From the quality of the air we breathe to the national leaders we choose, data and statistics are a pervasive feature of daily life and daily news. But how do news, numbers and public opinion interact with each other - and with what impacts on society at large? Featuring an international roster of established and emerging scholars, this book is the first comprehensive collection of research into the little understood processes underpinning the uses/misuses of statistical information in journalism and their socio-psychological and political effects. Moving beyond the hype around "data journalism," News, Numbers and Public Opinion delves into a range of more latent, fundamental questions such as: * Is it true that most citizens and journalists do not have the necessary skills and resources to critically process and assess numbers? * How do/should journalists make sense of the increasingly data-driven world? * What strategies, formats and frames do journalists use to gather and represent different types of statistical data in their stories? * What are the socio-psychological and political effects of such data gathering and representation routines, formats and frames on the way people acquire knowledge and form attitudes? * What skills and resources do journalists and publics need to deal effectively with the influx of numbers into in daily work and life - and how can newsrooms and journalism schools meet that need? The book is a must-read for not only journalists, journalism and media scholars, statisticians and data scientists but also anybody interested in the interplay between journalism, statistics and society.

New media has brought constant evolution to professional journalism practices and news genres. Online news practices challenge the occupational jurisdiction of journalism with a multiplicity of conflicting and competing journalistic ideals. In order to prepare journalism students to meet the demands of online journalism today, journalism schools have developed courses that emphasize journalistic practice on online news platforms and tools, such as Twitter, WordPress.com, Soundslides Plus, etc. Drawing on the theoretical lens of digital literacies, Multimedia News Storytelling as Digital Literacies problematizes the emphasis on transmission of certain professional values and news formats without raising students' critical awareness that there can be diversity of values. Methodologically, the present study proposes a genre-aware, semiotic-aware, critical framework that aims at analyzing digital literacies required and practiced by online journalists. It simultaneously encompasses dimensions of professional culture, professional practices, and abstraction of instantiated meaning making via multimodal semiotic resources. Multimedia News Storytelling as Digital Literacies is ideal for courses in journalism and mass communication, curriculum studies, and digital literacies. The book is a valuable resource for online journalism educators, journalism students, and online journalism practitioners.

Edited by Terri D. Pigott, Ann Marie Ryan, and Charles Tocci, the purpose of this volume is to present high-quality reviews that examine change to teaching practice from a variety of perspectives and a range of disciplines with an eye toward the enormous scope of the field. Taken as a whole, this volume presents a compelling profile of the core challenges and opportunities facing those engaged in the work of changing teaching practice and those who research these efforts. Divided into four sections, the first section of this volume delves into the history and policy of changing teaching practice, the second set of chapters consider the capacity of teachers to make changes, the third set of chapters review literature examining how to change practice in numerous settings in various ways, and the final section of the volume centers on emerging issues for practice. This volume considers some of the most critical problems facing educators and scholars today: how our history shapes our present-day possibilities, how we develop the capacity of educators to change and improve practice, the innumerable aspects that can be changed, which dimensions of teaching should we prioritize, and what emerging issues will shape this work in the coming years?

More students study management and organization studies than ever, the number of business schools worldwide continues to rise, and more management research is being published in a greater number of journals than could have been imagined twenty years ago. Dennis Tourish looks beneath the surface of this progress to expose a field in crisis and in need of radical reform. He identifies the ways in which management research has lost its way, including a remoteness from the practical problems that managers and employees face, a failure to replicate key research findings, poor writing, endless obscure theorizing, and an increasing number of research papers being retracted for fraud and other forms of malpractice. Tourish suggests fundamental changes to remedy these issues, enabling management research to become more robust, more interesting and more valuable to society. A must read for academics, practising managers, university administrators and policy makers within higher education.

Throughout the world, voters lack access to information about politicians, government performance, and public services. Efforts to remedy these informational deficits are numerous. Yet do informational campaigns influence voter behavior and increase democratic accountability? Through the first project of the Metaketa Initiative, sponsored by the Evidence in Governance and Politics (EGAP) research network, this book aims to address this substantive question and at the same time introduce a new model for cumulative learning that increases coordination among otherwise independent researcher teams. It presents the overall results (using meta-analysis) from six independently conducted but coordinated field experimental studies, the results from each individual study, and the findings from a related evaluation of whether practitioners utilize this information as expected. It also discusses lessons learned from EGAP's efforts to coordinate field experiments, increase replication of theoretically important studies across contexts, and increase the external validity of field experimental research.

