APPLYING HIERARCHICAL LINEAR MODELING TO EXTENDED LONGITUDINAL EVALUATIONS - THE BOYS-TOWN FOLLOW-UP-STUDY

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