This study demonstrates a fully Bayesian approach to multilevel/hierarchical linear growth modeling using freely available software. Further, the study incorporates informative prior distributions for fixed effect estimates using an objective approach. The objective approach uses previous sample results to form prior distributions included in subsequent samples analyses, a process referred to as Bayesian updating. Further, a method for model checking is outlined based on fit indices including information criteria (i.e., Akaike information criterion, Bayesian information criterion, and deviance information criterion) and approximate Bayes factor calculations. For this demonstration, five distinct samples of schools in the process of implementing School-Wide Positive Behavior Interventions and Supports (SWPBIS) collected from 2008 to 2013 were used with the unit of analysis being the school. First, the within-year SWPBIS fidelity growth was modeled as a function of time measured in months from initial measurement occasion. Uninformative priors were used to estimate growth parameters for the 2008-09 sample, and both uninformative and informative priors based on previous years' samples were used to model data from the 2009-10, 2010-11, 2011-12, 2012-13 samples. Bayesian estimates were also compared to maximum likelihood (ML) estimates, and reliability information is provided. Second, an additional three examples demonstrated how to include predictors into the growth model with demonstrations for: (a) the inclusion of one school-level predictor (years implementing) of SWPBIS fidelity growth, (b) several school-level predictors (relative socio-economic status, size, and geographic location), and (c) school and district predictors (sustainability factors hypothesized to be related to implementation processes) in a three-level growth model. Interestingly, Bayesian models estimated with informative prior distributions in all cases resulted in more optimal fit indices than models estimated with uninformative prior distributions.

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This thesis presents a novel method for the correlation of FEM results to experimental test results known as the "Load updating method." Specifically, the load updating method uses the math model from the FEM and the strains measured from experimental or flight test data as inputs and then predicts the loads in the FEM which would result in strains that would correlate best to the measured strains in the least squared sense. In this research, the load updating method is applied to the analysis of a complex frame structure whose validation is challenging due to the complex nature of its structural behavior, load distributions, and error derived from residual strains. A FEM created for this structure is used to generate strain data for thirty-two different load cases. These same thirty-two load cases are replicated in an experimental setup consisting of the frame, supporting structure, and thirty actuators which are used to load the frame according to the specifications for each of the thirty-two load conditions. A force-strain matrix is created from the math model in NASTRAN using unit loads which are separately applied to each load point in order to extract strain results for each of the locations of the seventy-four strain gages. The strain data from the structural test and the force-strain matrix is then input into a Matlab code which is created to perform the load updating method. This algorithm delivers a set of coefficients which in turn gives the updated loads. These loads are applied to the FEM and the strain values extracted for correlation to the strains from test data. It is found that the load updating method applied to this structure produces strains which correlate well to the experimental strain data. Although the loads found using the load updating method do not perfectly match those which are applied during the test, this error is primarily attributed to residual strains within the structure. In summary, the load updating method provides a way to predict loads which, when applied to the FEM, would result in strains that correlate best to the experimental strains. Ultimately, this method could prove especially useful for predicting loads in experimental and flight test structures and could aid greatly in the Federal Aviation Administration (FAA) certification process. / Master of Science

To prevent loss of lives during seasonal disasters, relief agencies distribute critical supplies and provide lifesaving services to the affected populations. Despite agencies' efforts, frequently occuring disasters increase the cost of relief operations. The purpose of our study is to minimize the cost of relief operations, considering that such disasters cause random demand. To achieve this, we have formulated a series of models, which are distinct from the current studies in three ways. First, to the best of our knowledge, we are the first ones to capture both perishable and durable products together. Second, we have aggregated multiple products in a different way than current studies do. This unique aggregation requires less data than that of other types of aggregation. Finally, our models are compatible with the practical data generated by FEMA. Our models offer insights on the impacts of various parameters on optimum cost and order size. The analyses of correlation of demand and quality of information offer interesting insights; for instance, under certain cases, the quality of information does not influence cost. Our study has considered both risk averse and risk neutral approaches and provided insights. The insights obtained from our models are expected to help agencies reduce the cost of operations by choosing cost effective suppliers.

<p><p>Previous research has indicated that meaningful background speech affects individuals reading comprehension performance differently and that this difference is related to working memory capacity. But what mechanism in working memory that is involved is not well understood. The present study’s main purpose was to investigate if individual differences in susceptibility to effects of speech on reading comprehension are moderated by working memory capacity as measured by the number updating task and two different mechanisms within this construct; delayed suppression (i.e. the inhibition of information that once was task-relevant but no longer is) and immediate suppression (i.e. the inhibition of processed but irrelevant information, while withholding attention focused on the to-be-recalled task-relevant items). Forty participants performed a number updating task and a reading comprehension task in silence and with meaningful background speech. The results indicated that the immediate suppression mechanism moderates the effects of background speech on reading comprehension. Those who can’t handle the interference from the background speech let the task-irrelevant information interfere with the ongoing cognitive task and therefore are more likely to be distracted by the background speech while reading a text.</p></p>

