The application of latent curve analysis to testing developmental theoriesin intervention research

The effectiveness of a prevention or intervention program has traditionally been assessed rising time-specific comparisons of mean levels between the treatment and the control groups. However, many times the behavior targeted by the intervention is naturally developing over rime, and the goal of the treatment is to alter this natural or normative developmental trajectory. Examining rime-specific mean levels can be both limiting and potentially mi sleading when the behavior of interest is developing systematically over ti me. Ir is argued here that there are both theoretical and statistical advan tages associated with recasting intervention treatment effects in terms of normative and altered developmental trajectories. The recently developed te chnique of latent curve (LC) analysis is reviewed and extended to a true ex perimental design setting in which subjects are randomly assigned to a trea tment intervention or a control condition. LC models are applied to both ar tificially generated and real intervention data sets to evaluate the effica cy of an intervention program. Not only do die LC models provide a more com prehensive understanding of the treatment and control group developmental p rocesses compared to more traditional fixed-effects models, but LC models h ave greater statistical power to detect a given treatment effect. Finally, the LC models are modified to allow for the computation of specific power e stimates under a variety of conditions and assumptions that can provide muc h needed information for the planning and design of more powerful bur cost- efficient intervention programs for the future.