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Children are conceptualized many ways by economists-- as sources of utility for their parents, investments, recipients of care, and public goods. Despite the understanding that children are also people, the economic literature is lacking in analysis of children as actors, making choices with consequences for economic development. Using a capability-driven approach and an emphasis on co-evolutionary processes of institutional and individual change, with mixed qualitative and quantitative methods, my dissertation analyzes the role of children in long-term economic development at the community level. I use a case study of community-based, out-of-school time (OST) programs for low-income youth funded through the 21st Century Community Learning Center (21st CCLC) to analyze the role of youth in economic development. OST programs provide community-level benefits such as reductions in juvenile crime and foster economic development by creating linkages between the state, the market, the community, and the family. My study contributes to the body of interdisciplinary research on OST programs, and is situated in the middle ground between case studies with very small samples and quantitative studies with a narrow focus on academic performance as measured by grades. The 21st CCLC programs in New York State are unique in their emphasis on partnerships between schools and community-based organizations. An analysis of the costs and benefits of OST programs shows that the benefits of programs such as 21st CCLC substantially outweigh the costs. Using Geographic Information Systems and statistical analysis, I examine the relationship between eligibility for 21st CCLC funding, demographic characteristics related to the need for free or low-cost OST programs, and the presence of 21st CCLC programs, and find that the presence of these programs cannot be explained solely through the characteristics of people who will be served by them. Additionally, it is clear that there are not enough 21st CCLC programs to serve all eligible communities, raising questions about the scale of funding as well as its distribution.

Introduktion: Populationen som lider av obesitas ökar och därmed förekommer allt oftare i antalet anestesier i Sverige. Obesitas är en riskfaktor för svår luftväg och det är därmed av vikt att förstå dess inverkan på klinisk luftvägshantering. Uppemot 30% av morbiditet och mortalitet vid anestesi är relaterad till luftvägshantering och är därmed den största orsaken till komplikationer inom anestesisjukvården. Patienter med obesitas har bland annat minskad funktionell residual kapacitet samt ökad metabolism som leder till minskad apnétolerans.  Nyckelfaktorer för säker luftvägshantering är att identifiera svår luftväg, planera inför luftvägsproblem, behålla lugn och fokus, ha tekniska färdigheter, och använda sig av hjälpmedel. Syfte: Syftet med denna studie var att observera anestesipersonalens tillvägagångssätt vid intubation med generell anestesi av patienter med obesitas. Metod: I denna studie användes icke-experimentell mixed method med deduktiv ansats. Datainsamling genomfördes med strukturerade intervjuer och observationer. Data analyserades med manifest kvalitativ innehållsanalys, kvantitativ innehållsanalys samt deskriptiva statistiska analyser. Resultat: Deltagares luftvägsbedömning särskiljer sig inte för patienter med obesitas jämfört med patienter med BMI <30 utifrån vad som kunde observeras dock menar deltagarna på att de bedömer lokalisation av fettvävnad. Det var extra viktigt med planeringen och förberedelserna när det kommer till patienter med obesitas och hur obesa patienter är påverkar förberedelserna. att deltagarna är extra fokuserade när det kommer till att intubera patienter med obesitas. Majoriteten av deltagarna framförde att positioneringen av patienter med obesitas var viktig till exempel genom att merparten av deltagarna rekommenderade en så kallad obesitaskudde som hjälpmedel vid intubation dock observerades användning av denna av endast en tredjedel av deltagarna. Slutsats: Anestesipersonal tillvägagångssätt vid intubation av obesa patienter som denna studie utgått ifrån används av majoriteten av deltagarna. Dock finns det fortfarande diskrepanser mellan litteratur och den kliniska verkligheten i luftvägshantering av obesa patienter och denna studie antyder att tillvägagångssättet vid intubation kan optimeras ytterligare.

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While countless studies have collected data on the effectiveness of various problem- solving models, few have attempted to identify which components of the training process are helpful to participants. Two teacher teams consisting of seven participants were trained in an adapted version of the Team-initiated Problem Solving (TIPS) model, observed each meeting, and provided performance feedback regarding how well they had adhered to the model. A mixed method approach was used to collect quantitative data in the form of the teams' adherence to the TIPS model and qualitative data once successful adherence to the model had been shown. Researchers interviewed each participant and used some strategies from the critical incident technique (CIT) to identify which components the training and performance feedback process were helpful, unhelpful, and which components they wished had been there that weren't. Themes among incidents reported as helpful were the expectation of meeting in professional learning community (PLC) teams to solve problems and consistent feedback on how closely they were adhering to the model. Unhelpful and wish list incidents included the difficult to use problem- solving form, structural components of meetings and training, and a desire to have more participants in meetings, particularly ones from other grades. Researchers and administrators who are looking to train teams with the TIPS model are recommended to use consistent performance feedback, include participants in the planning of the study, and have more question- and-answer style mini-trainings spaced throughout the observation phase. Lastly, two alternate problem-solving forms are presented as options to be used in place of the current meeting minutes form provided by the TIPS model. Both alternate versions aim to retain the components of the model, but with increased usability.

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This thesis presents simple efficient algorithms to estimate distribution parameters and to construct prediction intervals for location-scale families. Specifically, we study two scenarios: one is a frequentist method for a general location--scale family and then extend to a 3-parameter distribution, another is a Bayesian method for the Gumbel distribution. At the end of the thesis, a generalized bootstrap resampling scheme is proposed to construct prediction intervals for data with an unknown distribution. Our estimator construction begins with the equivariance principle, and then makes use of unbiasedness principle. These two estimates have closed form and are functions of the sample mean, sample standard deviation, sample size, as well as the mean and variance of a corresponding standard distribution. Next, we extend the previous result to estimate a 3-parameter distribution which we call a mixed method. A central idea of the mixed method is to estimate the location and scale parameters as functions of the shape parameter. The sample mean is a popular estimator for the population mean. The mean squared error (MSE) of the sample mean is often large, however, when the sample size is small or the scale parameter is greater than the location parameter. To reduce the MSE of our location estimator, we introduce an adaptive estimator. We will illustrate this by the example of the power Gumbel distribution. The frequentist approach is often criticized as failing to take into account the uncertainty of an unknown parameter, whereas a Bayesian approach incorporates such uncertainty. The present Bayesian analysis for the Gumbel data is achieved numerically as it is hard to obtain an explicit form. We tackle the problem by providing an approximation to the exponential sum of Gumbel random variables. Next, we provide two efficient methods to construct prediction intervals. The first one is a Monte Carlo method for a general location-scale family, based on our previous parameter estimation. Another is the Gibbs sampler, a special case of Markov Chain Monte Carlo. We derive the predictive distribution by making use of an approximation to the exponential sum of Gumbel random variables . Finally, we present a new generalized bootstrap and show that Efron's bootstrap re-sampling is a special case of the new re-sampling scheme. Our result overcomes the issue of the bootstrap of its ``inability to draw samples outside the range of the original dataset.'' We give an applications for constructing prediction intervals, and simulation shows that generalized bootstrap is better than that of the bootstrap when the sample size is small. The last contribution in this thesis is an improved GRS method used in nuclear engineering for construction of non-parametric tolerance intervals for percentiles of an unknown distribution. Our result shows that the required sample size can be reduced by a factor of almost two when the distribution is symmetric. The confidence level is computed for a number of distributions and then compared with the results of applying the generalized bootstrap. We find that the generalized bootstrap approximates the confidence level very well. / Dissertation / Doctor of Philosophy (PhD)