999 resultados para Méthode quantitative


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Modern statistical models and computational methods can now incorporate uncertainty of the parameters used in Quantitative Microbial Risk Assessments (QMRA). Many QMRAs use Monte Carlo methods, but work from fixed estimates for means, variances and other parameters. We illustrate the ease of estimating all parameters contemporaneously with the risk assessment, incorporating all the parameter uncertainty arising from the experiments from which these parameters are estimated. A Bayesian approach is adopted, using Markov Chain Monte Carlo Gibbs sampling (MCMC) via the freely available software, WinBUGS. The method and its ease of implementation are illustrated by a case study that involves incorporating three disparate datasets into an MCMC framework. The probabilities of infection when the uncertainty associated with parameter estimation is incorporated into a QMRA are shown to be considerably more variable over various dose ranges than the analogous probabilities obtained when constants from the literature are simply ‘plugged’ in as is done in most QMRAs. Neglecting these sources of uncertainty may lead to erroneous decisions for public health and risk management.

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The affects associated with culture, the values inherent in cultures and the identification of cultural assumptions are popular topics in recent management and Information Systems (IS) research. The main focus in relevant IS research over the years, has been on the comparison of cultural artifacts in different cultural settings. Despite these studies we need to ask whether there is a general approach to how culture can be researched in a rigorous manner? What are the issues that arise in cross- cultural research that have a bearing on decisions about a suitable research approach? What are the most appropriate methodologies to be used in cross-cultural research? Which is more appropriate, a qualitative, a quantitative or a mixed- method research approach? This paper will discuss important considerations in the process of deciding on the best research approach for cross-cultural projects. A case study will be then be reported as an example revealing the merits of integrating qualitative and quantitative approaches followed by a thorough discussion on the issues which may arise during this process.

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Issue addressed: Measures of 'social identity' and 'psychological sense of community' were included within a broader formative research inquiry to gain insight into the identity characteristics and level of connectedness among older recreational road travellers (commonly known as Grey Nomads). The research sought to gain insights on how best to reach or speak to this growing driver cohort. ----- ----- Method: Participants included 631 older recreational road travellers ranging in age from 50 years to over 80 years. Data were obtained through three scales which were incorporated into a larger formative research survey; an identity hierarchy, the Three Factor Model of Social Identity and the Sense of Community Index. ----- ----- Results: Older recreational road travellers see themselves principally as couples, with social group identity being secondary. Although many identified to some degree with the Grey Nomad identity, when asked to self categorise as either members of the Broad Network of Recreational Vehicle Travellers or as Grey Nomads, the majority categorised themselves as the former. Those identifying as Grey Nomads, however, reported significantly higher levels of 'social identification' and 'sense of community'. ----- ----- Conclusion: The Grey Nomad identity may not be the best identity at which to target road safety messages for this cohort. Targeting travelling 'couples' may be more efficacious. Using the 'Grey Nomad' identity is likely to reap at least some success, however, given that many identified to some degree with this group identity. Those identifying as Grey Nomads may be more open to community participation or behaviour change given their significantly higher levels of 'social identity' and 'sense of community'.

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Current knowledge about the relationship between transport disadvantage and activity space size is limited to urban areas, and as a result, very little is known to date about this link in a rural context. In addition, although research has identified transport disadvantaged groups based on their size of activity spaces, these studies have, however, not empirically explained such differences and the result is often a poor identification of the problems facing disadvantaged groups. Research has shown that transport disadvantage varies over time. The static nature of analysis using the activity space concept in previous research studies has lacked the ability to identify transport disadvantage in time. Activity space is a dynamic concept; and therefore possesses a great potential in capturing temporal variations in behaviour and access opportunities. This research derives measures of the size and fullness of activity spaces for 157 individuals for weekdays, weekends, and for a week using weekly activity-travel diary data from three case study areas located in rural Northern Ireland. Four focus groups were also conducted in order to triangulate the quantitative findings and to explain the differences between different socio-spatial groups. The findings of this research show that despite having a smaller sized activity space, individuals were not disadvantaged because they were able to access their required activities locally. Car-ownership was found to be an important life line in rural areas. Temporal disaggregation of the data reveals that this is true only on weekends due to a lack of public transport services. In addition, despite activity spaces being at a similar size, the fullness of activity spaces of low-income individuals was found to be significantly lower compared to their high-income counterparts. Focus group data shows that financial constraint, poor connections both between public transport services and between transport routes and opportunities forced individuals to participate in activities located along the main transport corridors.

