Objectives

This paper aims to suggest a framework to think of a more practical way to consider the broader impact of a program intervention beyond just its average, by considering the concept of treatment effect heterogeneity—how the same intervention may produce differential effects for different subgroups of individuals.

Methods

Using an application of data on an experimental intervention from the Johns Hopkins Prevention Intervention Research Center, the current study demonstrates the contribution of more general growth mixture modeling approaches, such as Group-Based Trajectory Model (Nagin in Group-based modeling of development. Harvard University Press, Cambridge, 2005) and growth mixture modeling (Muthén in New developments and techniques in structural equation modeling. Lawrence Erlbaum Associates, Mahwah, pp 1–33, 2001) for assessing meaningful heterogeneous effects of a treatment across clusters or classes of individuals following distinct patterns of development over time.

Results

The findings demonstrate how population-averaged treatment effects might underestimate substantively meaningful localized effects among more theoretically and policy relevant subgroups of individuals such as those with non-normative growth (high–low) and those with more room for improvement (low–low) in the development of self-control.

Conclusions

We are calling for the assessment of a program in terms of both average and localized effects because we might wrongfully conclude that a given program is not effective when it in fact has a great impact, but only on the segments of population who need it the most.