Dropout prevention: factors contributing to the resiliency of at-risk Hispanic students and their pursuit of a high school diploma

Dropping out of high school has become a reality for far too many Hispanic high school students. Some estimates have the Hispanic dropout rate at close to 50% (Stutz & Hobbs, 2008). Research on factors contributing to this high dropout rate is well documented and schools across the nation are well aware of the problem. But in spite of this knowledge, the dropout epidemic persists. The purpose of this study was to determine, from a student perspective, what can be done to reduce the number of Hispanic dropouts. Forty-six at-risk Hispanic seniors in a small Texas high school along the Mexican-American border participated in this study by documenting their educational experiences in personal biographies written a few weeks before graduation. From these students 9 were selected to participate in one of four focus group interviews to obtain more detailed information. All students participating in the study were considered to be resilient because of their ability to overcome multiple, identified at-risk traits in their lives. Analysis of the data derived from these students led to the identification of barriers to graduation, ways of overcoming these barriers and ultimately 16 resiliency factors contributing to their successful completion of high school. Implications for students, parents and school personnel are also provided to help illuminate what can be done to help more Hispanics in similar situations to experience the same success as these students by graduating from high school

Proposal for Funding a Computer Research Program in Communications

A proposal for a full time research position for Summer Term 1974 submitted to Gary L. Harmon, Chairman of the Department of Languages and Literature, Dean Willard O. Ash and the College of Arts and Sciences Summer Grant Committee

Wireless Business And The Impact On Firm Performance: The Strategic Move To Adopt A New Technology

Information and Communications Technology (ICT) is receiving increased attention due to the vast potential it presents for rural and underserved settings. Successful adoption of new technology, in particular mobile commerce, can serve as a catalyst for improving quality of life and reducing the digital divide. Adoption of new technology is dependent upon variables such as: organizational culture, business strategy, as well as their surrounding environment. Hypotheses that investigate the relationships between the above variables and business performance in the context of the new technology adoption process are formulated. A sample of small and medium-sized businesses from the American Midwest that adopted, or are in the process of adopting, wireless technology is used to test these hypotheses. The methodology, results, and managerial implications are discussed

Validation of a Predictive Model for Survival in Patients With Advanced Cancer: Secondary Analysis of RTOG 9714.

BackgroundThe objective of this study was to validate a simple predictive model for survival of patients with advanced cancer.MethodsPrevious studies with training and validation datasets developed a model predicting survival of patients referred for palliative radiotherapy using three readily available factors: primary cancer site, site of metastases and Karnofsky performance score (KPS). This predictive model was used in the current study, where each factor was assigned a value proportional to its prognostic weight and the sum of the weighted scores for each patient was survival prediction score (SPS). Patients were also classified according to their number of risk factors (NRF). Three risk groups were established. The Radiation Therapy and Oncology Group (RTOG) 9714 data was used to provide an additional external validation set comprised of patients treated among multiple institutions with appropriate statistical tests.ResultsThe RTOG external validation set comprised of 908 patients treated at 66 different radiation facilities from 1998 to 2002. The SPS method classified all patients into the low-risk group. Based on the NRF, two distinct risk groups with significantly different survival estimates were identified. The ability to predict survival was similar to that of the training and previous validation datasets for both the SPS and NRF methods.ConclusionsThe three variable NRF model is preferred because of its relative simplicity