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An essential step in predicting the output of a complicated system is the calibration of model parameters, but this step cannot be conducted directly if the data at the system-level are not available. In such a situation, a reasonable route is to quantify the system model parameters using tests at lower levels of complexity which share the same model parameters with the system, and propagate the results through the computational model at the system level. For such a multi-level problem, this paper proposes a methodology to quantify the uncertainty in the system-level model parameters by integrating model calibration, model validation and sensitivity analysis at different levels. The proposed approach considers the validity of the models used for parameter estimation at lower levels, as well as the relevance at the lower level to the prediction at the system level.