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Virtual methods to assess the fitting of a fracture fixation plate were proposed recently, however with limitations such as simplified fit criteria or manual data processing. This study aims to automate a fit analysis procedure using clinical-based criteria, and then to analyse the results further for borderline fit cases. Three dimensional (3D) models of 45 bones and of a precontoured distal tibial plate were utilized to assess the fitting of the plate automatically. A Matlab program was developed to automatically measure the shortest distance between the bone and the plate at three regions of interest and a plate-bone angle. The measured values including the fit assessment results were recorded in a spreadsheet as part of the batch-process routine. An automated fit analysis procedure will enable the processing of larger bone datasets in a significantly shorter time, which will provide more representative data of the target population for plate shape design and validation. As a result, better fitting plates can be manufactured and made available to surgeons, thereby reducing the risk and cost associated with complications or corrective procedures. This in turn, is expected to translate into improving patients' quality of life.

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Unstructured text data, such as emails, blogs, contracts, academic publications, organizational documents, transcribed interviews, and even tweets, are important sources of data in Information Systems research. Various forms of qualitative analysis of the content of these data exist and have revealed important insights. Yet, to date, these analyses have been hampered by limitations of human coding of large data sets, and by bias due to human interpretation. In this paper, we compare and combine two quantitative analysis techniques to demonstrate the capabilities of computational analysis for content analysis of unstructured text. Specifically, we seek to demonstrate how two quantitative analytic methods, viz., Latent Semantic Analysis and data mining, can aid researchers in revealing core content topic areas in large (or small) data sets, and in visualizing how these concepts evolve, migrate, converge or diverge over time. We exemplify the complementary application of these techniques through an examination of a 25-year sample of abstracts from selected journals in Information Systems, Management, and Accounting disciplines. Through this work, we explore the capabilities of two computational techniques, and show how these techniques can be used to gather insights from a large corpus of unstructured text.

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With estimates that two billion of the world’s population will be 65 years or older by 2050, ensuring that older people ‘age well’ is an international priority. To date, however, there is significant disagreement and debate about how to define and measure ‘ageing well’, with no consensus on either terminology or measurement. Thus, this chapter describes the research rationale, methodology and findings of the Australian Active Ageing Study (Triple A Study), which surveyed 2620 older Australians to identify significant contributions to quality of life for older people: work, learning, social participation, spirituality, emotional wellbeing, health, and life events. Exploratory factor analyses identified eight distinct elements (grouped into four key concepts) which appear to define ‘active ageing’ and explained 55% of the variance: social and life participation (25%), emotional health (22%), physical health and functioning (4%) and security (4%). These findings highlight the importance of understanding and supporting the social and emotional dimensions of ageing, as issues of social relationships, life engagement and emotional health dominated the factor structure. Our intension is that this paper will prompt informed debate and discussion on defining and measuring active ageing, facilitating exploration and understanding of the complexity of issues that intertwine, converge and enhance the ageing experience.