Previous research has indicated that meaningful background speech affects individuals reading comprehension performance differently and that this difference is related to working memory capacity. But what mechanism in working memory that is involved is not well understood. The present study’s main purpose was to investigate if individual differences in susceptibility to effects of speech on reading comprehension are moderated by working memory capacity as measured by the number updating task and two different mechanisms within this construct; delayed suppression (i.e. the inhibition of information that once was task-relevant but no longer is) and immediate suppression (i.e. the inhibition of processed but irrelevant information, while withholding attention focused on the to-be-recalled task-relevant items). Forty participants performed a number updating task and a reading comprehension task in silence and with meaningful background speech. The results indicated that the immediate suppression mechanism moderates the effects of background speech on reading comprehension. Those who can’t handle the interference from the background speech let the task-irrelevant information interfere with the ongoing cognitive task and therefore are more likely to be distracted by the background speech while reading a text.

Background: Systematic reviews (SRs) should be kept up-to-date to maintain importance in informing health care policy and practice. However, updating policies and practices of health care organizations (HCOs) that fund or conduct SRs are either unclear or non-existent. Objective: To examine updating policies and practices of relevant HCOs. Primary Research Design: An exploratory Internet survey of 195 HCOs within the international SR community. Results: The completed response rate was 58% (n=114) across 26 countries. Although 57% of organizations reported to have a formal updating policy, 59% reported updating practices as irregular. Moreover, 54% estimated more than half of their respective SRs were likely out dated. Resource constraints were a prominent barrier. Most (70%) supported centralizing updating efforts across institutions or agencies. Significance: This research provides a baseline glimpse of the state of updating among HCOs globally involved in evidence synthesis and therefore adds to a limited body of knowledge.

Background: Systematic reviews (SRs) should be kept up-to-date to maintain importance in informing health care policy and practice. However, updating policies and practices of health care organizations (HCOs) that fund or conduct SRs are either unclear or non-existent. Objective: To examine updating policies and practices of relevant HCOs. Primary Research Design: An exploratory Internet survey of 195 HCOs within the international SR community. Results: The completed response rate was 58% (n=114) across 26 countries. Although 57% of organizations reported to have a formal updating policy, 59% reported updating practices as irregular. Moreover, 54% estimated more than half of their respective SRs were likely out dated. Resource constraints were a prominent barrier. Most (70%) supported centralizing updating efforts across institutions or agencies. Significance: This research provides a baseline glimpse of the state of updating among HCOs globally involved in evidence synthesis and therefore adds to a limited body of knowledge.

Distinctive encoding is the processing of unique item-specific information in the context of more general relational or organizational information. It enhances memory performance for both younger and older adults (Smith, 2006). The current work examined how adults use distinctive encoding to aid their free recall performance and whether task experience alters subsequent use of a distinctive encoding strategy. At study participants saw a series of five-item taxonomically categorized lists (e.g., FRUITS). They were first required to generate a category-consistent label (e.g., TASTY FRUIT). In the guided condition, they were then required to generate a single word representing either (1) another category-consistent characteristic (e.g., GROWS) or (2) a characteristic that distinguished a study target from the other items (e.g., FUZZY for the target KIWI). In the self-initiated condition, participants were allowed to select an encoding strategy on their own. After test, all participants completed a second study-test phase with self-initiated strategies. Younger adults initially rated distinctive encoding as more effective, relative to relational encoding, than did older adults, and this difference persisted after test experience, indicating an age difference in learning about the relative superiority of distinctive processing. Consistent with these ratings, distinctive encoding was implemented more so by unguided younger adults than older adults in phase 1. However, both strategy use and recall performance were similar across age and study conditions in phase 2. Both older and younger adults were capable of utilizing distinctive encoding effectively following task experience, although perceptions of strategic effectiveness did not always correspond to self-initiated study behaviors.

The updating process, which consists of building mental models and adapting them to the changes occurring in the environment, is impaired in neglect patients. A simple rock-paper-scissors experiment was conducted in our lab to examine updating impairments in neglect patients. The results of this experiment demonstrate a significant difference between the performance of healthy and brain damaged participants. While healthy controls did not show any difficulty learning the computer’s strategy, right brain damaged patients failed to learn the computer’s strategy. A computational modeling approach is employed to help us better understand the reason behind this difference and thus learn more about the updating process in healthy people and its impairment in right brain damaged patients. Broadly, we hope to learn more about the nature of the updating process, in general. Also the hope is that knowing what must be changed in the model to “brain-damage” it can shed light on the updating deficit in right brain damaged patients. To do so I adapted a pattern detection method named “ELPH” to a reinforcement-learning human decision making model called “RELPH”. This model is capable of capturing the behavior of both healthy and right brain damaged participants in our task according to our defined measures. Indeed, this thesis is an effort to discuss the possible differences among these groups employing this computational model.