3 resultados para Secondary data analysis

em DRUM (Digital Repository at the University of Maryland)


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Policymakers make many demands of our schools to produce academic success. At the same time, community organizations, government agencies, faith-based institutions, and other groups often are providing support to students and their families, especially those from high-poverty backgrounds, that are meant to impact education but are often insufficient, uncoordinated, or redundant. In many cases, these institutions lack access to schools and school leaders. What’s missing from the dominant education reform discourse is a coordinated education-focused approach that mobilizes community assets to effectively improve academic and developmental outcomes for students. This study explores how education-focused comprehensive community change initiatives (CCIs) that utilize a partnership approach are organized and sustained. In this study, I examine three research questions: 1. Why and how do school system-level community change initiative (CCI) partnerships form? 2. What are the organizational, financial, and political structures that support sustainable CCIs? What, in particular, are their connections to the school systems they seek to impact? 3. What are the leadership functions and structures found within CCIs? How are leadership functions distributed across schools and agencies within communities? To answer these questions, I used a cross-case study approach that employed a secondary data analysis of data that were collected as part of a larger research study sponsored by a national organization. The original study design included site visits and extended interviews with educators, community leaders and practitioners about community school initiatives, one type of CCI. This study demonstrates that characteristics of sustained education-focused CCIs include leaders that are critical to starting the CCIs and are willing to collaborate across institutions, a focus on community problems, building on previous efforts, strategies to improve service delivery, a focus on education and schools in particular, organizational arrangements that create shared leadership and ownership for the CCI, an intermediary to support the initial vision and collaborative leadership groups, diversified funding approaches, and political support. These findings add to the literature about the growing number of education-focused CCIs. The study’s primary recommendation—that institutions need to work across boundaries in order to sustain CCIs organizationally, financially, and politically—can help policymakers as they develop new collaborative approaches to achieving educational goals.

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Objective: To examine sociodemographic and dental factors for associations with dental sealant placement in children and adolescents aged 6-18 years old. Methods: Secondary data analysis of 2011-2012 NHANES data was conducted. Multiple logistic regression models were used to assess relationships between predictor variables and sealant presence. Results: More than a third (37.1%) of children and adolescents have at least one sealant present; 67.9% of children compared with 40.4% of adolescents. Racial/ethnic differences exist, with Non-Hispanic black youth having the lowest odds of having sealants. Sealant placement odds vary by presence of dental home; the magnitude of the odds varies by age group. Those with untreated decay have lower odds of having sealants than those who do not have untreated decay (child OR: 2.6, 95% CI: 1.83-3.72; adolescent OR: 3.9, 95% CI: 2.59-6.07). Conclusion: Disparities exist in odds of sealant prevalence across racial/ethnic groups, income levels, and dental disease and visit characteristics. Further research is necessary to understand the reasons for these differences and to inform future interventions.

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The graph Laplacian operator is widely studied in spectral graph theory largely due to its importance in modern data analysis. Recently, the Fourier transform and other time-frequency operators have been defined on graphs using Laplacian eigenvalues and eigenvectors. We extend these results and prove that the translation operator to the i’th node is invertible if and only if all eigenvectors are nonzero on the i’th node. Because of this dependency on the support of eigenvectors we study the characteristic set of Laplacian eigenvectors. We prove that the Fiedler vector of a planar graph cannot vanish on large neighborhoods and then explicitly construct a family of non-planar graphs that do exhibit this property. We then prove original results in modern analysis on graphs. We extend results on spectral graph wavelets to create vertex-dyanamic spectral graph wavelets whose support depends on both scale and translation parameters. We prove that Spielman’s Twice-Ramanujan graph sparsifying algorithm cannot outperform his conjectured optimal sparsification constant. Finally, we present numerical results on graph conditioning, in which edges of a graph are rescaled to best approximate the complete graph and reduce average commute time.