df_txst posted on Sunday, February 18, 2018 - 7:06 pm
I conducted an EFA in SPSS and found a four-factor solution: shape = 2 factors, color = 1 factor, size = 1 factor.
The 2 factors that explain shape perfectly load separately as regular and reverse-coded items. When I calculate the to scales separate, the correlation between them is low and each of their correlations with the combined scale is high. Good right? So I'm assuming if I had the two scales in the model and specified them to load on a higher-order SHAPE latent factor, then that loading would work similar. Is this the best approach to use? and If so, how exactly to is specify this in Mplus?
Btw: I'm ultimately trying to run a path analysis from shape to color to size.