A Q-Methodology approach to investigating the relationship between level of reflection and typologies among prospective teachers in the physics learning assistant program at Florida International University

The purpose of this mixed methods study was to understand physics Learning Assistants' (LAs) views on reflective teaching, expertise in teaching, and LA program teaching experience and to determine if views predicted level of reflection evident in writing. Interviews were conducted in Phase One, Q methodology was used in Phase Two, and level of reflection in participants' writing was assessed using a rubric based on Hatton and Smith's (1995) "Criteria for the Recognition of Evidence for Different Types of Reflective Writing" in Phase Three. Interview analysis revealed varying perspectives on content knowledge, pedagogical knowledge, and experience in relation to expertise in teaching. Participants revealed that they engaged in reflection on their teaching, believed reflection helps teachers improve, and found peer reflection beneficial. Participants believed teaching experience in the LA program provided preparation for teaching, but that more preparation was needed to teach. Three typologies emerged in Phase Two. Type One LAs found participation in the LA program rewarding and believed expertise in teaching does not require expertise in content or pedagogy, but it develops over time from reflection. Type Two LAs valued reflection, but not writing reflections, felt the LA program teaching experience helped them decide on non-teaching careers and helped them confront gaps in their physics knowledge. Type Three LAs valued reflection, believed expertise in content and pedagogy are necessary for expert teaching, and felt LA program teaching experience increased their likelihood of becoming teachers, but did not prepare them for teaching. Writing assignments submitted in Phase Three were categorized as 19% descriptive writing, 60% descriptive reflections, and 21% dialogic reflections. No assignments were categorized as critical reflection. Using ordinal logistic regression, typologies that emerged in Phase Two were not found to be predictors for the level of reflection evident in the writing assignments. In conclusion, viewpoints of physics LAs were revealed, typologies among them were discovered, and their writing gave evidence of their ability to reflect on teaching. These findings may benefit faculty and staff in the LA program by helping them better understand the views of physics LAs and how to assess their various forms of reflection.

Diabetes Self Management: Are Cuban Americans Receiving Quality Health Care?

Objectives: We investigated the relationship among factors predicting inadequate glucose control among 182 Cuban-American adults (Females=110, Males=72) with type 2 diabetes mellitus (CAA). Study Design: Cross-sectional study of CAA from a randomized mailing list in two counties of South Florida Methods: Fasted blood parameters and anthropometric measures were collected during the study. BMI was calculated (kg/ m2). Characteristics and diabetes care of CAA were self-reported Participants were screened by trained interviewers for heritage and diabetes status (inclusion criteria: self-reported having type 2 diabetes; age  35 years, male and female; not pregnant or lactating; no thyroid disorders; no major psychiatric disorders). Participants signed informed consent form. Statistical analyses used SPSS and included descriptive statistic, multiple logistic and ordinal logistic regression models, where all CI 95%. Results: Eighty-eight percent of CAA had BMI of ≥ 25 kg/ m2. Only 54% reported having a diet prescribed/told to schedule meals. We found CAA told to schedule meals were 3.62 more likely to plan meals (1.81, 7.26), p<0.001) and given a prescribed diet, controlling for age, corresponded with following a meal plan OR 4.43 (2.52, 7.79, p<0.001). The overall relationship for HbA1c < 8.5 to following a meal plan was OR 9.34 (2.84, 30.7. p<0.001). Conclusions: The advantage of having a medical professional prescribe a diet seems to be an important environmental support factor in this sample’s diabetes care, since obesity rates are well above the national average. Nearly half CAA are not given dietary guidance, yet our results indicate CAA may improve glycemic control by receiving dietary instructions.

