Thanks for your suggestion. I tried MLR but the model fit results are sub-par (CFI/TLI <.9; RMSEA > .8) in contrast to the original WLSMV model fit results that have very good TLI and RMSEA results (though CFI is still <.9 and less than optimal). Since I'm very interested in predicting latent variables (intercept, slope, and slope-squared) MLR is ideal, but the fit results are so poor that I'm concerned that any predicted values would be way off. The parameter estimates based on both estimator types, for fixed and random effects, are almost exactly alike though. Additionally, the MLR results come along with warnings unlike the WLSMV results:
1: In computeOmega(Sigma.hat = Sigma.hat, Mu.hat = Mu.hat, lavsamplestats = lavsamplestats, :
lav_model_gradient: Sigma.hat is not positive definite
This message disappears when I remove time-invariant covariates as predictors for the latent slope variable though. Here are MLR model fit statistics and explained variance results for the observed variables and intercept latent variable:
cfi.scaled tli.scaled rmsea.scaled srmr
0.833 0.825 0.098 0.113
> inspect(RR.fit.3.a, 'r2')
RR10 RR11 RR12 RR13 RR14 RR15 RR16 RR17 RR18 intercept
0.829 0.832 0.842 0.852 0.866 0.864 0.869 0.874 0.881 0.488
I'm uncertain about the best path forward so any other suggestions would be appreciated.
On a somewhat related issue, is there any lavaan function that provides an indication of fit for individual model-implied trajectories?
Take care, and thanks again!