What is the most effective way for a researcher to read and analyze life-story materials? Using a new model for the classification of types of readings, Narrative Research shows readers how to read, analyze and interpret life story materials. The authors first introduce readers to four models of reading and interpreting a text: the holistic-content reading, the holistic-form reading, categorical-content reading, and categorical-form reading. They next present two complete narratives so that readers can compare the author?s interpretations against the actual text as well as try to analyze the stories on their own. The subsequent chapters provide readings, interpretations, and analyses of the narrative data from the four models. Each of the examples is highlighted by sharing with the reader some of the processes of the researcher? work, including various considerations, doubts, self-criticism, and dialogues about the narratives. Readers wanting to learn more about life story research will not only learn the tools for doing this analysis, but will be inspired to contribute their own creativity to this developing field.

This is the ideal book to get you up and running with the basics of qualitative data analysis. It breaks everything down into a series of simple steps and introduces the practical tools and techniques you need to turn your transcripts into meaningful research. Using multidisciplinary data from interviews and focus groups Jamie Harding provides clear guidance on how to apply key research skills such as making summaries, identifying similarities, drawing comparisons and using codes. The book sets out real world applicable advice, provides easy to follow best practice and helps you to: * Manage and sort your data * Find your argument and define your conclusions * Answer your research question * Write up your research for assessment and dissemination Clear, pragmatic and honest this book will give you the perfect framework to start understanding your qualitative data and to finish your research project.

Every new tractor now contains built-in sensors that collect data and stream it to cloud-based infrastructure. Seed and chemical companies are using these data, and these agribusinesses are a form of big tech alongside firms like Google and Facebook. The Immaculate Conception of Data peeks behind the secretive legal agreements surrounding agricultural big data to trace how it is used and with what consequences. Agribusinesses are among the oldest oligopoly corporations in the world, and their concentration gives them an advantage over other food system actors. Kelly Bronson explores what happens when big data get caught up in pre-existing arrangements of power. Her richly ethnographic account details the work of corporate scientists, farmers using the data, and activist "hackers" building open-source data platforms. Actors working in private and public contexts have divergent views on whom new technology is for, how it should be developed, and what kinds of agriculture it should support. Surprisingly, despite their differences, these groups share a way of speaking about data and its value for the future. Bronson calls this the immaculate conception of data, arguing that this phenomenon is a dangerous framework for imagining big data and what it might do for society. Drawing our attention to agriculture as an important new site for big tech criticism, The Immaculate Conception of Data uniquely bridges science and technology studies, critical data studies, and food studies, bringing to light salient issues related to data justice and a sustainable food system.

This book provides a comprehensive introduction by an extraordinary range of experts to the recent and rapidly developing field of learning analytics. Some of the finest current thinkers about ways to interpret and benefit from the increasing amount of evidence from learners' experiences have taken time to explain their methods, describe examples, and point out new underpinnings for the field. Together, they show how this new field has the potential to dramatically increase learner success through deeper understanding of the academic, social-emotional, motivational, identity and meta-cognitive context each learner uniquely brings. Learning analytics is much more than "analyzing learning data"-it is about deeply understanding what learning activities work well, for whom, and when. Learning Analytics in Education provides an essential framework, as well as guidance and examples, for a wide range of professionals interested in the future of learning. If you are already involved in learning analytics, or otherwise trying to use an increasing density of evidence to understand learners' progress, these leading thinkers in the field may give you new insights. If you are engaged in teaching at any level, or training future teachers/faculty for this new, increasingly technology-enhanced learning world, and want some sense of the potential opportunities (and pitfalls) of what technology can bring to your teaching and students, these forward-thinking leaders can spark your imagination. If you are involved in research around uses of technology, improving learning measurements, better ways to use evidence to improve learning, or in more deeply understanding human learning itself, you will find additional ideas and insights from some of the best thinkers in the field here. If you are involved in making administrative or policy decisions about learning, you will find new ideas (and dilemmas) coming your way from inevitable changes in how we design and deliver instruction, how we measure the outcomes, and how we provide feedback to students, teachers, developers, administrators, and policy-makers. For all these players, the trick will be to get the most out of all the new developments to efficiently and effectively improve learning performance, without getting distracted by "shiny" technologies that are disconnected from how human learning and development actually work.

