100
STDYX Standardization Y WITH X 0.322 0.156 2.062 0.039
Leaving Bayes est aside for the moment, which results would you put more stock in, the unstandardized (n.s.) or the standardized (sig)? Is it arbitrary because they're both "correct"? Sigh, I'm sure this is where the people who advocate getting rid of null hypothesis testing would pipe up.
So you're suggesting that Bayes is "better" than either the regular unstandardized or standardized solutions? I should trust the result that is more consistent with the Bayes result? If so, does that imply that I should be using Bayes all the time?
By the way, I've been using MLR because the IV and DV distributions are nonnormal.
Bayes does not assume that the sampling distributions of the parameters are normal as frequentist methods do. It is not clear how robust Bayes is to non-normality of the data. If your data are non-normal and the sampling distributions of the parameters are non-normal, you may be best off with ML and bootstrapping.