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In this paper, spatially offset Raman spectroscopy (SORS) is demonstrated for non-invasively investigating the composition of drug mixtures inside an opaque plastic container. The mixtures consisted of three components including a target drug (acetaminophen or phenylephrine hydrochloride) and two diluents (glucose and caffeine). The target drug concentrations ranged from 5% to 100%. After conducting SORS analysis to ascertain the Raman spectra of the concealed mixtures, principal component analysis (PCA) was performed on the SORS spectra to reveal trends within the data. Partial least squares (PLS) regression was used to construct models that predicted the concentration of each target drug, in the presence of the other two diluents. The PLS models were able to predict the concentration of acetaminophen in the validation samples with a root-mean-square error of prediction (RMSEP) of 3.8% and the concentration of phenylephrine hydrochloride with an RMSEP of 4.6%. This work demonstrates the potential of SORS, used in conjunction with multivariate statistical techniques, to perform non-invasive, quantitative analysis on mixtures inside opaque containers. This has applications for pharmaceutical analysis, such as monitoring the degradation of pharmaceutical products on the shelf, in forensic investigations of counterfeit drugs, and for the analysis of illicit drug mixtures which may contain multiple components.

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Navigational safety analysis relying on collision statistics is often hampered because of low number of observations. A promising alternative approach that overcomes this problem is proposed in this paper. By analyzing critical vessel interactions this approach proactively measures collision risk in port waters. The proposed method is illustrated for quantitative measurement of collision risks in Singapore port fairways, and validated by examining correlations between the measured risks with those perceived by pilots. This method is an ethically appealing alternative to the collision-based analysis for fast, reliable and effective safety assessment, thus possesses great potential for managing collision risks in port waters.

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The concept of Six Sigma was initiated in the 1980s by Motorola. Since then it has been implemented in several manufacturing and service organizations. Till now Six Sigma implementation is mostly limited to healthcare and financial services in private sector. Its implementation is now gradually picking up in services such as call center, education, construction and related engineering etc. in private as well as public sector. Through a literature review, a questionnaire survey, and multiple case study approach the paper develops a conceptual framework to facilitate widening the scope of Six Sigma implementation in service organizations. Using grounded theory methodology, this study develops theory for Six Sigma implementation in service organizations. The study involves a questionnaire survey and case studies to understand and build a conceptual framework. The survey was conducted in service organizations in Singapore and exploratory in nature. The case studies involved three service organizations which implemented Six Sigma. The objective is to explore and understand the issues highlighted by the survey and the literature. The findings confirm the inclusion of critical success factors, critical-to-quality characteristics, and set of tools and techniques as observed from the literature. In case of key performance indicator, there are different interpretations about it in literature and also by industry practitioners. Some literature explain key performance indicator as performance metrics whereas some feel it as key process input or output variables, which is similar to interpretations by practitioners of Six Sigma. The response of not relevant and unknown to us as reasons for not implementing Six Sigma shows the need for understanding specific requirements of service organizations. Though much theoretical description is available about Six Sigma, but there has been limited rigorous academic research on it. This gap is far more pronounced about Six Sigma implementation in service organizations, where the theory is not mature enough. Identifying this need, the study contributes by going through theory building exercise and developing a conceptual framework to understand the issues involving its implementation in service organizations.

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Objective: To use our Bayesian method of motor unit number estimation (MUNE) to evaluate lower motor neuron degeneration in ALS. Methods: In subjects with ALS we performed serial MUNE studies. We examined the repeatability of the test and then determined whether the loss of MUs was fitted by an exponential or Weibull distribution. Results: The decline in motor unit (MU) numbers was well-fitted by an exponential decay curve. We calculated the half life of MUs in the abductor digiti minimi (ADM), abductor pollicis brevis (APB) and/or extensor digitorum brevis (EDB) muscles. The mean half life of the MUs of ADM muscle was greater than those of the APB or EDB muscles. The half-life of MUs was less in the ADM muscle of subjects with upper limb than in those with lower limb onset. Conclusions: The rate of loss of lower motor neurons in ALS is exponential, the motor units of the APB decay more quickly than those of the ADM muscle and the rate of loss of motor units is greater at the site of onset of disease. Significance: This shows that the Bayesian MUNE method is useful in following the course and exploring the clinical features of ALS. 2012 International Federation of Clinical Neurophysiology.