882 resultados para Methods : Data Analysis
Resumo:
Obtaining attribute values of non-chosen alternatives in a revealed preference context is challenging because non-chosen alternative attributes are unobserved by choosers, chooser perceptions of attribute values may not reflect reality, existing methods for imputing these values suffer from shortcomings, and obtaining non-chosen attribute values is resource intensive. This paper presents a unique Bayesian (multiple) Imputation Multinomial Logit model that imputes unobserved travel times and distances of non-chosen travel modes based on random draws from the conditional posterior distribution of missing values. The calibrated Bayesian (multiple) Imputation Multinomial Logit model imputes non-chosen time and distance values that convincingly replicate observed choice behavior. Although network skims were used for calibration, more realistic data such as supplemental geographically referenced surveys or stated preference data may be preferred. The model is ideally suited for imputing variation in intrazonal non-chosen mode attributes and for assessing the marginal impacts of travel policies, programs, or prices within traffic analysis zones.
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In this paper, we explore the use of Twitter as a political tool in the 2013 Australian Federal Election. We employ a ‘big data’ approach that combines qualitative and quantitative methods of analysis. By tracking the accounts of politicians and parties, and the tweeting activity to and around these accounts, as well as conversations on particular hashtagged topics, we gain a comprehensive insight into the ways in which Twitter is employed in the campaigning strategies of different parties. We compare and contrast the use of Twitter by political actors with its adoption by citizens as a tool for political conversation and participation. Our study provides an important longitudinal counterpoint, and opportunity for comparison, to the use of Twitter in previous Australian federal and state elections. Furthermore, we offer innovative methodologies for data gathering and evaluation that can contribute to the comparative study of the political uses of Twitter across diverse national media and political systems.
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Ethnographic methods have been widely used for requirements elicitation purposes in systems design, especially when the focus is on understanding users? social, cultural and political contexts. Designing an on-line search engine for peer-reviewed papers could be a challenge considering the diversity of its end users coming from different educational and professional disciplines. This poster describes our exploration of academic research environments based on different in situ methods such as contextual interviews, diary-keeping, job-shadowing, etc. The data generated from these methods is analysed using a qualitative data analysis software and subsequently is used for developing personas that could be used as a requirements specification tool.
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In this paper, we present fully Bayesian experimental designs for nonlinear mixed effects models, in which we develop simulation-based optimal design methods to search over both continuous and discrete design spaces. Although Bayesian inference has commonly been performed on nonlinear mixed effects models, there is a lack of research into performing Bayesian optimal design for nonlinear mixed effects models that require searches to be performed over several design variables. This is likely due to the fact that it is much more computationally intensive to perform optimal experimental design for nonlinear mixed effects models than it is to perform inference in the Bayesian framework. In this paper, the design problem is to determine the optimal number of subjects and samples per subject, as well as the (near) optimal urine sampling times for a population pharmacokinetic study in horses, so that the population pharmacokinetic parameters can be precisely estimated, subject to cost constraints. The optimal sampling strategies, in terms of the number of subjects and the number of samples per subject, were found to be substantially different between the examples considered in this work, which highlights the fact that the designs are rather problem-dependent and require optimisation using the methods presented in this paper.
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Background Heatwaves could cause the population excess death numbers to be ranged from tens to thousands within a couple of weeks in a local area. An excess mortality due to a special event (e.g., a heatwave or an epidemic outbreak) is estimated by subtracting the mortality figure under ‘normal’ conditions from the historical daily mortality records. The calculation of the excess mortality is a scientific challenge because of the stochastic temporal pattern of the daily mortality data which is characterised by (a) the long-term changing mean levels (i.e., non-stationarity); (b) the non-linear temperature-mortality association. The Hilbert-Huang Transform (HHT) algorithm is a novel method originally developed for analysing the non-linear and non-stationary time series data in the field of signal processing, however, it has not been applied in public health research. This paper aimed to demonstrate the applicability and strength of the HHT algorithm in analysing health data. Methods Special R functions were developed to implement the HHT algorithm to decompose the daily mortality time series into trend and non-trend components in terms of the underlying physical mechanism. The excess mortality is calculated directly from the resulting non-trend component series. Results The Brisbane (Queensland, Australia) and the Chicago (United States) daily mortality time series data were utilized for calculating the excess mortality associated with heatwaves. The HHT algorithm estimated 62 excess deaths related to the February 2004 Brisbane heatwave. To calculate the excess mortality associated with the July 1995 Chicago heatwave, the HHT algorithm needed to handle the mode mixing issue. The HHT algorithm estimated 510 excess deaths for the 1995 Chicago heatwave event. To exemplify potential applications, the HHT decomposition results were used as the input data for a subsequent regression analysis, using the Brisbane data, to investigate the association between excess mortality and different risk factors. Conclusions The HHT algorithm is a novel and powerful analytical tool in time series data analysis. It has a real potential to have a wide range of applications in public health research because of its ability to decompose a nonlinear and non-stationary time series into trend and non-trend components consistently and efficiently.
