What you are looking for are the “estimated parameter”, the second rof from top.
To add yet another A’ (sorry 😉 : You can let Onyx do the work of deterimining the difference of degrees of freedoms between two models, just drag a line from one model to the other, and Onyx will give you the difference in chi^2, the difference in degrees of freedom, and the p-value determined from these two values.
Thanks, once more, for answering.
I’m not sure, though. According to http://davidakenny.net/cm/basics.htm#Degrees, df = k(k – 1)/2 – q where k is the number of variables and q is the number of free parameters.
What do you think of this?
This reply was modified 3 months, 3 weeks ago by sebwin.
these are the “restricted degrees of freedom”; they are sadly sometimes, not fully precisely, also just called “degrees of freedom”. The k(k-1)/2 are the number of entries in the covariance matrix, so these would be the degrees of freedom of the “saturated model”, i.e., the most complex possible model which estimates every entry of the covariance matrix separately. The difference of k(k-1)/2 to q is the number of degrees of freedom that your model has less than the saturated model. Note that it doesn’t make any difference if you compare two models, because the “k(k-1)/2” part appears on both sides of the minus sign and goes away anyway.