I have two sets of a longitudinal data that I hypothesize to measure same latent construct.I am trying to test this hypothesis using Structural equation modelling technique. Basically, I am trying to use confirmatory factor analysis in longitudinal data using SEM. My models are relatively simple CFA models.
The problem is that my sample size is small in term of SEM standards. We collected data only from 20 individuals for nearly two years (number of time points nearly equals 18) because the data collection is very expensive.
I am new to SEM and I was not able to decide if SEM is right choice for me. My question is given such a small sample size, is it a good idea to use SEM based techniques to model the data and test my hypothesis? Based on my researches, I concluded that I should not fit SEM using least squares, weighted least squares or Maximum likelihood since they require large samples which I do not have. It seems that they recommend fitting SEM using Partial least squares or Baysian approaches for small sample size but the problem is my sample size is very small. Are there any articles that discuss fitting SEM using very small sample size?
I am looking for suggestions, literatures or recomendations to fit such data with small sample size.