I tested the fit of a two-class growth mixture model for a continuous outcome by looking at the tech13 output. The two-sided multivariate skew test of fit gives a p-value of 0.150 The two-sided multivariate kurtosis test of fit gives a p-value of 0.120 I read that obtaining low p-values indicates that the model does not fit the data.
(1) Can I conclude this test does not reject the two-class model ?
(2) I found little information on the SK-test. Where can I find these two manuscripts: * Muthén & Asparouhov (2002): Mixture testing using multivariate skewness and kurtosis * Wang & Brown (2002): Residual diagnostics for growth mixture models: examining the impact of a preventive intervention.
(3) The SK testing should be preceded by outlier investigations. How do I perform such analyses ?
1. Yes. 2. Muthen and Asparouhov is not yet available. I believe Wang and Brown will be coming out in JASA. You need to check with them about that. 3. There are many books that describe outlier detection and some programs do this automatically. Basically, it is just looking at univariate and bivariate distributions to identify extreme observations.
Natalie posted on Thursday, June 09, 2011 - 6:30 pm
Can tech13 be used with a single-class model to obtain multivariate skew and kurtosis statistics for the variables included in the model? In other words, I'm not interested in using these statistics for model selection. I just want to test multivariate normality. If one can do this, can small p values be interpreted as signifying departure from multivariate normality?