Forum Replies Created
May 24, 2019 at 10:57 am in reply to: "Model is overspecified" despite enough observed statistics #896
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!
ElisabethMay 24, 2019 at 10:44 am in reply to: "Model is overspecified" despite enough observed statistics #894
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 z-transformed all observed variables, too.
What would it mean for my sample if Onyx tells me it’s overspecified only based on the sample?
ElisabethMay 23, 2019 at 11:12 am in reply to: "Model is overspecified" despite enough observed statistics #892
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.
RayneMay 21, 2019 at 4:14 pm in reply to: "Model is overspecified" despite enough observed statistics #891
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!