Does the Role of Student Factors with Adolescent Substance Use Vary by Geography?

This study assesses the association of 12th-grade student factors (e.g., grades, classroom misbehaviors) with substance use (i.e., binge drinking, cigarette smoking, marijuana) across large, medium-sized, and nonmetropolitan areas. Based on a sample of 2,189 students from the 2013 Monitoring the Future data set, logistic regression analyses showed that poor student academic and behavioral factors were associated with higher substance use after controlling for gender, race, and socioeconomic status. Interaction analyses further showed no significant differences in the influence of student factors on substance use across locales. Results suggest that prevention efforts against substance use should target student factors regardless of area of residency.     

Relearning to Live Life Without Substances: A Grounded Theory of the Impact of Group Physical Exercise on Sobriety

Research has found significant benefits in using exercise as an adjunct treatment for substance use. This study used a qualitative grounded theory approach to explore the role of group exercise on maintaining sobriety. Sixteen persons (13 White men and 3 White women, age M = 31.81, SD = 8.23), recruited from a substance use recovery group that meets at a gym in an urban area in the Southeast part of the United States, were individually interviewed. Data were analyzed using Charmaz’s (2006) constructivist grounded theory strategies. Central themes revealed a parallel process between the steps taken to seek sobriety and those taken to maintain sobriety. The driving forces of sobriety success in both was 12 steps and group exercise programming that included elements of mentorship, structure, accountability, and fellowship or community. This study supported the use of physical group exercise as an adjunct treatment option for substance use treatment.     

Some alternative bivariate Kumaraswamy models

In this article we discuss various strategies for constructing bivariate Kumaraswamy distributions. As alternatives to the Nadarajah et al. (2011) bivariate model, four different models are introduced utilizing a conditional specification approach, a conditional survival function approach, and an Arnold–Ng bivariate beta distribution construction approach. Distributional properties for such bivariate distributions are investigated. Parameter estimation strategies for the models are discussed, as are the consequences of fitting two of the models to a particular data set involving the proportion of foggy days at two different airports in Colombia.     

Investment model predictions of workplace ostracism on K–12 teachers’ commitment to their schools and the profession of teaching

The purpose of this study was to determine how the ostracism of K-12 teachers influences their commitment to their schools and commitment to the teaching profession. The investment model was used to situate ostracism as a predictor of teacher commitment. Participants were 200 full-time K-12 teachers who completed a survey assessing their experiences with ostracism at work and investment model variables (i.e., investments, quality of alternatives, satisfaction, and commitment). Results confirmed investment model predictions with teachers’ investments, quality of alternatives, and satisfaction predicting their commitment to their schools and profession. Results of mediation models also demonstrated that controlling for teachers’ investments and quality of alternatives, ostracism predicted commitment indirectly through its effect on satisfaction. This study revealed that teacher commitment is explained by investment model predictions, but after controlling for those predictions, is further explained by ostracism from teacher colleagues.     

Threshold Estimation for Stochastic Processes with Small Noise

Consider a process satisfying a stochastic differential equation with unknown drift parameter, and suppose that discrete observations are given. It is known that a simple least squares estimator (LSE) can be consistent but numerically unstable in the sense of large standard deviations under finite samples when the noise process has jumps. We propose a filter to cut large shocks from data and construct the same LSE from data selected by the filter. The proposed estimator can be asymptotically equivalent to the usual LSE, whose asymptotic distribution strongly depends on the noise process. However, in numerical study, it looked asymptotically normal in an example where filter was chosen suitably, and the noise was a Lévy process. We will try to justify this phenomenon mathematically, under certain restricted assumptions.