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PURPOSE To examine correlates and consequences of parents' encouragement of girls' physical activity (PA) for weight loss (ENCLOSS). METHODS Data were collected for 181 girls, mothers and fathers when girls were 9, 11, and 13 years old. Mothers and fathers completed a self-report questionnaire of ENCLOSS (e.g., “I have talked to my daughter about how to exercise to lose weight”). Correlates of ENCLOSS that were assessed include girls' Body Mass Index (BMI) z-score and parents' modeling of and logistic support for PA. Dependent variables assessed at age 13 include girls' self-reported and objectively-measured PA, enjoyment of physical activity, and weight concerns. Associations between ENCLOSS, girls' BMI, and parent's support for PA were assessed using spearman rank correlations. To examine links between ENCLOSS and the outcome variables, scores for ENCLOSS were divided into tertiles at each age. Three groups were created including girls who were in the highest tertile at each age (high ENCLOSS), girls who were in the lowest tertile at each age (low ENCLOSS), and girls who varied in their tertile ranking (mid ENCLOSS). Group differences in the outcome variables were assessed using regression analysis (referent group: low ENCLOSS), controlling for girls' BMI and the outcome variable at age 9. RESULTS Girls' with higher BMI had mothers and fathers who reported higher ENCLOSS (r = .61-. 69, p<. 0001). Parents'reports of ENCLOSS were not associated with modeling of or logistic support for PA. Girls in the high ENCLOSS group reported significantly lower enjoyment of PA and higher weight concerns at age 13, independent of covariates. No differences in PA were noted. CONCLUSION Parents who encourage their daughters to be active for weight loss do not model PA or facilitate girls' PA. Persistent encouragement of PA for weight loss may lead to low enjoyment of PA and higher weight concerns among adolescent girls.
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Background Early feeding practices lay the foundation for children’s eating habits and weight gain. Questionnaires are available to assess parental feeding but overlapping and inconsistent items, subscales and terminology limit conceptual clarity and between study comparisons. Our aim was to consolidate a range of existing items into a parsimonious and conceptually robust questionnaire for assessing feeding practices with very young children (<3 years). Methods Data were from 462 mothers and children (age 21–27 months) from the NOURISH trial. Items from five questionnaires and two study-specific items were submitted to a priori item selection, allocation and verification, before theoretically-derived factors were tested using Confirmatory Factor Analysis. Construct validity of the new factors was examined by correlating these with child eating behaviours and weight. Results Following expert review 10 factors were specified. Of these, 9 factors (40 items) showed acceptable model fit and internal reliability (Cronbach’s α: 0.61-0.89). Four factors reflected non-responsive feeding practices: ‘Distrust in Appetite’, ‘Reward for Behaviour’, ‘Reward for Eating’, and ‘Persuasive Feeding’. Five factors reflected structure of the meal environment and limits: ‘Structured Meal Setting’, ‘Structured Meal Timing’, ‘Family Meal Setting’, ‘Overt Restriction’ and ‘Covert Restriction’. Feeding practices generally showed the expected pattern of associations with child eating behaviours but none with weight. Conclusion The Feeding Practices and Structure Questionnaire (FPSQ) provides a new reliable and valid measure of parental feeding practices, specifically maternal responsiveness to children’s hunger/satiety signals facilitated by routine and structure in feeding. Further validation in more diverse samples is required.
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Most commentators understand that contemporary social, economic and environmental challenges require quality governance from global to local scales. While public scrutiny of governance has increased in recent years, the literature on frameworks and methods for analysis in complex, poly-centric and multi-thematic governance systems remains fragmented; displaying many disciplinary or sectoral biases. This paper establishes a stronger theory-based foundation for the analysis of complex governance systems. It also develops a clear analytical framework applicable across a vast array of differing governance themes, domains and scales (GSA). The key methodological steps and evaluative criteria for the GSA framework are determined and practical guidance for its application in reform is provided.
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Time series classification has been extensively explored in many fields of study. Most methods are based on the historical or current information extracted from data. However, if interest is in a specific future time period, methods that directly relate to forecasts of time series are much more appropriate. An approach to time series classification is proposed based on a polarization measure of forecast densities of time series. By fitting autoregressive models, forecast replicates of each time series are obtained via the bias-corrected bootstrap, and a stationarity correction is considered when necessary. Kernel estimators are then employed to approximate forecast densities, and discrepancies of forecast densities of pairs of time series are estimated by a polarization measure, which evaluates the extent to which two densities overlap. Following the distributional properties of the polarization measure, a discriminant rule and a clustering method are proposed to conduct the supervised and unsupervised classification, respectively. The proposed methodology is applied to both simulated and real data sets, and the results show desirable properties.
