i am looking at a simple psychological construct defined by two Subscales A and B. My supervisor told me to estimate the latent means of A and B. Looking on cross validated i found a similar question: providing mean, sd, and correlations in sem .However the answer belongs to MPlus and i don´t know if i can simply translate it like that into code for lavaan.
What i have done so far:
1.) lavaan model: constraining loadings (to 1) and intercepts (to 0) of A1 and B1, while estimating the intercepts and variances of the latent variables A and B freely.
model ="A =~ A1 + A2 + A3 + A4 + A5 B =~ B1 + B2 + B3 + B4 + B5 A1 ~ 0*1 A2 ~ 0*1 A ~ NA*1 b ~ NA*1"
2.) estmating the model:
sem = cfa(model, data = data, meanstructure = TRUE) summary(sem)
1.) can i see the intercept of A and B as the mean of A and B?
2.) in the lavaan Documention -http://lavaan.ugent.be/tutorial/means.html- it says, that "By default, the cfa() and sem() functions fix the latent variable intercepts (which in this case correspond to the latent means) to zero.". Is this no longer true, because i have estimated the intercepts freely?
3.) How can i interpret the mean of the latent variables? Is it like the mean of the Subscale?