I get that I need to somehow put restrictions on the (residual?) variances in order to obtain unbiased SEs and fit indices, but I can't seem to find information on how exactly to do this.
model <- 'a =~ 1*x1 + 1*x2 + 1*x3b =~ 1*x4 + 1*x5 + 1*x6c =~ 1*x7 + 1*x8 + 1*x9method =~ 1*x1 + 1*x4 + 1*x7g =~ 1*x1 + 1*x2 + 1*x3 + 1*x4 + 1*x5 + 1*x6 + 1*x7 + 1*x8 + 1*x9x1 ~~ e1*x1x2 ~~ e1*x2x3 ~~ e1*x3x4 ~~ e2*x4x5 ~~ e2*x5x6 ~~ e2*x6x7 ~~ e3*x7x8 ~~ e3*x8x9 ~~ e3*x9'
a ~~ var.a*a
b ~~ var.b*b
c ~~ var.c*c
g ~~ var.g*g
method ~~ var.m*method
## calculate explained variances
explained1 := var.a + var.m + var.g
explained23 := var.a + var.g
explained4 := var.b + var.m + var.g
explained56 := var.b + var.g
...
## constrain residuals to yield total variance == 1
e1 == 1 - explained1
e1 == 1 - explained23 # PROBLEM
e2 == 1 - explained4
e2 == 1 - explained56 # PROBLEM
Maybe someone has a hint or can point me towards a paper on how to correctly apply the restrictions here?
Thanks again, I'll try to put your advice into practice!
right?