Factors Influencing Alumni Giving of First-Generation Hispanic Women

Alumni giving has become a vital revenue source for colleges and universities in the United States. For private universities, alumni support is integral to the institution’s growth and sustainability. As a result, there is a growing body of research on the factors that influence alumni giving in order for fundraising professionals to identify potential donors. This study aimed to enhance this body of research by examining first-generation, low-income, alumni giving from Hispanic women from a California Private University’s (CPU) Neighborhood Engagement Program (NEP). NEP is a need-based scholarship program for underserved students from CPU’s host city. The purpose of the study was to explore how NEP alumni become financially motivated to support a CPU as well as analyze how the social exchange theory can explain their giving behavior. The study employed a case study methodology, using NEP alumni giving and interviews to gather data. Out of the 1,177 alumni, 408 (34.6%) had made a gift to the university in their lifetime. In addition, the Hispanic alumni from this group gave at a more significant rate than other ethnic groups. Alumni who had made at least five gifts within the last five years from the university’s host city were invited to participate. This resulted in four Hispanic women agreeing to the interview. The study found that NEP alumni were motivated to give based on their positive undergraduate experience and their continued engagement with the university as alumni. The participants supported areas at the university that provided them with a sense of family and home while they were undergraduate students. These participants felt isolated at the university as they were from a minority group and as a result, they gravitated to programs and activities that connected them with peers from their same ethnic group. The NEP alumni were grateful for the scholarship support they received and now primarily give to scholarships to support Hispanic students. In exchange for giving, NEP alumni receive feelings that enhance their self-esteem and image as well as recognition. The findings support prior research on alumni giving and adds to this growing body of research

Opportunities for farming in alpine countries – pathways to truly grassland-based beef and milk production in Austria and Switzerland

Farming in the alpine countries of Austria and Switzerland fulfils important economic, socio-cultural and ecological functions for society. However, even though both Austria and Switzerland have increasingly focused their agricultural policy towards ecology, in both countries negative environmental impacts of agriculture still have to be reduced massively

Pion scattering in Wilson ChPT

We compute the scattering amplitude for pion scattering in Wilson chiral perturbation theory for two degenerate quark flavors. We consider two different regimes where the quark mass m is of order (i) a\Lambda_QCD^2 and (ii) a^2\Lambda_QCD^3. Analytic expressions for the scattering lengths in all three isospin channels are given. As a result of the O(a^2) terms the I=0 and I=2 scattering lengths do not vanish in the chiral limit. Moreover, additional chiral logarithms proportional to a^2\ln M_{\pi}^2 are present in the one-loop results for regime (ii). These contributions significantly modify the familiar results from continuum chiral perturbation theory.Comment: 20 pages, 4 figures. V3: Comments on finite size effects and the axial vector current added, one more reference. To be published in PR

Optimal design when outcome values are not missing at random

The presence of missing values complicates statistical analyses. In design of experiments, missing values are particularly problematic when constructing optimal designs, as it is not known which values are missing at the design stage. When data are missing at random it is possible to incorporate this information into the optimality criterion that is used to find designs; Imhof, Song and Wong (2002) develop such a framework. However, when data are not missing at random this framework can lead to inefficient designs. We investigate and address the specific challenges that not missing at random values present when finding optimal designs for linear regression models. We show that the optimality criteria will depend on model parameters that traditionally do not affect the design, such as regression coefficients and the residual variance. We also develop a framework that improves efficiency of designs over those found assuming values are missing at random

Optimal design for experiments with possibly incomplete observations

Missing responses occur in many industrial or medical experiments, for example in clinical trials where slow acting treatments are assessed. Finding efficient designs for such experiments can be problematic since it is not known at the design stage which observations will be missing. The design literature mainly focuses on assessing robustness of designs for missing data scenarios, rather than finding designs which are optimal in this situation. Imhof, Song and Wong (2002) propose a framework for design search, based on the expected information matrix. We develop a new approach which includes Imhof, Song and Wong (2002)'s method as special case and justifies its use retrospectively. Our method is illustrated through a simulation study based on real data from an Alzheimer's disease trial