This two-study investigation examined the social behavior competencies of a sample of students ages 8 to 18 who identified themselves as either bullies or non-bullies based on ratings of items on a comprehensive behavior rating scale. Specifically, the purpose of Study 1 was to establish criteria using the Social Skills Improvement System – Student Rating Scale (SSIS-S) to identify students from a nationally representative standardization sample who displayed high frequencies of bullying behaviors. The social behavior ratings for these self-identified bullies were then compared with all other students in the national sample and analyzed to determine differences among various domains of social skills and problem behaviors. In Study 2, the same students’ social behaviors were rated by adult informants to determine if there was added value in including parents and teachers in the assessment of the self-identified bullies. Finally, the extent of concurrent agreement was examined for all students among the teachers, parents, and students’ ratings of social skills and problem behavior domains. Study 1 revealed that self-identified bullies are not a homogeneous group. The main findings from Study 2 showed parents and teachers may add to the overall predictive validity of the student self-report assessment, but not the accuracy of classifying the students as bullies. Study 2 showed differences and similarities exist across the ratings provided by each rater. The relative value of including adult reports in the self-assessment likely lies in the reported differences from each rater, as they provide a more complete social behavior profile for each student. These findings are discussed in terms of existing research and theories regarding children and youths’ bullying behavior. Limitations and recommendations for future research conclude the report.

Through a two study simulation design with different design conditions (sample size at level 1 (L1) was set to 3, level 2 (L2) sample size ranged from 10 to 75, level 3 (L3) sample size ranged from 30 to 150, intraclass correlation (ICC) ranging from 0.10 to 0.50, model complexity ranging from one predictor to three predictors), this study intends to provide general guidelines about adequate sample sizes at three levels under varying ICC conditions for a viable three level HLM analysis (e.g., reasonably unbiased and accurate parameter estimates). In this study, the data generating parameters for the were obtained using a large-scale longitudinal data set from North Carolina, provided by the National Center on Assessment and Accountability for Special Education (NCAASE). I discuss ranges of sample sizes that are inadequate or adequate for convergence, absolute bias, relative bias, root mean squared error (RMSE), and coverage of individual parameter estimates. The current study, with the help of a detailed two-part simulation design for various sample sizes, model complexity and ICCs, provides various options of adequate sample sizes under different conditions. This study emphasizes that adequate sample sizes at either L1, L2, and L3 can be adjusted according to different interests in parameter estimates, different ranges of acceptable absolute bias, relative bias, root mean squared error, and coverage. Under different model complexity and varying ICC conditions, this study aims to help researchers identify L1, L2, and L3 sample size or both as the source of variation in absolute bias, relative bias, RMSE, or coverage proportions for a certain parameter estimate. This assists researchers in making better decisions for selecting adequate sample sizes in a three-level HLM analysis. A limitation of the study was the use of only a single distribution for the dependent and explanatory variables, different types of distributions and their effects might result in different sample size recommendations.

Researchers have postulated that math academic achievement increases student success in college (Lee, 2012; Silverman & Seidman, 2011; Vigdor, 2013), yet 80% of universities and 98% of community colleges require many of their first-year students to be placed in remedial courses (Bettinger & Long, 2009). Many high school graduates are entering college ill prepared for the rigors of higher education, lacking understanding of basic and important principles (ACT, 2012). The desire to increase academic achievement is a wide held aspiration in education and the idea of adapting instruction to individuals is one approach to accomplish this goal (Lalley & Gentile, 2009a). Frequently, adaptive learning environments rely on a mastery learning approach, it is thought that when students are afforded the opportunity to master the material, deeper and more meaningful learning is likely to occur. Researchers generally agree that the learning environment, the teaching approach, and the students' attributes are all important to understanding the conditions that promote academic achievement (Bandura, 1977; Bloom, 1968; Guskey, 2010; Cassen, Feinstein & Graham, 2008; Changeiywo, Wambugu & Wachanga, 2011; Lee, 2012; Schunk, 1991; Van Dinther, Dochy & Segers, 2011). The present study investigated the role of college students' affective attributes and skills, such as academic competence and academic resilience, in an adaptive mastery-based learning environment on their academic performance, while enrolled in a remedial mathematics course. The results showed that the combined influence of students' affective attributes and academic resilience had a statistically significant effect on students' academic performance. Further, the mastery-based learning environment also had a significant effect on their academic competence and academic performance.