To provide context:
My model is a latent model; 3 Latent Variables, 3 Indicators for each of two Latent Variables and the last Latent Variable with 4 indicators.
Hence, Total of 3 Latent and 10 Measured Variables.
Paramaratization nature of CFA model:
Two latent variables are correlated with each other, while the same last latent variable is negatively correlated both of the other two.
All indicator / measured variables are expected to positively estimate unto their respective latent variables.
My Questions is
As an amateur, how to I determine / interpret / visualize relevant indices that will judge an appropriate sample size?
I never use R, but I am willing to learn through the coding. They seems okay but of course not so for a first-timer. It feels so close to finding the right spot but I am not there yet.
For clarifications on my research model, you may refer to this site: https://www.researchgate.net/publication/319433479_User's_Guide_for_the_Expectancy-Value-Cost_Survey_of_Student_Motivation
I also found a lot of resources to look at, but I am not sure which is appropriate for my project inquiry:
My model seems very close to this below, except that I have one latent variable negatively correlated with the other two: https://github.com/simsem/simsem/wiki/Example-2:-Covariance-Matrix-Specification
The matching coding pattern seems to fit those of continuousPower function of simsem: https://github.com/cran/simsem/blob/204201dd4227a6df6199a1d4fb51e017c15de77c/man/continuousPower.Rd
However, I am not sure how to implement with all the unfamiliar coding.