Avaliação da efetividade do Programa de Alimentação do Trabalhador

This study analyzed the Worker’s Healthy Eating Program in Rio Grande do Norte state (RN) to assess its possible impact on the nutritional status of the workers benefitted. To that end, we conducted a cross-sectional observational prospective study based on a multistage stratified random sample comparing 26 small and medium-sized companies from the Manufacturing Sector (textiles, food and beverages, and nonmetallic minerals) of RN, divided into two equal groups (WFP and Non WFP). Interviews were conducted at each company by trained interviewers from Tuesday to Saturday between September and December 2014. Data were collected on the company (characterization and information regarding the program’s desired results) and workers (personal and professional information, anthropometrics, health, lifestyle and food consumed the previous day). Population estimates were calculated for RN on the characteristics of workers and the study variables. The main variable was BMI. The secondary variables were waist circumference (WC), nutritional diagnosis, calorie intake, blood pressure, metabolic variables and lifestyle indicators. The statistical method used was hierarchical mixed effects linear regression for interval variables and hierarchical mixed effects logistic regression for binary variables. The variables measured in ordinal scales were analyzed by ordinal logistic regression adjusted for correlated variables, adopting robust standard errors. The results for interval variables are presented as point estimates and their 95% confidence intervals; and as odds-ratios and their 95% confidence intervals for binary variables. The Fisher’s exact and Student’s t-tests were used for simple comparisons between proportions and means, respectively. Differences were considered statistically significant at p<0.05. A total of 1069 workers were interviewed, of which 541 were from the WFP group and 528 from the Non WFP group. Subjects were predominantly males and average age was 34.5 years. Significant intergroup differences were observed for schooling level, income above 1 MW (minimum wage) and specific training for their position at the company. The results indicated a significant difference between the BMI of workers benefitted, which was on average 0.989 kg/m2 higher than the BMI of workers from the Non WFP group (p=0.002); and between the WC, with the waist circumference of WFP group workers an average of 1.528 cm larger (p<0.05). Higher prevalence of overweight and obesity (p<0.001) and cardiovascular risk (p=0.038) were recorded in the WFP group. Tests on the possible effect of the WFP on health (blood pressure and metabolic indicators) and lifestyle indicators (smoking, alcohol consumption and exercise) were not significant. With respect to worker’s diets, differences were significant for consumption of saturated fat (lunch and daily intake), salt (lunch, other meals and daily intake) and proteins (other meals and daily intake), with higher consumption of these nutrients in the WFP group. The study showed a possible positive impact of the WFP on nutritional status (BMI and WC) among the workers benefitted. No possible effects of the program were observed for the lifestyle indicators studied. Workers benefitted consumed less salt, saturated fat and protein. The relevance of the WFP is recognized for this portion of society and it is understood that, if the program can reach and impact those involved, the development of educational initiatives aimed at nutritional and food safety may also exert a positive influence.

Can we improve the statistical analysis of stroke trials? Statistical re-analysis of functional outcomes in stroke trials

Background: Most large acute stroke trials have been neutral. Functional outcome is usually analysed using a yes or no answer, e.g. death or dependency vs. independence. We assessed which statistical approaches are most efficient in analysing outcomes from stroke trials. Methods: Individual patient data from acute, rehabilitation and stroke unit trials studying the effects of interventions which alter functional outcome were assessed. Outcomes included modified Rankin Scale, Barthel Index, and ‘3 questions’. Data were analysed using a variety of approaches which compare two treatment groups. The results for each statistical test for each trial were then compared. Results: Data from 55 datasets were obtained (47 trials, 54,173 patients). The test results differed substantially so that approaches which use the ordered nature of functional outcome data (ordinal logistic regression, t-test, robust ranks test, bootstrapping the difference in mean rank) were more efficient statistically than those which collapse the data into 2 groups (chi square) (ANOVA p<0.001). The findings were consistent across different types and sizes of trial and for the different measures of functional outcome. Conclusions: When analysing functional outcome from stroke trials, statistical tests which use the original ordered data are more efficient and more likely to yield reliable results. Suitable approaches included ordinal logistic regression, t-test, and robust ranks test.

