Purpose: In an era of unprecedented student measurement and emphasis on data-driven educational decision making, the full potential for using data to target resources to students has yet to be realized. This study explores the utility of machine-learning techniques with large-scale administrative data to identify student dropout risk. Research…

In the current study, based on a sample of 1,873 adolescents between 11.4 and 20.9 years of age from the first 3 waves of the National Longitudinal Study of Adolescent Health, we investigated the longitudinal effects of future orientation on levels of and developmental changes in problem behaviors, while controlling for the effects by impulsivity;…

Needs assessments in substance abuse prevention often rely on secondary data measures of consumption and consequences to determine what population subgroup and geographic areas should receive a portion of limited resources. Although these secondary data measures have some benefits (e.g. large sample sizes, lack of survey response biases and cost),…

Analyzing problem-behavior trajectories can be difficult. The data are generally categorical and often quite skewed, violating distributional assumptions of standard normal-theory statistical models. In this article, the authors present several currently available modeling options, all of which make appropriate distributional assumptions for the…