Moment independent methods for the sensitivity analysis of model output are attracting growing attention among both academicians and practitioners. However, the lack of benchmarks against which to compare numerical strategies forces one to rely on ad-hoc experiments in estimating the sensitivity measures. This paper introduces a methodology that allows one to obtain moment independent sensitivity measures analytically. We illustrate the procedure by implementing four test cases with different model structures and model input distributions. Numerical experiments are performed at increasing sample size to check convergence of the sensitivity estimates to the analytical values.