Can we improve the statistical analysis of stroke trials? Statistical reanalysis of functional outcomes in stroke trials

Background and Purpose—Most large acute stroke trials have been neutral. Functional outcome is usually analyzed using a yes or no answer, eg, death or dependency versus independence. We assessed which statistical approaches are most efficient in analyzing outcomes from stroke trials. Methods—Individual patient data from acute, rehabilitation and stroke unit trials studying the effects of interventions which alter functional outcome were assessed. Outcomes included modified Rankin Scale, Barthel Index, and “3 questions”. Data were analyzed using a variety of approaches which compare 2 treatment groups. The results for each statistical test for each trial were then compared. Results—Data from 55 datasets were obtained (47 trials, 54 173 patients). The test results differed substantially so that approaches which use the ordered nature of functional outcome data (ordinal logistic regression, t test, robust ranks test, bootstrapping the difference in mean rank) were more efficient statistically than those which collapse the data into 2 groups (2; ANOVA, P0.001). The findings were consistent across different types and sizes of trial and for the different measures of functional outcome. Conclusions—When analyzing functional outcome from stroke trials, statistical tests which use the original ordered data are more efficient and more likely to yield reliable results. Suitable approaches included ordinal logistic regression, test, and robust ranks test.

Elicitator : an expert elicitation tool for regression in ecology

Expert elicitation is the process of retrieving and quantifying expert knowledge in a particular domain. Such information is of particular value when the empirical data is expensive, limited, or unreliable. This paper describes a new software tool, called Elicitator, which assists in quantifying expert knowledge in a form suitable for use as a prior model in Bayesian regression. Potential environmental domains for applying this elicitation tool include habitat modeling, assessing detectability or eradication, ecological condition assessments, risk analysis, and quantifying inputs to complex models of ecological processes. The tool has been developed to be user-friendly, extensible, and facilitate consistent and repeatable elicitation of expert knowledge across these various domains. We demonstrate its application to elicitation for logistic regression in a geographically based ecological context. The underlying statistical methodology is also novel, utilizing an indirect elicitation approach to target expert knowledge on a case-by-case basis. For several elicitation sites (or cases), experts are asked simply to quantify their estimated ecological response (e.g. probability of presence), and its range of plausible values, after inspecting (habitat) covariates via GIS.

Temporally adaptive estimation of logistic classifiers on data streams

Modern technology has allowed real-time data collection in a variety of domains, ranging from environmental monitoring to healthcare. Consequently, there is a growing need for algorithms capable of performing inferential tasks in an online manner, continuously revising their estimates to reflect the current status of the underlying process. In particular, we are interested in constructing online and temporally adaptive classifiers capable of handling the possibly drifting decision boundaries arising in streaming environments. We first make a quadratic approximation to the log-likelihood that yields a recursive algorithm for fitting logistic regression online. We then suggest a novel way of equipping this framework with self-tuning forgetting factors. The resulting scheme is capable of tracking changes in the underlying probability distribution, adapting the decision boundary appropriately and hence maintaining high classification accuracy in dynamic or unstable environments. We demonstrate the scheme's effectiveness in both real and simulated streaming environments. © Springer-Verlag 2009.

Autoavaliação do estado de saúde: associação com fatores sociodemográficos, hábitos de vida, morbidade e experiência de discriminação racial em inquérito populacional no Brasil

