Principles and Application of the Cooperative Model in Continuing Education

This article sets forth a philosophy of cooperative planning for continuing education and lists eight major principles that guide its development A cooperative education planning venture between the Division of Continuing Education at the Boston University School of Social Work and Rhode Island groups spearheaded by social workers illustrates how the application of these principles culminated in the Rhode Island Consortium for Continuing Education in Social Welfare. Plans are currently underway to expand the concept to include other groups in order to build a state-wide model of continuing education in human service and community welfare.     

School Integration of Migrant Children from the United States in a Border Context

Since 2008, about half a million children have arrived in Mexico from the United States, most of whom are US-born. However, due to limited statistical data, there have been few quantitative studies on their school integration. Using data from the 2017 School Integration Survey, conducted in 86 lower secondary schools in the Tijuana metropolitan area, we analyse the relationship between students’ liking of school and their binational school trajectories in this border city with high return migration. The results, based on a multivariate statistical analysis, show that students who studied in that country exhibit a lower liking of school, which correlates with a higher number of years of schooling in the U.S. and less time spent in Mexico since last arrival. Various mediating mechanisms are identified, such as limited Spanish proficiency, teacher indifference and lack of cultural identification with Mexico. The implications for education policy are also discussed.     

The relationship between the mean,median, and mode with grouped data

It is widely believed that the median is “usually” between the mean and the mode for skewed unimodal distributions. However, this inequality is not always true, especially with grouped data. Unavailability of complete raw data further necessitates the importance of evaluating this characteristic in grouped data. There is a gap in the current statistical literature on assessing mean–median–mode inequality for grouped data. The study aims to evaluate the relationship between the mean, median, and mode with unimodal grouped data; derive conditions for their inequalities; and present their application.     

Cross-border school enrolment: Associated factors in the U.S.–Mexico borderlands

This paper examines students who live in Mexico but attend school in the U.S., and looks into the factors associated with their decision to study abroad. Based on Mexico’s 2015 Intercensal Survey, cross-border students are described in terms of their number, location, educational level and socioeconomic characteristics. Subsequently, the study estimates probit models to analyze the factors associated with studying in the United States. Cross-border students are mainly U.S.-born and concentrated in Tijuana and Ciudad Juárez. The probability of being a cross-border student is positively associated with age, household income and having a household member who was born in the U.S. or is a cross-border worker. Cross-border students come from high-income households with strong ties to the United States. The decision to study in the U.S. is likely taken due to the higher quality of the country’s education system and to facilitate an eventual transition into the U.S. labor market.     

Variance function estimation of a one-dimensional nonstationary process

We propose a flexible nonparametric estimation of a variance function from a one-dimensional process where the process errors are nonstationary and correlated. Due to nonstationarity a local variogram is defined, and its asymptotic properties are derived. We include a bandwidth selection method for smoothing taking into account the correlations in the errors. We compare the proposed difference-based nonparametric approach with Anderes and Stein(2011)’s local-likelihood approach. Our method has a smaller integrated MSE, easily fixes the boundary bias, and requires far less computing time than the likelihood-based method.