Recursive partitioning is an analytic technique that is useful for identifying complex combinations of conditions that predict particular outcomes as well as for delineating multiple subgroup differences in how such factors work together. As such, the methodology is well suited to multidisciplinary, life course inquiry in which the goal is to integrate many interacting influences and understand subgroup variation. The authors conducted recursive partitioning analyses on a previously published study (D. K. Mroczek & C. M. Kolarz, 1998) that investigated life course profiles of positive and negative affect and incorporated various top-down (personality traits) and bottom-up (sociodemographic statuses, contextual influences) influences. The new analyses reveal multiway, nonlinear interactions among these variables in predicting affective experience and, importantly, life course differences in how these various factors combine. Included are details of how recursive partitioning trees are generated as well as descriptions of the software packages available for using such techniques. Overall, the methodology offers tractable strategies for discerning meaningful patterns in highly complex data sets.