Despite having drawn from empirical evidence and cumulative prior expertise in the formulation of research questions as well as study design, each study is treated as a stand-alone product rather than positioned within a sequence of cumulative evidence. While results of prior studies are typically cited within the body of prior literature review, the actual analyses of newly collected data are usually conducted without taking these sources of prior information into consideration, and study findings are often accompanied by a list of limitations including the lack of generalizability of study results. As a result, individuals who hope to rely on the body of scholarly work for any type of evidence based decision-making either a) rely on large scale review work conducted with similar research objectives by third party organizations such as the Cochrane Collaboration, or b) face the challenge of synthesizing available empirical evidence. This task becomes particularly daunting when the issue under consideration is contentious, with large volumes of studies appearing to exist on both side of the argument. The primary objective of this dissertation is to investigate and illustrate the logic, use, and value of conducting Bayesian analyses using a "Community of Prior" (CP) in evaluations of interventions and programs in the behavioral sciences, education and related fields. Utilizing the basic underpinnings of Bayesian analysis, this dissertation outlines how prior information from multiple sources can be systematically incorporated in the analysis of newly collected data at hand. Rather than relying on a single (or a few hand-picked) prior specification(s)--an aspect frequently critiqued in conventional Bayesian analysis for potential subjectivity, the "Community of Prior" approach adopted in this dissertation allows for the incorporation of a potentially large number of sources for prior specification, thereby effectively considering prior evidence of completely opposing viewpoints. The main illustration of the implementation of such an approach will focus on analyses of the data from a large-scale evaluation of a behavioral intervention, i.e., the Treatment System Impact (TSI) study, designed to investigate the effectiveness of a behavioral treatment for incarcerated substance abusers. Of particular interest is the in-depth examination of practical considerations involved in the construction and implementation of CP, including: 1) identification of key segments which constitutes the community of interest (e.g., researchers, policy-makers, practitioners), as well as which segments of, or individuals in, the community seem to take different sides on the policy-debate or central issues of interest, 2) identification of available sources to be used for CP construction (e.g., scientific literature, experimental data, as well as expert elicitations), 3) formulation and specification of CP as probability distributions / densities (e.g., issues concerning location, form, spread). This includes considerations of determining, and perhaps varying, the weight placed on a prior relative to the likelihood (e.g., exchangeability), and 4) assessment of the sensitivity of inferences concerning the treatment of interest based on the various posterior distributions resulting from the specified CP. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]