A autoavaliação do estado de saúde (AAS) é um indicador de saúde amplamente utilizado e influenciado por uma grande variedade de fatores. Em particular, existem evidências crescentes de que a discriminação racial é um importante fator de risco para eventos mórbidos em saúde e seu impacto na saúde da população brasileira ainda é pouco explorado. No primeiro artigo, o objetivo principal é investigar a associação entre AAS e fatores sociodemográficos, comportamentais e de morbidade. No segundo artigo, o objetivo é estimar a associação entre discriminação racial e diferentes desfechos em saúde, a saber, AAS, morbidade física e depressão ajustando por variáveis sociodemográficas, comportamentos relacionados à saúde e Índice de Massa Corporal, na população de pretos e pardos. O presente estudo possui delineamento seccional, baseado nos dados do inquérito de abrangência nacional Pesquisa Dimensão Social das Desigualdades. Os entrevistados responderam a questionários estruturados e suas medidas antropométricas foram aferidas. No primeiro artigo, foram avaliados 12.324 indivíduos, entre chefes de família e cônjuges, com idade maior ou igual a 20 anos. No segundo artigo, foram avaliados 3.863 chefes de família que responderam a pergunta sobre discriminação racial e que se classificaram como pretos e pardos. AAS foi avaliada por meio de pergunta obtida do instrumento de qualidade de vida SF-36 e, para o primeiro artigo, foi analisada de forma dicotômica em AAS boa (categorias de resposta excelente, muito boa e boa) e AAS ruim (categorias de resposta razoável e ruim). No segundo artigo, esse desfecho foi analisado utilizando-se as 5 categorias de resposta. As análises foram realizadas utilizando-se modelos de regressão logística uni e multivariados, para dados binários (artigo 1) ou ordinais (artigo 2). Os resultados foram apresentados na forma de Odds Ratios com os respectivos intervalos de 95% de confiança. Maior faixa etária, analfabetismo, tabagismo, obesidade e doenças crônicas estiveram associados a maior chance de AAS ruim. Para cada incremento na faixa de renda, observou-se uma redução de 20% na chance de relatar AAS ruim. Atividade física esteve associada a menor chance de AAS ruim. No segundo artigo, exposição à discriminação racial esteve associada com aumento na chance de relato de pior AAS, de morbidade física e de depressão. O presente estudo identificou a influência de diversos fatores sociais, demográficos, comportamentos relacionados à saúde e morbidade física na AAS. O estudo demonstrou ainda que a discriminação racial está associada negativamente aos três desfechos em saúde avaliados (AAS, morbidade física e depressão). Esses resultados podem traçar um perfil de subgrupos populacionais mais vulneráveis, ou seja, com maior risco de contrair doenças ou de procurar o serviço de saúde por uma doença já existente, auxiliando na definição de populações-alvo para o adequado planejamento de políticas e de programas de promoção de saúde.

Log-Burr XII regression models with censored data

In survival analysis applications, the failure rate function may frequently present a unimodal shape. In such case, the log-normal or log-logistic distributions are used. In this paper, we shall be concerned only with parametric forms, so a location-scale regression model based on the Burr XII distribution is proposed for modeling data with a unimodal failure rate function as an alternative to the log-logistic regression model. Assuming censored data, we consider a classic analysis, a Bayesian analysis and a jackknife estimator for the parameters of the proposed model. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and compared to the performance of the log-logistic and log-Burr XII regression models. Besides, we use sensitivity analysis to detect influential or outlying observations, and residual analysis is used to check the assumptions in the model. Finally, we analyze a real data set under log-Buff XII regression models. (C) 2008 Published by Elsevier B.V.

Aplicação de redes neurais na classificação de rentabilidade futura de empresas

Este trabalho tem por motivação evidenciar a eficiência de redes neurais na classificação de rentabilidade futura de empresas, e desta forma, prover suporte para o desenvolvimento de sistemas de apoio a tomada de decisão de investimentos. Para serem comparados com o modelo de redes neurais, foram escolhidos o modelo clássico de regressão linear múltipla, como referência mínima, e o de regressão logística ordenada, como marca comparativa de desempenho (benchmark). Neste texto, extraímos dados financeiros e contábeis das 1000 melhores empresas listadas, anualmente, entre 1996 e 2006, na publicação Melhores e Maiores – Exame (Editora Abril). Os três modelos foram construídos tendo como base as informações das empresas entre 1996 e 2005. Dadas as informações de 2005 para estimar a classificação das empresas em 2006, os resultados dos três modelos foram comparados com as classificações observadas em 2006, e o modelo de redes neurais gerou o melhor resultado.

