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.
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