Meaningful use of advanced Bayesian methods requires a good understanding of the fundamentals. This engaging book explains the ideas that underpin the construction and analysis of Bayesian models, with particular focus on computational methods and schemes. The unique features of the text are the extensive discussion of available software packages combined with a brief but complete and mathematically rigorous introduction to Bayesian inference. The text introduces Monte Carlo methods, Markov chain Monte Carlo methods, and Bayesian software, with additional material on model validation and comparison, transdimensional MCMC, and conditionally Gaussian models. The inclusion of problems makes the book suitable as a textbook for a first graduate-level course in Bayesian computation with a focus on Monte Carlo methods. The extensive discussion of Bayesian software - R/R-INLA, OpenBUGS, JAGS, STAN, and BayesX - makes it useful also for researchers and graduate students from beyond statistics.

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.

Geometric and topological inference deals with the retrieval of information about a geometric object using only a finite set of possibly noisy sample points. It has connections to manifold learning and provides the mathematical and algorithmic foundations of the rapidly evolving field of topological data analysis. Building on a rigorous treatment of simplicial complexes and distance functions, this self-contained book covers key aspects of the field, from data representation and combinatorial questions to manifold reconstruction and persistent homology. It can serve as a textbook for graduate students or researchers in mathematics, computer science and engineering interested in a geometric approach to data science.

Biopower & data In her book BioMachtData, Sophie Reyer looks at data and the phenomenon of "dataism," the homage paid to the unlimited flow of data. She describes "dataism" as a theory that has evolved into a veritable religion, yielding not only prophecies but also commandments, such as "Increase the flow of data!" Which means, in essence, "Consume and produce!" In the process, humanity recedes into the background, and the free flow or even flood of information becomes the new value. Based on Michel Foucault's concept of biopower, Reyer develops artistic-philosophical approaches in words and verbal images - in the form of essays, monologues, dialogues, and theatrical fragments. She examines various figures of dataism, from incels to nerds and heroines. A linguistic-artistic and philosophical examination of biopower in times of unlimited data flow From data to "dataism" - in essays, experimental texts, and images A new book by Sophie Reyer, following on from the publication BioMachtTheater (2020)

An extensively revised and expanded third edition of the successful textbook on analysis and visualization of social networks integrating theory, applications, and professional software for performing network analysis (Pajek). The main structural concepts and their applications in social research are introduced with exercises. Pajek software and datasets are available, so readers can learn network analysis through application and case studies. In the end readers will have the knowledge, skills, and tools to apply social network analysis across different disciplines. A fundamental redesign of the menu structure and the capability to analyze much larger networks required a new edition. This edition presents several new operations including community detection, generalized main paths searches, new network indices, advanced visualization approaches, and instructions for installing Pajek under MacOSX. This third edition is up-to-date with Pajek version 5 and it introduces PajekXXL for very large networks and Pajek3XL for huge networks.

Traditionally seen as a purely people function unconcerned with numbers, HR is now uniquely placed to use company data to drive performance, both of the people in the organization and the organization as a whole. Data-driven HR is a practical guide which enables HR professionals to leverage the value of the vast amount of data available at their fingertips. Covering how to identify the most useful sources of data, collect information in a transparent way that is in line with data protection requirements and turn this data into tangible insights, this book marks a turning point for the HR profession.

Covering all the key elements of HR including recruitment, employee engagement, performance management, wellbeing and training, Data-driven HR examines the ways data can contribute to organizational success by, among other things, optimizing processes, driving performance and improving HR decision making. Packed with case studies and real-life examples, this is essential reading for all HR professionals looking to make a measurable difference in their organizations.

Social Network Analysis: Methods and Examples prepares social science students to conduct their own social network analysis (SNA) by covering basic methodological tools along with illustrative examples from various fields. This innovative book takes a conceptual rather than a mathematical approach as it discusses the connection between what SNA methods have to offer and how those methods are used in research design, data collection, and analysis. Four substantive applications chapters provide examples from politics, work and organizations, mental and physical health, and crime and terrorism studies.

A century of education and education reform, along with more than three decades of high-stakes testing and accountability, reveals a disturbing paradox: education has a steadfast commitment to testing and grading. This commitment persists despite ample research, theory, and philosophy revealing the corrosive consequences of both testing and grading in an education system designed to support human agency and democratic principles. This revised edited volume brings together a collection of updated and new essays that confronts the failure of testing and grading. The book explores the historical failure of testing and grading; the theoretical and philosophical arguments against testing and grading; the negative influence of tests and grades on social justice, race, class, and gender; and the role that they play in perpetuating a deficit perspective of children. The chapters fall under two broad sections. Part I, Degrading Learning, Detesting Education: The Failure of High-Stake Accountability in Education, includes essays on the historical, theoretical, and philosophical arguments against testing and grading. Part II, De-Grading and De-Testing in a Time of High-Stakes Education Reform, presents practical experiments in de-testing and de-grading classrooms for authentic learning experiences.

