Multilevel modeling provides the ability to simultaneously evaluate the discounting of individuals and groups using indifference point data. After considering the conditions when weaknesses emerge in estimating individual discounting as a prelude to estimating group discounting, examples are provided that indicate that multilevel modeling improves estimation in the presence of variability and missing data, and when trying to fit two‐parameter discounting functions. Concrete examples of how to fit nonlinear multilevel models are provided to help researchers in the adoption of these methods.