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This project recognized lack of data analysis and travel time prediction on arterials as the main gap in the current literature. For this purpose it first investigated reliability of data gathered by Bluetooth technology as a new cost effective method for data collection on arterial roads. Then by considering the similarity among varieties of daily travel time on different arterial routes, created a SARIMA model to predict future travel time values. Based on this research outcome, the created model can be applied for online short term travel time prediction in future.
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This project makes a contribution to knowledge about the successful leadership practices that enhance education for young people with a disability in a Queensland Secondary School. The project used a critical ethnographic approach with a variety of data collection methods and analysis. For example, the use of work diaries, semi-structured interviews, document analysis and observation. These leadership practices were found to be relevant to the development of inclusive schools for all learners.The most powerful leadership practices found were those used by the leader to challenge, interupt and replace exsisting discourse and processes that led to exclusion of students with a disability.
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In recent years, increasing focus has been made on making good business decisions utilizing the product of data analysis. With the advent of the Big Data phenomenon, this is even more apparent than ever before. But the question is how can organizations trust decisions made on the basis of results obtained from analysis of untrusted data? Assurances and trust that data and datasets that inform these decisions have not been tainted by outside agency. This study will propose enabling the authentication of datasets specifically by the extension of the RESTful architectural scheme to include authentication parameters while operating within a larger holistic security framework architecture or model compliant to legislation.
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Aim Evaluation or assessment of competence is an important step to ensure the safety and efficacy of health professionals, including dietitians. Most competency-based assessment studies are focussed on valid and reliable methods of assessment for the preparation of entry-level dietitians, few papers have explored student dietitians’ perceptions of these evaluations. This study aimed to explore the perceptions of recent graduates from accredited nutrition and dietetics training programs in Australia. It also aimed to establish the relevance of competency-based assessment to adequately prepare them for entry-level work roles. Methods A purposive sample of newly-graduated dietitians with a range of assessment experiences and varied employment areas was recruited. A qualitative approach, using in-depth interviews with 13 graduates, with differing assessment experiences was undertaken. Graduates were asked to reflect upon their competency-based assessment experiences whilst a student. Data was thematically analysed by multiple authors. Results Four themes emerged from the data analysis: (i) Transparency and consistency are critical elements of work-based competency assessment. (ii) Students are willing to take greater responsibility in their assessment process. (iii) Work-based competency assessment prepares students for employment. (iv) The relationship between students and their assessors can impact on the student experience and their assessment performance. Conclusions Understanding this unique perspective of students can improve evaluation of future health professionals and assist in designing valid competency-based assessment approaches.
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PURPOSE Every health care sector including hospice/palliative care needs to systematically improve services using patient-defined outcomes. Data from the national Australian Palliative Care Outcomes Collaboration aims to define whether hospice/palliative care patients' outcomes and the consistency of these outcomes have improved in the last 3 years. METHODS Data were analysed by clinical phase (stable, unstable, deteriorating, terminal). Patient-level data included the Symptom Assessment Scale and the Palliative Care Problem Severity Score. Nationally collected point-of-care data were anchored for the period July-December 2008 and subsequently compared to this baseline in six 6-month reporting cycles for all services that submitted data in every time period (n = 30) using individual longitudinal multi-level random coefficient models. RESULTS Data were analysed for 19,747 patients (46 % female; 85 % cancer; 27,928 episodes of care; 65,463 phases). There were significant improvements across all domains (symptom control, family care, psychological and spiritual care) except pain. Simultaneously, the interquartile ranges decreased, jointly indicating that better and more consistent patient outcomes were being achieved. CONCLUSION These are the first national hospice/palliative care symptom control performance data to demonstrate improvements in clinical outcomes at a service level as a result of routine data collection and systematic feedback.
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The aim of this study was to investigate the practice profile of emergency nurse practitioners across Australia. Nurse practitioners have been providing health service in the emergency setting internationally for more than 30 years, and evidence supports the value of this role in terms of patient satisfaction, effectiveness in improving service indicators, and acceptability of the role. The introduction of this service model has been instrumental in reducing waiting times for low-acuity patients and impacting positively on emergency department service delivery. Recent rapid uptake of this role internationally has outpaced development of the service model to inform education and ongoing service development. This was a national study that used interpretive research methods to identify the practice profile of emergency nurse practitioners. Data were collected from December 2012 to February 2013 through in-depth interviews. An inductive approach was used in data analysis to identify conceptual themes and develop an analysis framework. The study participants worked in a range of service models and managed patient presentations across all levels of acuity and complexity. The findings show that although there is no single definable model of the emergency nurse practitioner role in Australia, there are practice features that are common across all service models; these have been conceptualized as "modes of practice." This study has produced new knowledge about the practice profile of emergency nurse practitioners. The findings will inform development of practice standards for education and continuing professional development for emergency nurse practitioners and facilitate standardized operational definitions for ongoing research into this growing service model.