Humberto Barreto gives professors a simple way to teach fundamental concepts for any undergraduate macroeconomics course using Microsoft Excel (R) with Excel workbooks and add-ins and videos freely available on his university website. The Excel files are designed to be used by students with any textbook, and have been used many times by the author in his own teaching. Each Excel workbook contains links to short screencasts, around five to ten minutes, that show the cursor and typing as the file is manipulated with narration that walks the student through the steps needed to complete a task. The book shows professors a simple way to present macroeconomic models and incorporate data into their courses.

Petty trade helped vast numbers of people to survive the crisis faced by post-Soviet Russia. The book analyses how this survival technique was carried out in practice. On the basis of his fieldwork research, the author shows how people coped with rapid social change and places their activities within a context of government policies, migration flows and entrepreneurial strategies. "This is an original work based on extensive fieldwork research. Wielecki skillfully intertwined "ethnographic meat" with "the bones of theory", which has resulted in a "flesh-and-blood" anthropology." Michal Buchowski "This is an immensely insightful exploration of petty trade in post-Soviet Russia. The author laces his genuine ethnographic work in a coherent account of the concepts of uncertainty, embeddedness, and informal economy." Violetta Zentai

Since the early days of performance assessment, human ratings have been subject to various forms of error and bias. Expert raters often come up with different ratings for the very same performance and it seems that assessment outcomes largely depend upon which raters happen to assign the rating. This book provides an introduction to many-facet Rasch measurement (MFRM), a psychometric approach that establishes a coherent framework for drawing reliable, valid, and fair inferences from rater-mediated assessments, thus answering the problem of fallible human ratings. Revised and updated throughout, the Second Edition includes a stronger focus on the Facets computer program, emphasizing the pivotal role that MFRM plays for validating the interpretations and uses of assessment outcomes.

This book has won the CHOICE Outstanding Academic Title award 2014. A century of education and education reform along with the last three decades of high-stakes testing and accountability reveals a disturbing paradox: Education has a steadfast commitment to testing and grading despite decades of research, theory, and philosophy that reveal the corrosive consequences of both testing and grading within an education system designed to support human agency and democratic principles. This edited volume brings together a collection of essays that confronts the failure of testing and grading and then offers practical and detailed examinations of implementing at the macro and micro levels of education teaching and learning free of the weight of testing and grading. The book explores the historical failure of testing and grading; the theoretical and philosophical arguments against testing and grading; the negative influence of testing and grading on social justice, race, class, and gender; and the role of testing and grading in perpetuating a deficit perspective of children, learning, race, and class. The chapters fall under two broad sections: Part I: "Degrading Learning, Detesting Education: The Failure of High-Stake Accountability in Education" includes essays on the historical, theoretical, and philosophical arguments against testing and grading; Part II: "De-Grading and De-Testing in a Time of High-Stakes Education Reform" presents practical experiments in de-testing and de-grading classrooms for authentic learning experiences.

This book comprises three studies on minority shareholder monitoring in Germany. Mandatory disclosure requirements have increased transparency. An analysis of the information that is publicly available is presented, regardless of the size of the target corporation. The second essay in the form of an event study pays special attention to the German supervisory board and its appointment for a fixed term. Capital markets perceive an activist effort as being more credible under certain circumstances. The study as a whole is empirical evidence for increased minority shareholder activity in Germany. The evidence presented supports the strong shareholder rights perspective. It conflicts with the weak shareholder rights view brought forward in the international literature.

RESEARCH METHODOLOGY explains: How to select topic for doing research; How to review literature; How to collect data; How to analyze the data; How to interpolate between the data; How to estimate error in the calculations; How to calculate various statistical parameters for the results; How to present results; How to write a research paper and a thesis for the award of degree; What is a research journal; What is the difference between an Editor of a research journal and a Referee for research paper; What is an Editorial Board of a research journal; Information about various techniques required for analyzing the data; Information about the hypotheses testing; Statistical tests and application of computers in research.

