As the title suggests, I have a question regarding whether to fix the factor loading or the latent variable variance. Both can be fixed in order to derive a 'scale' for your latent variables. The fit statistics should be the same in both cases.
In one of the openmx examples this is the case. I've attached the output (outputopenmx.txt). The scripts can be found on the forum (http://openmx.psyc.virginia.edu/sites/default/files/ThreeFactorObliqueCo... ). The dataset can also be found there as well (http://openmx.psyc.virginia.edu/sites/default/files/factorExample1.csv ).
I tried to replicate the above using the Digman97 dataset provided with metaSEM. I've attached the code (code.r) and output (code_output.r). The original example can be found on the metasem website (http://courses.nus.edu.sg/course/psycwlm/Internet/metaSEM/masem.html#sec... ). The fit statistics were not the same and were worse when fixing the factor loadings.
Did I incorrectly specify the model? Did I overlook something? Should the fit statistics be the same?
I've included a graph to illustrate the different (modelspectification.png). I made the graph in ms visio; if anybody knows a tutorial or example to directly render path diagrams from r which doesn't require the openmx path speficiation (e.g. semPlot), I would like to know as well.
All suggestions are most welcome.
|code.r ||7.04 KB|
|code_output.txt ||4.22 KB|
|outputopenmx.txt ||9.8 KB|
|modelspecification.png ||15.4 KB|