Accepted Solutions
Hi @lujc07,
The reason for the missing values on the diagram is because the variance of the latent variable is negative --variances shouldn't be negative so we don't try to give you a standardized estimate because it won't make sense. Also, it's important to check the convergence status of your model prior to examining the results; if the model fails to converge, the results are not interpretable. In your image, I see convergence is a problem:
Convergence failure can happen for a number of reasons but in this model I have two concerns:
1) It appears some of your latent variables might be nearly orthogonal (see, for example, the correlations between the Latent3 indicators and all other variables from the multivariate output), so the 2-indicator latent variables might not be estimable and this can lead to problems.
2) The negative correlation of DO and Temp could be making things harder.
My suggestions to address these concerns are:
1) You could change the specification of the 2-indicator latent variables, such that you fix their variance to 1 and then set the two loadings to equal:
For consistency, you could free the first loading of Latent3 and also fix its variance to 1.
2) You could try "reverse-coding" one of those two variables so that the correlation is positive. For example, for Temp, you could go to the column in the data table, right-click on it, and select New Formula Column > Transform > Negation. This will create a new data table column that's reverse coded and you can use it in place of Temp.
I hope this helps your model converge to a proper solution.
Best,
~Laura
Hi @lujc07,
The reason for the missing values on the diagram is because the variance of the latent variable is negative --variances shouldn't be negative so we don't try to give you a standardized estimate because it won't make sense. Also, it's important to check the convergence status of your model prior to examining the results; if the model fails to converge, the results are not interpretable. In your image, I see convergence is a problem:
Convergence failure can happen for a number of reasons but in this model I have two concerns:
1) It appears some of your latent variables might be nearly orthogonal (see, for example, the correlations between the Latent3 indicators and all other variables from the multivariate output), so the 2-indicator latent variables might not be estimable and this can lead to problems.
2) The negative correlation of DO and Temp could be making things harder.
My suggestions to address these concerns are:
1) You could change the specification of the 2-indicator latent variables, such that you fix their variance to 1 and then set the two loadings to equal:
For consistency, you could free the first loading of Latent3 and also fix its variance to 1.
2) You could try "reverse-coding" one of those two variables so that the correlation is positive. For example, for Temp, you could go to the column in the data table, right-click on it, and select New Formula Column > Transform > Negation. This will create a new data table column that's reverse coded and you can use it in place of Temp.
I hope this helps your model converge to a proper solution.
Best,
~Laura