Survey data are used in many disciplines including Social Sciences, Economics and Psychology. Interviewers' behaviour might affect the quality of such data. This book presents the results of new research on interviewers' motivation and behaviour. A substantial number of contributions address deviant behaviour, methods for assessing the impact of such behaviour on data quality and tools for detecting faked interviews. Further chapters discuss methods for preventing undesirable interviewer effects. Apart from specific methodological contributions, the chapters of the book also provide a unique collection of examples of deviant behaviour and its detection - a topic not overly present in literature despite its substantial prevalence in survey field work. The volume includes 13 peer reviewed papers presented at an international workshop in Rauischholzhausen in October 2011.

The burgeoning field of data analysis is expanding at an incredible pace due to the proliferation of data collection in almost every area of science. The enormous data sets now routinely encountered in the sciences provide an incentive to develop mathematical techniques and computational algorithms that help synthesize, interpret and give meaning to the data in the context of its scientific setting. A specific aim of this book is to integrate standard scientific computing methods with data analysis. By doing so, it brings together, in a self-consistent fashion, the key ideas from: * statistics, * time-frequency analysis, and * low-dimensional reductions The blend of these ideas provides meaningful insight into the data sets one is faced with in every scientific subject today, including those generated from complex dynamical systems. This is a particularly exciting field and much of the final part of the book is driven by intuitive examples from it, showing how the three areas can be used in combination to give critical insight into the fundamental workings of various problems. Data-Driven Modeling and Scientific Computation is a survey of practical numerical solution techniques for ordinary and partial differential equations as well as algorithms for data manipulation and analysis. Emphasis is on the implementation of numerical schemes to practical problems in the engineering, biological and physical sciences. An accessible introductory-to-advanced text, this book fully integrates MATLAB and its versatile and high-level programming functionality, while bringing together computational and data skills for both undergraduate and graduate students in scientific computing.

Identifying factors which stimulate regional growth and international competitiveness and using them for forecasting are the aims of this book. Departing from the theory of comparative advantages and their impact, the author demonstrates that such an approach has to be based on a sound theoretical foundation and on appropriate, advanced econometric methods. He proposes the use of heuristic optimization techniques, Monte Carlo simulation experiments and Lasso-type estimators to avoid bias or misleading findings, which might be the result of applying standard regression methods when key assumptions are not satisfied. In addition, the author demonstrates how some heuristic optimization-based methods can be used to obtain forecasts of industrial production in Russia and Germany founded on past observations and some leading indicators.

This dissertation comprises five studies analyzing daily stock returns of listed firms. Studies one and two shed light on corporate diversification through M&A and how related risk dynamics affect shareholder wealth. Carrying over the risk analysis methodology 'GARCH' to external events in studies three and four, the author individually scrutinizes the adverse implications of bank failures and bailouts in the 2007-2009 financial crisis. Finding opposing return shocks, he identifies the limits of the 'symmetric' GARCH. As observed of the behavior of stock return data, volatility reacts asymmetrically to positive and negative return shocks. The advanced EGARCH incorporates this so called 'leverage effect'. Applying the EGARCH in his final study, the author can simultaneously scrutinize the adverse bank events with an appropriate econometric foundation.

Just Plain Data Analysis teaches students statistical literacy skills that they can use to evaluate and construct arguments about public affairs issues grounded in numerical evidence. The book addresses skills that are often not taught in introductory social science research methods courses and that are often covered sketchily in the research methods textbooks: where to find commonly used measures of political and social conditions; how to assess the reliability and validity of specific indicators; how to present data efficiently in charts and tables; how to avoid common misinterpretations and misrepresentations of data; and how to evaluate causal arguments based on numerical data. With a new chapter on statistical fallacies and updates throughout the text, the new edition teaches students how to find, interpret, and present commonly used social indicators in an even clearer and more practical way.

Written for anyone beginning a research project, this introductory book takes you through the process of analysing your data from start to finish. The author sets out an easy-to-use model for coding data in order to break it down into parts, and then to reassemble it to create a meaningful picture of the phenomenon under study. Full of useful advice, the book guides the reader through the last difficult integrating phase of qualitative analysis including diagramming, memoing, thinking aloud, and using one's feelings, and how to incorporate the use of software where appropriate. Ideal for third year undergraduate students, master students, postgraduates and anybody beginning a research project, the book includes examples covering a wide range of subjects - making the book useful for students across the social science disciplines. Hennie Boeije is currently an Associate Professor with the Department of Methodology and Statistics of the Faculty of Social and Behavioural Sciences at Utrecht University, The Netherlands.