Hi.
In my project, I am working on a CFA SEM-based model for validation analysis to adopt a questionnaire translation to the native language in my country.
Due to certain constraints, I am only able to use purposive sampling (e.g. only psychology undergraduates students are sampled due to the narrow nature of the questionnaire; it asks participants specifically to provide a likert-scale points to one's own major under the observed variables).
I have 3 questions:
(1)
I am curious if the package "simsem" takes into account of non-random sampling method? In other words, does the package only applies to random sampling?
To provide context:
My model is a latent model; 3 Latent Variables, 3 Indicators for 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.
(2)
As an amateur, how to I determine / interpret / visualize relevant indices that will judge an appropriate sample size?
(3)
What does it mean when (simSetCFA function - RDocumentation):
(need to be SimMatrix object). [in terms of writing the coding]
E.g.
LXorLYfor factor loading matrix (need to beSimMatrixobject).
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: