Measuring College Student Satisfaction: A Multi-Year Study of the Factors Leading to Persistence

Using Tinto's (1987) social integration theory as a framework, this study measured student satisfaction in six transformative areas: educational experience, skills development, faculty interaction, personal growth, sense of community, and overall expectations. Emerging as a strategic planning process priority, this project sought to identify those areas where students succeeded or were at risk. Employing a three-phase mixed methods approach, this descriptive, longitudinal study was conducted from 1990-2004 at a highly selective specialized college and assisted college administrators in developing or modifying programs that would enhance student satisfaction to ensure degree completion.

The Effect of Classmate Characteristics on Individual Outcomes: Evidence from the Add Health

We use data from the National Longitudinal Study of Adolescent Health (Add Health) to examine the effects of classmate characteristics on economic and social outcomes of students. The unique structure of the Add Health allows us to estimate these effects using comparisons across cohorts within schools, and to examine a wider range of outcomes than other studies that have used this identification strategy. This strategy yields variation in cohort composition that is uncorrelated with student observables suggesting that our estimates are not biased by the selection of students into schools or grades based on classmate characteristics. We find that increases in the percent of classmates whose mother is college educated has significant, desirable effects on educational attainment and substance use. We do not find much evidence that the percent of classmates who are black or Hispanic has significant effects on individual outcomes, on average. Additional analyses suggest, however, that an increase in the percent black or Hispanic may increase dropout rates among black students and post-high school idleness among males.

Validation of a Spatially Continuous EDEN Water-Surface Model for the Everglades, Florida

The Everglades Depth Estimation Network (EDEN) is an integrated network of realtime water-level monitoring, ground-elevation modeling, and water-surface modeling that provides scientists and managers with current (2000-present), online water-stage and water-depth information for the entire freshwater portion of the Greater Everglades. Continuous daily spatial interpolations of the EDEN network stage data are presented on grid with 400-square-meter spacing. EDEN offers a consistent and documented dataset that can be used by scientists and managers to: (1) guide large-scale field operations, (2) integrate hydrologic and ecological responses, and (3) support biological and ecological assessments that measure ecosystem responses to the implementation of the Comprehensive Everglades Restoration Plan (CERP) (U.S. Army Corps of Engineers, 1999). The target users are biologists and ecologists examining trophic level responses to hydrodynamic changes in the Everglades. The first objective of this report is to validate the spatially continuous EDEN water-surface model for the Everglades, Florida developed by Pearlstine et al. (2007) by using an independent field-measured data-set. The second objective is to demonstrate two applications of the EDEN water-surface model: to estimate site-specific ground elevation by using the validated EDEN water-surface model and observed water depth data; and to create water-depth hydrographs for tree islands. We found that there are no statistically significant differences between model-predicted and field-observed water-stage data in both southern Water Conservation Area (WCA) 3A and WCA 3B. Tree island elevations were derived by subtracting field water-depth measurements from the predicted EDEN water-surface. Water-depth hydrographs were then computed by subtracting tree island elevations from the EDEN water stage. Overall, the model is reliable by a root mean square error (RMSE) of 3.31 cm. By region, the RMSE is 2.49 cm and 7.77 cm in WCA 3A and 3B, respectively. This new landscape-scale hydrological model has wide applications for ongoing research and management efforts that are vital to restoration of the Florida Everglades. The accurate, high-resolution hydrological data, generated over broad spatial and temporal scales by the EDEN model, provides a previously missing key to understanding the habitat requirements and linkages among native and invasive populations, including fish, wildlife, wading birds, and plants. The EDEN model is a powerful tool that could be adapted for other ecosystem-scale restoration and management programs worldwide.