According to a wry saying among radiologists, finding a tumour in a mammogram is like finding a snowball in a blizzard. Up to thirty percent of breast-cancer diagnoses are given to those who have no cancer at all. Medicine is subject to far more uncertainty than we commonly acknowledge. While it is portrayed a science, it can sometimes be scarily close to educated guesswork. Covering everything from the efficacy of Prozac to the regular barrage of health advice by the media, Snowball in a Blizzard is a profound meditation on why it's essential that doctors and their patients know what we don't know. The world is more complicated than we like to believe. Informed by years of frontline medical experience and filled with personal reflections, this important book is filled with counter-intuitive revelations about flawed reasoning, helpful guidance and hard-earned insight. It will change the way you view the health of yourself, your loved ones or your patients.

'This book provides an excellent reference guide to basic theoretical arguments, practical quantitative techniques and the methodologies that the majority of social science researchers are likely to require for postgraduate study and beyond' - Environment and Planning 'The book provides researchers with guidance in, and examples of, both quantitative and qualitative modes of analysis, written by leading practitioners in the field. The editors give a persuasive account of the commonalities of purpose that exist across both modes, as well as demonstrating a keen awareness of the different things that each offers the practising researcher' - Clive Seale, Brunel University 'With the appearance of this handbook, data analysts no longer have to consult dozens of disparate publications to carry out their work. The essential tools for an intelligent telling of the data story are offered here, in thirty chapters written by recognized experts. ' - Michael Lewis-Beck, F Wendell Miller Distinguished Professor of Political Science, University of Iowa 'This is an excellent guide to current issues in the analysis of social science data. I recommend it to anyone who is looking for authoritative introductions to the state of the art. Each chapter offers a comprehensive review and an extensive bibliography and will be invaluable to researchers wanting to update themselves about modern developments' - Professor Nigel Gilbert, Pro Vice-Chancellor and Professor of Sociology, University of Surrey This is a book that will rapidly be recognized as the bible for social researchers. It provides a first-class, reliable guide to the basic issues in data analysis, such as the construction of variables, the characterization of distributions and the notions of inference. Scholars and students can turn to it for teaching and applied needs with confidence. The book also seeks to enhance debate in the field by tackling more advanced topics such as models of change, causality, panel models and network analysis. Specialists will find much food for thought in these chapters. A distinctive feature of the book is the breadth of coverage. No other book provides a better one-stop survey of the field of data analysis. In 30 specially commissioned chapters the editors aim to encourage readers to develop an appreciation of the range of analytic options available, so they can choose a research problem and then develop a suitable approach to data analysis.

Fully updated, this revised edition describes the statistical aspects of both the design and analysis of trials, with particular emphasis on the more recent methods of analysis.About 8000 clinical trials are undertaken annually in all areas of medicine, from the treatment of acne to the prevention of cancer. Correct interpretation of the data from such trials depends largely on adequate design and on performing the appropriate statistical analyses. This book provides a useful guide to medical statisticians and others faced with the often difficult problems of designing and analysing clinical trials.

Margarita Pivovarova is an Assistant Professor of Education Economics at the Mary Lou Fulton Teachers College at Arizona State University. Her research interests include education policy and related quantitative research. Specifically, her research studies the economic consequences of accountability and financial incentives in education. In addition, she studies peer interactions in school contexts and optimal classroom or school design.

This guide offers support to anyone using statistics and research data on a regular or occasional basis by explaining specific indicators, classifications, terminologies, and sources in short, easy-to-understand entries. Users can dip into the guide whenever they are unsure about the meaning or scope of a specific statistical indicator or terminology, or are unclear about the nature of a specific survey or classification.
This excellent research tool concentrates on economic, business, and demographic data, and conforms to internationally agreed classifications and techniques.

Fundamentals of MEG and EEG: Biophysics, Instrumentation, and Data Analysis gives graduate students and researchers a technical understanding of the fundamentals of MEG and EEG that will enable them to gain expertise in the state-of-the-art, understand the generation, measurement and modeling of electromagnetic brain signals, understand the relationship of MEG/EEG with other brain imaging methods, design MEG/EEG measurement systems and evaluate their performance, and develop and evaluate data analysis methods.