I am trying to run a random intercept cross-lagged panel model (cf. Hamaker, Kuiper, & Grasman, 2015, http://dare.uva.nl/document/2/168970). However, this model was designed with continuous observed variables, and my variables are non-normally distributed ordinal categorical variables. I tried to run this model without specifying the variables as categorical and that went fine. However, when I add the "categorical = ..." command, I receive an error message: "The following MODEL statements are ignored: * Statements in the GENERAL group: [ W1CBV ] [ W2CBV ] [ W3CBV ] [ W1CBP ] [ W2CBP ] [ W3CBP ]". This refers to the following line in the MODEL-command: [w1CBV-w3CBP@0];
With continuous variables, the goal is to fix the observed means to zero so that the latent means can be estimated. However, this does not seem to work with categorical variables. How can I run this model with categorical variables?
Thanks for the suggestion. I tried this, but received the following error message: THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES COULD NOT BE COMPUTED. THE MODEL MAY NOT BE IDENTIFIED. CHECK YOUR MODEL. PROBLEM INVOLVING THE FOLLOWING PARAMETER: Parameter 11, [ MCBV ]
From the tech1-output, I see that parameter 11 is the alpha-value for MCBV. In tech4, the means of MCBV is estimated to be 0.194, so I do not see what the problem is here.
Hi I would like to know if it is possible to use RI-CLPM with latent variables I already tested a CLPM with latent variables and I was wondering if I could use RI CLPM , I couldn't find anything about it Thanks