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When running a quadratic latent growth curve model (model_6 below) I get the following warning message: "lavaan WARNING: some estimated lv variances are negative".

What does this mean, am I getting the warning because the quadratic model is not a good fit for the data?

For context: I am using latent growth curve models to model cognitive scores over 4 timepoints. I am in the process of deciding if a linear or quadratic fitted line is most appropriate.

I have included my linear (model_4) and quadratic (model_6) models below. The RMSEA for the linear model is 0.055, whereas the RMSEA for the quadratic model is 0.039.

enter image description here

model_4 = '
    # Intercept     
    i =~ 1*Score.baseline + 1*Score.wave1 + 1*Score.wave2 + 1*Score.wave3
    
    # Slope
    s =~ 0*Score.baseline + 1*Score.wave1 + 2*Score.wave2 + 3*Score.wave3
  
    # Set the residual variances to all the same 
    Score.baseline ~~ r*Score.baseline    
    Score.wave1 ~~ r*Score.wave1
    Score.wave2 ~~ r*Score.wave2
    Score.wave3 ~~ r*Score.wave3
'

lgcm_model_4 <- growth(model_4, data=Data_wide, missing = "fiml")

enter image description here

model_6 <- '
    # Intercept
    i =~ 1*Score.baseline + 1*Score.wave1 + 1*Score.wave2 + 1*Score.wave3

    # Slope 1
    s1 =~ 0*Score.baseline + 1*Score.wave1 + 2*Score.wave2 + 3*Score.wave3

    # Slope 2
    s2 =~ 0*Score.baseline + 1*Score.wave1 + 4*Score.wave2 + 9*Score.wave3
'

lgcm_model_6 <- growth(model_6, data=Data_wide, missing = "fiml")

Below I've also included two plots. The first shows the mean score across the 4 timepoints (waves).

enter image description here The second plot shows the fitted lines for the linear model (red) and the quadratic model (blue). enter image description here

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  • $\begingroup$ Were the variances estimated with the Cramer-Rao bound? If so, what is the condition number of your Fisher information matrix? $\endgroup$
    – Galen
    Nov 29, 2021 at 16:37
  • $\begingroup$ What optimization strategy did you use? $\endgroup$
    – Galen
    Nov 29, 2021 at 16:46
  • $\begingroup$ Why have you manually set some of the parameters? Example: 9 * Score.wave3. $\endgroup$
    – Galen
    Nov 29, 2021 at 19:25

1 Answer 1

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This warning means that your model is wrong.

The parameter estimates that are most consistent with the data require negative variances, which cannot exist.

Another way to think of it: There's not enough covariance between the measured variables to provide enough variance for three latent variables.

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  • $\begingroup$ Thanks, Jeremy. To confirm, does this mean that the model has been set up correctly, but is wrong based on the data it is applied to? $\endgroup$
    – Aepkr
    Dec 15, 2021 at 9:36
  • $\begingroup$ Well, could be either. But assuming it was set up correctly, it doesn't match the data. There is not enough non-linearity to require s2. $\endgroup$ Dec 15, 2021 at 18:33
  • $\begingroup$ Hi Jeremy, a follow-up question - the above warning does not occur when missing = "listwise", could you explain why this is? Thanks $\endgroup$
    – Aepkr
    Aug 19, 2022 at 15:35
  • $\begingroup$ Whether you get this warning from the specified model depends on the data that you analyze. When you specify 'missing = "listwise"' you're using different data. It could easily have gone the the other way - you get the warning with listwise, not with fiml. (And if you made me guess, I'd say it was more likely with listwise). $\endgroup$ Aug 19, 2022 at 16:04

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