Efforts to objectively inform cannabis discourses include research on the epidemiology of cannabis abuse and dependence disorders or, collectively, cannabis use disorder (CUD). For my dissertation I identified classes of individuals based on intraindividual CUD trajectory patterns and contrasted trajectory classes with respect to clinical characteristics of CUD, developmental risk factors, and psychosocial outcomes. Identifying differences between trajectory classes provides evidence for the validity of trajectory-based CUD constructs and informs the development of comprehensive models of CUD epidemiology and trajectory-specific intervention approaches. My dissertation used data from the Oregon Adolescent Depression Project, a prospective epidemiological study of the psychiatric and psychosocial functioning of a representative community-based sample randomly selected from nine high schools across western Oregon. Four waves of data collection occurred between mid-adolescence and early adulthood and included diagnostic interviews and self-report questionnaires. Onset and offset ages of all CUD episodes were recorded. The reference sample included 816 participants who completed all diagnostic interviews. A series of latent class growth models revealed three distinct CUD trajectory classes through age 30: (1) a persistent increasing risk class; (2) a maturing out class, marked by increasing risk through age 20 and then a decreasing risk through early adulthood; and (3) a stable low risk class. Rates of cannabis dependence were similar across the persistent increasing and the maturing out classes. Trajectory classes characterized by a history of CUD were associated with a variety of childhood risk factors and measures of psychosocial functioning during early adulthood. Participants who were male, had externalizing disorders, and had psychotic experiences during early adulthood discriminated between the persistent increasing and the maturing out classes. Future research based on more diverse samples is indicated, as are well-controlled tests of associations between risk factors, trajectory class membership, and psychosocial outcomes. A better understanding of these relationships will inform etiological theories of CUD and the development of effective intervention programs that target problematic cannabis use at specific developmental stages. Designing targeted versus undifferentiated interventions for those at greatest risk for adult psychosocial impairment could be a cost-effective way to mitigate the consequences of CUD.

This dissertation explored the academic growth in mathematics of a longitudinal cohort of 21,567 Oregon students during middle school on a state accountability test. The student test scores were used to calculate estimates of school performance based on four different accountability models (percent proficient [PP], change in PP, multilevel growth, and growth mixture). On average, 72% of Oregon eighth graders were proficient in mathematics in 2012, 71% in the average school, and 6% more students in this cohort demonstrated mathematics proficiency compared to 2011. The two-level unconditional multilevel growth model estimated the average intercept (Grade 6) to be 228.4 (SE = 0.07) scale score points with an average middle school growth rate of 5.40 scale points per year (SE = 0.02) on the state mathematics test. Student demographic characteristics were a statistically significant improvement on the unconditional model. A major shortcoming of this research, however, was the inability to find successful model convergence for any three-level growth model or any growth mixture model. A latent class growth analysis was used to uncover groups of students who shared common growth trajectories. A five-latent class solution best represented the data with the lowest BIC and a significant LMR p. Two of the latent classes were students who had high achievement in Grade 6 and demonstrated high growth across middle school and a second group with low sixth grade achievement that had below average growth in middle school. Student-level demographic predictors had statistically significant relations with growth characteristics and latent class membership. In comparing school performance based on the four different models, it was found that, although statistically correlated, the models of school performance ranked schools differently. A school’s percentage of proficient students in Grade 8 correlated moderately (r = [.60, .70]) with growth over the middle school years as estimated by the growth and LCGA models. About 70% to 80% of schools ranked more than 10 percentiles differently for every pairwise comparison of models. These results, like previous research call into question whether currently used models of school performance produce consistent and valid descriptions of school performance using state test scores.