Use of hydroclimatic forecasts for improved water management in central Texas

Accurate seasonal to interannual streamflow forecasts based on climate information are critical for optimal management and operation of water resources systems. Considering most water supply systems are multipurpose, operating these systems to meet increasing demand under the growing stresses of climate variability and climate change, population and economic growth, and environmental concerns could be very challenging. This study was to investigate improvement in water resources systems management through the use of seasonal climate forecasts. Hydrological persistence (streamflow and precipitation) and large-scale recurrent oceanic-atmospheric patterns such as the El Niño/Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), the Atlantic Multidecadal Oscillation (AMO), the Pacific North American (PNA), and customized sea surface temperature (SST) indices were investigated for their potential to improve streamflow forecast accuracy and increase forecast lead-time in a river basin in central Texas. First, an ordinal polytomous logistic regression approach is proposed as a means of incorporating multiple predictor variables into a probabilistic forecast model. Forecast performance is assessed through a cross-validation procedure, using distributions-oriented metrics, and implications for decision making are discussed. Results indicate that, of the predictors evaluated, only hydrologic persistence and Pacific Ocean sea surface temperature patterns associated with ENSO and PDO provide forecasts which are statistically better than climatology. Secondly, a class of data mining techniques, known as tree-structured models, is investigated to address the nonlinear dynamics of climate teleconnections and screen promising probabilistic streamflow forecast models for river-reservoir systems. Results show that the tree-structured models can effectively capture the nonlinear features hidden in the data. Skill scores of probabilistic forecasts generated by both classification trees and logistic regression trees indicate that seasonal inflows throughout the system can be predicted with sufficient accuracy to improve water management, especially in the winter and spring seasons in central Texas. Lastly, a simplified two-stage stochastic economic-optimization model was proposed to investigate improvement in water use efficiency and the potential value of using seasonal forecasts, under the assumption of optimal decision making under uncertainty. Model results demonstrate that incorporating the probabilistic inflow forecasts into the optimization model can provide a significant improvement in seasonal water contract benefits over climatology, with lower average deficits (increased reliability) for a given average contract amount, or improved mean contract benefits for a given level of reliability compared to climatology. The results also illustrate the trade-off between the expected contract amount and reliability, i.e., larger contracts can be signed at greater risk.

Towards greater accuracy in individual-tree mortality regression

Background mortality is an essential component of any forest growth and yield model. Forecasts of mortality contribute largely to the variability and accuracy of model predictions at the tree, stand and forest level. In the present study, I implement and evaluate state-of-the-art techniques to increase the accuracy of individual tree mortality models, similar to those used in many of the current variants of the Forest Vegetation Simulator, using data from North Idaho and Montana. The first technique addresses methods to correct for bias induced by measurement error typically present in competition variables. The second implements survival regression and evaluates its performance against the traditional logistic regression approach. I selected the regression calibration (RC) algorithm as a good candidate for addressing the measurement error problem. Two logistic regression models for each species were fitted, one ignoring the measurement error, which is the “naïve” approach, and the other applying RC. The models fitted with RC outperformed the naïve models in terms of discrimination when the competition variable was found to be statistically significant. The effect of RC was more obvious where measurement error variance was large and for more shade-intolerant species. The process of model fitting and variable selection revealed that past emphasis on DBH as a predictor variable for mortality, while producing models with strong metrics of fit, may make models less generalizable. The evaluation of the error variance estimator developed by Stage and Wykoff (1998), and core to the implementation of RC, in different spatial patterns and diameter distributions, revealed that the Stage and Wykoff estimate notably overestimated the true variance in all simulated stands, but those that are clustered. Results show a systematic bias even when all the assumptions made by the authors are guaranteed. I argue that this is the result of the Poisson-based estimate ignoring the overlapping area of potential plots around a tree. Effects, especially in the application phase, of the variance estimate justify suggested future efforts of improving the accuracy of the variance estimate. The second technique implemented and evaluated is a survival regression model that accounts for the time dependent nature of variables, such as diameter and competition variables, and the interval-censored nature of data collected from remeasured plots. The performance of the model is compared with the traditional logistic regression model as a tool to predict individual tree mortality. Validation of both approaches shows that the survival regression approach discriminates better between dead and alive trees for all species. In conclusion, I showed that the proposed techniques do increase the accuracy of individual tree mortality models, and are a promising first step towards the next generation of background mortality models. I have also identified the next steps to undertake in order to advance mortality models further.

