Reply To: "Model is overspecified" despite enough observed statistics

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Timo von Oertzen

Hi Rayne,

now this is great-looking model 🙂 !

I’ve run it with simulated data, that worked without overspecification, so there doesn’t seem to be anything conceptually wrong. Your data may create an empirical overspecification, or it could be that your data runs into a situation which is so close to oversspecification that the numerical test misstakes it as such. As long as you get only one solution or the solutions are virtually identical (you can switch between solutions by clicking ALT+1, ALT+2, and so on; be careful only to compare ML-solutions, you will also be shown LS (=Least Squares) solutions, which necessarily will be different), you’re good.

If not, there are two tricks to avoid empirical overspecification situations: The first is to normalize the data (which seems okay here since you are not interested in means). For this, just right-click on an observed variable (or select multiple and do the steps on one of them to reduce the work) and choose “Apply z-transform” in the context menu. This may solve your problem already, and it may also make effects more visible.

The second trick is to do the analogous thing on the latents by fixing all factor variances to one instead of fixing one of the loadings to one.

Let me know if this worked! If not, if you can send me an anonymized version of your data set, I can play around with it.

BTW, 150 participants are usually fully enough and fairly impressive for a Bachelor thesis!



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