Is it possible & appropriate to include age as a time varying covariate in LGCA?
I have a dataset with 4 ~"equally" spaced waves, although there is about 1 year range in ages within a given wave (e.g., in Wave 1, age ranges from 18-19; in Wave 2, age ranges from 19-20).
1) Modeling a linear LGCA: When I include AGE as a time varying covariate in the linear model, the fit is very good as suggested by CFI, TLI, SRMR, RMSEA, and lower BIC. [As a sidenote, AGE is not a significant covariate at any wave in this model]
However, when I remove AGE from the time varying covariate list, the model fit becomes abysmal as indicated by the various indicators.
2) So, I tried LGCA with individually-varying times of observation, using AGE as a the TSCORES. However, I get a giant residual variance for ICEPT (200+, when outcome Y is only scaled 0-16).
I'm trying to understand if keeping age as a TVC is OK, and further, trying to understand what's happening when I attempt individually-varying times of observation.
Thank you. I have a follow-up question about interpretation of time-varying covariates.
A current growth curve model I'm working with is similar to Example 6.10:
i s | y0@0 y1@1 y2@2; i s ON x1 x2 x3;
y0 ON a0 b0 c0; y1 ON a1 b1 c1; y2 ON a2 b2 c2;
What would be the proper interpretation of a significant time-varying covariate (e.g., a1) in the y1 regression? Is it just a standard regression where a1 influences the y1? Or is "a1" exerting an effect on the slope? [everything is continuous]
Thanks, and sorry if I missed this elsewhere on the board.