In partitioned analysis, individual constituent models developed in an isolated manner are coupled to predict the behavior of a system more complex than the individual constituents themselves. Such coupled models, be they multiscale and/or multi-physics in nature, are regularly implemented to improve engineering systems at design and off-design conditions, establish safety procedures, develop public policies, or determine legal liabilities. The only rigorous means of establishing and improving confidence in predictions of these models involves calibrating and validating them against physical experiments. In this paper, the authors propose a model calibration framework tailored specifically for strongly coupled numerical models to assist in resource allocation strictly from the perspective of predictive maturity. The focus herein is not only on the allocation of resources between code development efforts and physical experiments conducted for validation, but also on determining the most efficient means of allocating resources in accomplishing code development and data collection tasks. Thus, an approach for prioritization of code development efforts as well as an approach for design of optimal experiments is provided. Furthermore, the framework defines a domain within which the model is executed to make predictions and takes a sufficient coverage of this domain through physical experiments into account. This paper thoroughly explains this framework and demonstrates its application and feasibility through a series of complex case study applications.