Quality education is the question of many countries. Students' achievement were a measure of teaching-learning process. Scholars suggested that regardless of the subject nature, many factors affect students' achievements in each school subject. This study is an attempt to determine factors influencing academic achievements of grade 8, 10 and 12 students in Debre Tabor Town (DT) and Bahir Dar Town (BDT) in 2013-2014 academic year with achievements variations occurred in student, class and school levels. Depending on the objectives descriptive analysis and multivariate multilevel linear regression analysis with maximum likelihood estimation were used to analyze the data. The results of BDT data show that school type and average work load of teachers jointly show a significant effect on both subjects; Teachers experience in terms of Mathematics, class size and availability of reference and text books in terms of English show significant influence on academic achievements. From the study, it was found out that multilevel modeling is much better than the single level ordinary regression model in fitting the data and in explaining the variations of the academic achievement at different levels. Likewise, on average non-governmental school students achievements were better than governmental. It can be concluded from this study that the variation of academic achievement of students in each grade level of mathematics and English subjects high within class followed by between schools. It can be recommended that academic facilities and managements at schools and home, and students personal efforts need to be improved in order to achieve better quality of education in all fields of studies at high standard.

The Migration of Ethiopians to Middle Eastern countries has become a common phenomenon. Research emphasizing aspects of Migration in Ethiopia has focused on the causes of Migration and the situation in the host country. But there has been no focus on future intentions of forced mass return. The internal instability of Ethiopia is also another issue for returnees staying in their homeland. Thus, this cross-sectional mixed research reveals the causes, challenges, and expectations of forced female returnee migrants in the Amhara region while they were in Migration to the Middle East, during work, and upon arrival to the homeland, as well as their future intention/plan/ after return in the homeland by applying factor analysis and binary logistic regression model. The study participants in this research were 346 forced female returnees from the Middle East selected with multistage sampling and two key informants from each study area's labor and social affairs office. Findings indicate that robbery, extortion, and lack of accommodation were challenges during Migration; verbal abuse, restricted mobility, and communication, no days off at work, and not receiving a wage for work were challenges during work in the Middle East. Besides, lack of happiness, lack of employment and support, and feeling inferiority and low self-esteem are challenges for return migrants after coming to their homeland. Most return migrants received continuous social counseling training, skill training, and entrepreneurship training. The study also reveals that currently married returnees have a significant positive intention to live in their homeland. Though, deprived economic and joblessness-related factors are statistically significant as the negative chance to live in the homeland. Lastly, the study recommends that close cooperation between all parties, governmental and non-governmental institutions, UN organizations like IOM, ILO, and other NGOs are needed to better reintegration and living of these forced returnees.

Verbal learning and memory summaries of older adults have usually been used to describe neuropsychiatric complaints. Bayesian hierarchical models are modern and appropriate approaches for predicting repeated measures data where information exchangeability is considered and a violation of the independence assumption in classical statistics. Such models are complex models for clustered data that account for distributions of hyper-parameters for fixed-term parameters in Bayesian computations. Repeated measures are inherently clustered and typically occur in clinical trials, education, cognitive psychology, and treatment follow-up. The Hopkins Verbal Learning Test (HVLT) is a general verbal knowledge and memory assessment administered repeatedly as part of a neurophysiological experiment to examine an individual’s performance outcomes at different time points. Multiple trial-based scores of verbal learning and memory tests were considered as an outcome measurement. In this article, we attempted to evaluate the predicting effect of individual characteristics in considering within and between-group variations by fitting various Bayesian hierarchical models via the hybrid Hamiltonian Monte Carlo (HMC) under the Bayesian Regression Models using ‘Stan’ (BRMS) package of R. Comparisons of the fitted models were done using leave-one-out information criteria (LOO-CV), Widely applicable information criterion (WAIC), and K-fold cross-validation methods. The full hierarchical model with varying intercepts and slopes had the best predictive performance for verbal learning tests [from the Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) study dataset] using the hybrid Hamiltonian-Markov Chain Monte Carlo approach.

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