Path analysis is applicable only in those kinds of cases where relatively small numbers of hypotheses can be easily represented by a single path. In path analysis, the association among the model should be linear in nature.  The associations among the models should be additive in nature. In path analysis, the association among the model should be causal in nature.  The data that is used should follow an interval type of scale.  In order to reduce volatilities in the data, it is assumed in the theory of path analysis that all the error terms are not correlated among the various variables. It is also assumed that errors are not correlated among themselves.  In path analysis, it is assumed that there is only one way causal flow. It's sample size is therefore recommended to be 10-20 times the number of parameters considered in the analysis. However, 280 participants or more is appropriate and can be accepted.

@Lianne Yes it's considered to be appropriate if you have 10 responses per observed item henceforth a sample of 280 response seems justified for applying SEM. But for representing the target population, you can take help of the paper attached below.

There is no fixed rule that applies to all situations. Also, your sample size may be large enough to obtain unbiased parameter estimates, but you may not have enough power to detect the effect sizes (paths) of interest (especially if the paths are small in size). The best way to study sample size requirements for path models and SEM/CFA is by running a Monte Carlo simulation through which you can study bias in fit statistics, parameter estimates, standard errors specifically for your model, target sample size, and expected effect sizes. Simulations also allow you to determine statistical power empirically.

I offer a free mini-workshop on sample size planning/power analysis with Mplus that uses path analysis as an example:

https://www.goquantfish.com/courses/sample-size-planning-in-mplus

See also:

http://www.statmodel.com/bmuthen/articles/Article_096.pdf

Many thanks for all your answers...very informative.

Regards,

Lianne