Hi I have a model with latent variable interaction, which means that TYPE = RANDOM is enabled, and that TECH4 cannot be acquired. I need standard deviations of the latent variables for calculating standardised coefficients for the interaction. How can I ask for the standard deviations of the latent variables?
I have read the paper that is attached to the FAQ regarding latent variable interaction. I have also checked all the relevant topics on the forum. However, it seems that TECH4 cannot be acquired when MODEL = RANDOM. The question still remains as to how I can ask an output that tells me the SD of the latent variables in the model with interaction term.
Thanks, Bengt. If you could direct me to a book or reference that contains the formula, that would be much appreciated. I am not terribly familiar with manual calculations of statistics, so would really appreciate your help. Thanks in advance.
I am trying to work out how I can calculate variances of endogenous latent variables following equation (16) in your latent variable interaction paper in FAQ. There are obviously a few parameters that are unknown from the output of a model with latent variable interaction (TECH1 and TECH 8):
V(eta1), Cov (eta1,eta2), V (eta1 eta2) and V (residual variance of eta3).
Can you let me know whether the values of all these parameters have to be calculated by hand from say covariance matrix at the indicator level? Can I specify model in a way that will allow me obtain all the necessary parameters for the calculation of this equation?
Hello, I ran a model assessing simultaneous growth on 11 factors, with covariances between latent Intercepts and Slopes. The model runs, but means for Intercepts and Slopes were not automatically provided. So I requested them by typing, e.g., "[Ment_Slp];"
Without explicitly requesting means, several factors have significant variance in Intercept and Slope. When requesting means, SEs for these parameters increased, so none are significant anymore. The parameter values did not change much, but larger SEs made these values nonsignificant.
Should we consider these significant or not? Why would SEs increase? Below is a portion of the output from these runs.
The large SEs when you request factor means indicate that your model is not identified. You need scalar invariance over time to identify the factor means, and even then the factor means at the first time point need to be fixed to zero. See the setup in the UG for multiple-indicator growth.