Development of a Bayesian Joint Logistic Model to Better Study the Association between Haplotypes and Disease

In 2011, there will be an estimated 1,596,670 new cancer cases and 571,950 cancer-related deaths in the US. With the ever-increasing applications of cancer genetics in epidemiology, there is great potential to identify genetic risk factors that would help identify individuals with increased genetic susceptibility to cancer, which could be used to develop interventions or targeted therapies that could hopefully reduce cancer risk and mortality. In this dissertation, I propose to develop a new statistical method to evaluate the role of haplotypes in cancer susceptibility and development. This model will be flexible enough to handle not only haplotypes of any size, but also a variety of covariates. I will then apply this method to three cancer-related data sets (Hodgkin Disease, Glioma, and Lung Cancer). I hypothesize that there is substantial improvement in the estimation of association between haplotypes and disease, with the use of a Bayesian mathematical method to infer haplotypes that uses prior information from known genetics sources. Analysis based on haplotypes using information from publically available genetic sources generally show increased odds ratios and smaller p-values in both the Hodgkin, Glioma, and Lung data sets. For instance, the Bayesian Joint Logistic Model (BJLM) inferred haplotype TC had a substantially higher estimated effect size (OR=12.16, 95% CI = 2.47-90.1 vs. 9.24, 95% CI = 1.81-47.2) and more significant p-value (0.00044 vs. 0.008) for Hodgkin Disease compared to a traditional logistic regression approach. Also, the effect sizes of haplotypes modeled with recessive genetic effects were higher (and had more significant p-values) when analyzed with the BJLM. Full genetic models with haplotype information developed with the BJLM resulted in significantly higher discriminatory power and a significantly higher Net Reclassification Index compared to those developed with haplo.stats for lung cancer. Future analysis for this work could be to incorporate the 1000 Genomes project, which offers a larger selection of SNPs can be incorporated into the information from known genetic sources as well. Other future analysis include testing non-binary outcomes, like the levels of biomarkers that are present in lung cancer (NNK), and extending this analysis to full GWAS studies.

Systematic analysis of factors associated with progression and regression of ulcerative colitis in 918 patients.

BACKGROUND Studies that systematically assess change in ulcerative colitis (UC) extent over time in adult patients are scarce. AIM To assess changes in disease extent over time and to evaluate clinical parameters associated with this change. METHODS Data from the Swiss IBD cohort study were analysed. We used logistic regression modelling to identify factors associated with a change in disease extent. RESULTS A total of 918 UC patients (45.3% females) were included. At diagnosis, UC patients presented with the following disease extent: proctitis [199 patients (21.7%)], left-sided colitis [338 patients (36.8%)] and extensive colitis/pancolitis [381 (41.5%)]. During a median disease duration of 9 [4-16] years, progression and regression was documented in 145 patients (15.8%) and 149 patients (16.2%) respectively. In addition, 624 patients (68.0%) had a stable disease extent. The following factors were identified to be associated with disease progression: treatment with systemic glucocorticoids [odds ratio (OR) 1.704, P = 0.025] and calcineurin inhibitors (OR: 2.716, P = 0.005). No specific factors were found to be associated with disease regression. CONCLUSIONS Over a median disease duration of 9 [4-16] years, about two-thirds of UC patients maintained the initial disease extent; the remaining one-third had experienced either progression or regression of the disease extent.