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;…

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…

Over the past decade, policymakers have enforced increasingly greater accountability demands for student achievement and zero tolerance policies. Legislators have outlined proficient measures for student achievement which includes students enrolled in general and special education classes and all ethnicities and socioeconomic levels. Schools…