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May 24, 2019 at 10:57 am in reply to: "Model is overspecified" despite enough observed statistics #896
Hello Timo,
oh, I see! Yes, that’s why I thought nothing had happened – the second ML solution is the same as the first one, probably only so slightly different that rounding leads to the same solution. Great, I’m glad that I can work with these results now.
Thank you so much for your help!
Cheers,
ElisabethMay 24, 2019 at 10:44 am in reply to: "Model is overspecified" despite enough observed statistics #894Hello Timo,
thank you for answering so quickly!
Clicking ALT+1, ALT+2 etc. doesn’t really change the model, in fact, it doesn’t change any parameters, which makes me think that it’s not working. Also when I select “Show best LS estimate” nothing changes. Is there a way to click through the estimates manually?I fixed the variance of the latent variables instead of one loading, and ztransformed all observed variables, too.
What would it mean for my sample if Onyx tells me it’s overspecified only based on the sample?Cheers,
ElisabethMay 23, 2019 at 11:12 am in reply to: "Model is overspecified" despite enough observed statistics #892Hello again,
I ran into the “Model is overspecified” problem again once I added a few paths to allow covariance between certain manifest variables. Notably, the problem only occurs once I connect my data with the model! My sample size is too small to draw definite conclusions (n = 150), but as it is my bachelor thesis, this shouldn’t be too big of a problem, it’s more exploratory. Could this be causing the problem?
https://www.dropbox.com/s/14rxfvuhok0u8cw/SSQ_SEM.xml?dl=0 This is the .xml code of my model.
Cheers,
RayneMay 21, 2019 at 4:14 pm in reply to: "Model is overspecified" despite enough observed statistics #891Hello Timo,
thank you so much for your response! I played around with the model and I seem to have fixed the problem. Maybe someone who stumbles across this forum has the same problem, so I can say what I did:
1) I made sure that every measurement model / every factor had one loading fixed to 1.
2) I made sure that every latent variable has a residual (this is what caused this particular problem).
3) I also made sure, if I made a model formative (the arrows point from the manifest variables to the latent variable, not vice versa) that I deleted the residuals on the manifest variables.Thank you again for your work and I hope this can help someone who’s also new in SEM!
Cheers,
Rayne 
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