Model = CSW =~ x1:x7
LED =~ x8:x14
GF =~ x14: x21
VAN =~ x22:x28
GN =~ a*VAN + a*LED
VN =~ CSW + GF + HN
When I run this model I get the error stating some of the estimated latent variances are negative and it is for the factor VAN. So I fixed the error variance to equal to 0.01 and found the factor loads onto GN at 0.99. I then tried with GN and VN not being correlated and found the loading to decrease to 0.948. Normally, GN and VN correlate at around 0.4-0.5.
I also tried running LED and VAN as one factor but ended up with really poor model fit indices. When I run just the part of the model that involves GN I get a loading of 0.87 for VAN onto GN. I am not super familiar with the mathematics of this so I don't really understand what is going on. I thought maybe it was due to items from LED/VAN that were also loading highly onto the other factor, but modification indices quickly showed that was not the case. Currently, my next option is to explore model building to see where this issue might lie. But before I launch into that I wanted to see if you guys had any ideas.
For more contextual information LED only really correlates with VAN and none of the other factors (expected theoretically). The two LED and VAN factors make theoretical and we would expect to observe those.
My questions with this are:
- Is it a problem to have a factor load at > 0.80 onto a second order factor, when there are only two manifest factors?
- What are options around this error?
I really appreciate any feedback! Thank you