I'm trying to specify McDonald's Omega. I'm using MPlus (based on FAQ Guide for Reability - https://www.statmodel.com/download/Omega%20coefficient%20in%20Mplus.pdf).

It works perfect for regular models, but when I try it to use it with factor analysis random intercept models, I do get Omega's higher than 1.00 in some factors.

That’s my model (not the syntax, only a brief approach to the example):

f1 by item1-item5

f2 by item6-item10

fRI by item1-item10

fRI with f1-f2@0

Based in Mplus FAQ’s, Omega’s calculation is (loaditems)^2/((loaditems)^2+resvarianceitems), right?

So, I wonder if is it ‘ok’ to have a ‘Omega higher than 1.00’ and what it would mean?

Also, there's an example of my Omega’s calcs (again, not my syntax, just a brief exercise of what I’ve been trying to do):

OmegaFactor1 = (loaditems1to5)^2/((loadsitems1to5)^2+resvarianceitems1to5)

OmegaFactor2 = (loaditems6to10)^2/((loadsitems6to10)^2+resvarianceitems6to10)

OmegaRandomIntercept = (loaditems1to10)^2/((loadsitems1to10)^2+resvarianceitems1to10)

I’m not sure if that approach is correct (even if Omega should be used or not in that case), but I do have an intuition that it’s happening because I’m lacking to specify in my model constraint ‘resvariance’ for Factor 1 and Factor 2, once the Random Intercept Factor is ‘interacting with it’.

Could anyone give me a tip about that theme?

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