Yes, it is applicable. It can be achieved by applying this formula

[ n0 = Z^2. p (1-p)/e^2

Where:( n0 ) is the sample size.( Z ) is the Z-value (the number of standard deviations from the mean corresponding to the desired confidence level, e.g., 1.96 for 95% confidence).( p ) is the estimated proportion of an attribute present in the population (often 0.5 is used if the proportion is unknown, as it maximizes the sample size).( e ) is the desired level of precision (the margin of error).If you are aiming for a 95% confidence level and a margin of error of 5%, and assuming the proportion (( p )) is 0.5, the calculation would be:[ n_0 = (1.96)^2 . 0.5 . (1-0.5)/(0.05)^2=approx 384

Sir Minraj Paudel

The net sample size should be at least 384, which means after excluding and deleting all the missing values and not requiring observations.

So, accordingly, you should go for more than 384.

You can consider the following for your reference-

Article Determining Sample Size for Research Activities

Malhotra Naresh, K., & Dash, S. (2015). Marketing Research, an Applied Orientation, 7e. Pearson India.

Article Successfully Combining Meta-analysis and Structural Equation...

Article Sample Size Requirements for Structural Equation Models: An ...

http://www.raosoft.com/samplesize.html

I hope it helps :)

If you have a particular statistical test in mind, then you might use the more recently developed approach to sample size of assessing the "power" of that test. The program g*power will do this for you.