Training civic bridge builders: Outcomes of community leadership development programs

As rural communities experience rapid economic, demographic, and political change, program interventions that focus on the development of community leadership capacity could be valuable. Community leadership development programs have been deployed in rural U.S. communities for the past 30 years by university extension units, chambers of commerce, and other nonprofit foundations. Prior research on program outcomes has largely focused on trainees’ self-reported change in individual leadership knowledge, skills, and attitudes. However, postindustrial leadership theories suggest that leadership in the community relies not on individuals but on social relationships that develop across groups akin to social bridging. The purpose of this study is to extend and strengthen prior evaluative research on community leadership development programs by examining program effects on opportunities to develop bridging social capital using more rigorous methods. Data from a quasi-experimental study of rural community leaders (n = 768) in six states are used to isolate unique program effects on individual changes in both cognitive and behavioral community leadership outcomes. Regression modeling shows that participation in community leadership development programs is associated with increased leadership development in knowledge, skills, attitudes, and behaviors that are a catalyst for social bridging. The community capitals framework is used to show that program participants are significantly more likely to broaden their span of involvement across community capital asset areas over time compared to non-participants. Data on specific program structure elements show that skills training may be important for cognitive outcomes while community development learning and group projects are important for changes in organizational behavior. Suggestions for community leadership program practitioners are presented.

Statistical modeling of protein lysate array data

The protein lysate array is an emerging technology for quantifying the protein concentration ratios in multiple biological samples. It is gaining popularity, and has the potential to answer questions about post-translational modifications and protein pathway relationships. Statistical inference for a parametric quantification procedure has been inadequately addressed in the literature, mainly due to two challenges: the increasing dimension of the parameter space and the need to account for dependence in the data. Each chapter of this thesis addresses one of these issues. In Chapter 1, an introduction to the protein lysate array quantification is presented, followed by the motivations and goals for this thesis work. In Chapter 2, we develop a multi-step procedure for the Sigmoidal models, ensuring consistent estimation of the concentration level with full asymptotic efficiency. The results obtained in this chapter justify inferential procedures based on large-sample approximations. Simulation studies and real data analysis are used to illustrate the performance of the proposed method in finite-samples. The multi-step procedure is simpler in both theory and computation than the single-step least squares method that has been used in current practice. In Chapter 3, we introduce a new model to account for the dependence structure of the errors by a nonlinear mixed effects model. We consider a method to approximate the maximum likelihood estimator of all the parameters. Using the simulation studies on various error structures, we show that for data with non-i.i.d. errors the proposed method leads to more accurate estimates and better confidence intervals than the existing single-step least squares method.