Applying Hierarchical Linear Modeling to Extended Longitudinal Evaluations

Abstract

Longitudinal research designs with many waves of data have the potential to provide a fine-grained description of program impact, so they should be of special value for evaluation research. This potential has been illusive because our principal analysis methods are poorly suited to the task. We present strategies for analyzing these designs using hierarchical linear modeling (HLM). The basic growth curve models found in most longitudinal applications of HLM are not well suited to program evaluation, so we develop more appropriate alternatives. Our approach defines well-focused parameters that yield meaningful effect-size estimates and significance tests, efficiently combining all waves of data available for each subject. These methods do not require a uniform set of observations from all respondents. The Boys Town Follow-Up Study, an exceptionally rich but complex data set, is used to illustrate our approach.