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Introduction Symptoms are universally experienced and can be bothersome. There have been comparatively few studies of common symptoms in the UK general population. As a result, little is known about the current epidemiology of common symptoms in the UK. There are major methodological challenges to conducting an epidemiological study of common symptoms in the community. Current knowledge about the most appropriate methodological tools for collecting and interpreting data on symptoms is limited. A range of methods has been used in previous studies, but the most appropriate method is not known. An evaluation of the different methods that could be used for investigating common symptoms in the community was therefore required. Aims The overall aim of this study was to investigate and compare the use of different approaches to collecting epidemiological data on common symptoms, with a view to developing an evidence-base for the use of these different approaches. Methods A general review of methods used in epidemiological studies and a comprehensive review of methods used in previous epidemiological studies of symptoms were undertaken. The reviews informed the development of symptoms survey. Symptoms survey was conducted to collect epidemiological data on common symptoms in the community. A total of 1179 individuals was approached from four GP practices in the Aberdeen area and sent an invitation letter, information sheet and consent form. Potential participants were requested to select at least two data collection instruments from four options (postal questionnaire, diary, online questionnaire and telephone interview). All the instruments collected the same data (presence of symptoms, severity of symptoms, management of symptoms and quality of life). This allowed a direct comparison of the data collected by the various measures. Those agreeing to participate in the study were sent one of their chosen instruments in phase one of the study and a second (but different) one of their chosen instruments in phase two, which was conducted three weeks later.

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The primary purposes of this study were: (1) to study empirically differences that might occur among item-samples and (2) to compare empirically the effect of test item samples on matrix sampling estimates of the mean and variance of a population of test scores, and (3) to study empirically an analysis of variance design through multiple matrix sampling.

In order to determine the most effective ways to increase ridership in their communities, bicycle planners require quality data on bicycling behavior. Traditional bicycle data collection methods, however, are limited by the large amount of time and expertise required to process and analyze the data, by their inability to provide information at the level of detail needed to understand the complexities of bicycling behavior, and by issues related to sampling bias and poor respondent trip recall. Fortunately, a relatively new method for collecting travel data has emerged that has the potential to provide higher quality and lower cost bicycle data to local planning agencies than has previously been possible with traditional data collection methods: the use of global positioning system (GPS) sensors in smartphones. Researchers at The University of Texas recently evaluated the usefulness of one such smartphone application - “CycleTracks” - to collect bicycle route data. Over 3,600 unique trips were collected from around 300 cyclists in Austin, Texas between May and October, 2011. While they found the CycleTracks app to be useful for collecting a large dataset, to this point there has been only limited analysis of the route data in terms of its usefulness in the planning field. This report will explore the ways in which GPS route data collected from smartphones can address some of the limitations of traditional data collection methods. Austin is used as a case study to show how the GPS route data can be used to plan for network connectivity, to identify barriers in the bicycle network, and to analyze cycling behavior before and after the installation of new facilities. The report finds that despite a number of limitations, smartphone-based GPS data collection has the potential to become an important part of local planning agencies’ regular data collection efforts. / text

The purpose of this study was to explore the data-driven decision making process within the context of K-12 physical education. Although the topic has received extraordinary attention in other areas of education, it has yet to be investigated directly in physical education settings. A conceptual framework proposed by Mandinach, Honey, Light, and Brunner (2008) guided the investigation. Using a multi-site case study design, one school district previously awarded a Carol M. White Physical Education Program Grant served as the overarching case and eight schools within the district served as embedded cases. Eight physical education teachers, three district coordinators, one principal, and one school counselor participated in the study. Evidence was gathered through interviews, observations, documents, archival records, and artifacts. Analytic strategies such as pattern matching, examining rival explanations, and drawing diagrams were utilized to generate common themes within the data. Overall, findings indicated that physical education teachers collected substantial amounts of physical activity and fitness data aligned with policy requirements, often at the expense of data related to other important teaching domains. Evidence also indicated that teachers rarely transformed collected data into actionable knowledge. It seemed as though teachers were only collecting data because they were required to and held little value in the data once they were collected. Teachers reported that the data collection process was time-consuming and challenges associated with pedometers and information management systems served as barriers to the collection/organization process. In addition, professional development was not utilized to help teachers use data for effective teaching as district coordinators had limited access to teachers on designated professional development days. It is important to note that teachers had substantial concerns surrounding the validity and reliability of the data that were collected. This likely contributed to the low value that was placed upon data. Based upon the findings, ten recommendations for the enhancement of the DDDM process in physical education were generated. One of the most important recommendations is to provide physical education teachers with support in developing data literacy skills so they can take full advantage of the data they collect for the benefit of student learning. / text

Technology has become an inherent part of our daily lives. If we don’t want to abstain from the benefits technology brings, we have to acknowledge the fact that tech generates data and adjust our norms and habits to it. This thesis critiques how corporations and governmental institutions collect, store and analyze data of individuals. It discusses the economic and technological forces that stand behind the collection and usage of data in the past, today, and the near future. Beyond that, it alludes to political implications. The overarching goal is to stimulate reflection about culture and future. To achieve that, the design of an interactive educational web story within the browser is proposed. A curated personal data platform in combination with interactive web stories make data collection, data usage, and the risks of data aggregation visible. Business practices and interests are rendered transparent on the basis of users’ actual online behavior and exposure. The web stories allows to understand the meaning and value of the data traces users leave online. In five chapters, they experience the basic technologies of the Internet, business motivations, and surveillance practices in the context of their individual web browsing behavior. Each chapter invites to explore details of the topic to accommodate for individual need and interest in the matter. A critical reflection on the future of data collection is encouraged, tools and settings within the browser help users to protect their digital identities.

No description available.

In order to determine the most effective ways to increase ridership in their communities, bicycle planners require quality data on bicycling behavior. Traditional bicycle data collection methods, however, are limited by the large amount of time and expertise required to process and analyze the data, by their inability to provide information at the level of detail needed to understand the complexities of bicycling behavior, and by issues related to sampling bias and poor respondent trip recall. Fortunately, a relatively new method for collecting travel data has emerged that has the potential to provide higher quality and lower cost bicycle data to local planning agencies than has previously been possible with traditional data collection methods: the use of global positioning system (GPS) sensors in smartphones. Researchers at The University of Texas recently evaluated the usefulness of one such smartphone application - "CycleTracks" - to collect bicycle route data. Over 3,600 unique trips were collected from around 300 cyclists in Austin, Texas between May and October, 2011. While they found the CycleTracks app to be useful for collecting a large dataset, to this point there has been only limited analysis of the route data in terms of its usefulness in the planning field. This report will explore the ways in which GPS route data collected from smartphones can address some of the limitations of traditional data collection methods. Austin is used as a case study to show how the GPS route data can be used to plan for network connectivity, to identify barriers in the bicycle network, and to analyze cycling behavior before and after the installation of new facilities. The report finds that despite a number of limitations, smartphone-based GPS data collection has the potential to become an important part of local planning agencies' regular data collection efforts.

Thesis (M.S.)--Ohio University, June, 1999. / Title from PDF t.p.