Parent engagement and community organizing with Latinos: a qualitative grounded theory study of an urban community

This explored parent engagement and community organizing in a Southern California community. The purpose of this exploration was to examine participants’ awareness of how community engagement and school committees affect Latino students in urban schools. Latino students in urban high schools with highly involved parents are more likely to achieve academic success and retention over the long term. This grounded theory methodological study examined participation and engagement, as well as what resources or changes could lead to further engagement. The focus group interviews took place in which community members participated in an open-ended interview. Twenty-six participants contributed to the data over the 2 focus groups. Eleven participants identified themselves as parents and 14 identified themselves as community members. A 3rd focus group was conducted for a member check, to present the themes to participants, and to ask for additional input. The final selective coding categories were time, policies and procedures, healthy schools, civic engagement, and gentrification. All participants felt bureaucracy and the school district’s rules and regulations were a roadblock for parents and community members. Time was an important issue for parents and community members. Participants noted that they wanted a healthy environment where their kids would be encouraged, loved, and respected. Many of the participants spoke about the need for more civic engagement and empowerment. Participants also saw gentrification as disrupting the fabric of the existing community. Six conclusions emerged from the analysis of the focus groups and the final selective coding categories. The conclusions were as follows: a connection exists between parent engagement and community organizing, Latino parents and community want to be active in children’s lives, school district rules and regulations can hinder parent and community engagement, parents want authentic communication and proper notice, community-based organizations are not working together in the community, and gentrification is a concern for community residents

Non-Gaussian Geostatistical Modeling using (skew) t Processes

We propose a new model for regression and dependence analysis when addressing spatial data with possibly heavy tails and an asymmetric marginal distribution. We first propose a stationary process with $t$ marginals obtained through scale mixing of a Gaussian process with an inverse square root process with Gamma marginals. We then generalize this construction by considering a skew-Gaussian process, thus obtaining a process with skew-t marginal distributions. For the proposed (skew) $t$ process we study the second-order and geometrical properties and in the $t$ case, we provide analytic expressions for the bivariate distribution. In an extensive simulation study, we investigate the use of the weighted pairwise likelihood as a method of estimation for the $t$ process. Moreover we compare the performance of the optimal linear predictor of the $t$ process versus the optimal Gaussian predictor. Finally, the effectiveness of our methodology is illustrated by analyzing a georeferenced dataset on maximum temperatures in Australi

Skewed Factor Models Using Selection Mechanisms

Traditional factor models explicitly or implicitly assume that the factors follow a multivariate normal distribution; that is, only moments up to order two are involved. However, it may happen in real data problems that the first two moments cannot explain the factors. Based on this motivation, here we devise three new skewed factor models, the skew-normal, the skew-t, and the generalized skew-normal factor models depending on a selection mechanism on the factors. The ECME algorithms are adopted to estimate related parameters for statistical inference. Monte Carlo simulations validate our new models and we demonstrate the need for skewed factor models using the classic open/closed book exam scores dataset