Dear Statalisters,

I am trying to conduct an EFA on 17 binary variables. I have successfully used both tetrachoric and polychoric matrices with the factormat command, using the ml option

below my syntax

Code:
polychoric var1 - var17 
display r(sum_w)
global N = r(sum_w)
matrix r = r(R)
factormat r, n($N) ml
factormat r,  factor(2) ml n($N) altdiv
rotate, promax
sortl
estat kmo
estat factors 
factortest var1 - var17

and

Code:
tetrachoric var1 - var17
matrix r = r(Rho)
factormat r,  n(8819) ml
estat factors 

factormat r,  factor(2) ml n(8819) altdiv
rotate, promax
sortl
estat kmo
estat factors 
factortest var1 - var17

now, the reviewer says that the advantage of ml estimation over paf is "There isn't a good objective way to discern the optimal number of factors, and using the scree plot and eigenvalue rules are quite subjective and open to interpretation, and research shows that they tend to favor more rather than fewer factors. State of the art EFA would use maximum likelihood estimation for factoring, producing a chi-square test which allows for model comparison between a model with k factors vs. k-1 factors."

my question is: how do I obtain that chi2?

thanks in advance!