Traditionally, research impact has been measured by counting citations, and citation-based indicators, such as impact factors. But in the last few years there has been increasing pressure on research and higher education institutions to move beyond citation metrics, and look instead at different forms of impact - at real world impact.Scholarly impact expert Elaine Lasda brings together a cast of innovative contributors from a variety of sectors to look at how impact is measured in ways that go beyond citations in peer-reviewed journal articles. With case studies from publishers, museums, scientific centers and government agencies, the contributors show how using a different mix of traditional bibliometrics, newer altmetrics, and other new measures can provide vital information to support the mission and vision of their organizations. For librarians and information professionals, it is becoming increasingly more important to be able to provide expertise on research impact, influence, productivity and prestige. This exciting new book shows readers how to clarify the importance and relevance of organizational research output, and therefore increase their professional value. With the growing sophistication of research impact analysis, the need for "impact metric literacy" is rising, and this book is a helpful tool for those looking to improve their understanding of research impact.

Polls are conducted every day all around the world for almost everything (especially during elections). But not every poll is a good one. A lot depends on the type of questions asked, how they are asked and whether the sample used is truly representative. And these are not the only aspects of a poll that should be checked. So how does one separate the chaff from the wheat? That's where Understanding Public Opinion Polls comes in. Written by a well-known author with over thirty years of experience, the book is built around a checklist for polls that describes the various aspects of polls to pay attention to if one intends to use its results. By comprehensively answering the questions in the checklist, a good idea of the quality of the poll is obtained. Features: Provides readers with a deeper understanding of practical and theoretical aspects of opinion polls while assuming no background in mathematics or statistics Shows how to determine if a poll is good or bad Provides a historical perspective and includes examples from real polls Gives special attention to online and election polls The book gives an overview of many aspects of polls - questionnaire design, sample selection, estimation, margins of error, nonresponse and weighting. It is essential reading for those who want to gain a better understanding of the ins and outs of polling including those who are confronted with polls in their daily life or work or those who need to learn how to conduct their own polls.

The volume presents new developments in data analysis and classification, and gives a state of the art impression of these scientific fields at the turn of the Millennium. Areas that receive considerable attention in this book are Cluster Analysis, Data Mining, Multidimensional and Symbolic Data Analysis, Decision and Regression Trees. The volume contains a refereed selection of original research papers, overview papers, and innovative applications presented at the 7th Conference of the International Federation of Classification Societies (IFCS-2000), with contributions from eminent scientists all over the world. The reader finds introductory material into various areas and kaleidoscopic views of recent technical and methodological developments in widely different areas within data analysis and classification. The presence of a large number of application papers demonstrates the usefulness of the recently developed techniques.

Effectively and ethically leveraging people data to deliver real business value is what sets the best HR leaders and teams apart. Excellence in People Analytics provides business and human resources leaders with everything they need to know about creating value from people analytics. Written by two leading experts in the field, this practical guide outlines how to create sustainable business value with people analytics and develop a data-driven culture in HR. Most importantly, it allows HR professionals and business executives to translate their data into tangible actions to improve business performance. while navigating the rapidly evolving world of work. Full of practical tools and advice assembled around the Insight222 Nine Dimensions in People Analytics (R) model, this book demonstrates how to use people data to increase profits, improve staff retention and workplace productivity as well as develop individual employee experience. Featuring case studies from leading companies including Microsoft, HSBC, Syngenta, Capital One, Novartis, Bosch, Uber, Santander Brasil and American Eagle Outfitters (R), Excellence in People Analytics is essential reading for all HR professionals needing to unlock the potential in their people data and gain competitive advantage

Data has dramatically changed how our world works. Understanding and using data is now one of the most transferable and desirable skills. Whether you're an entrepreneur wanting to boost your business, a jobseeker looking for that employable edge, or simply hoping to make the most of your current career, Confident Data Skills is here to help. This updated second edition takes you through the basics of data: from data mining and preparing and analysing your data, to visualizing and communicating your insights. It now contains exciting new content on neural networks and deep learning. Featuring in-depth international case studies from companies including Amazon, LinkedIn and Mike's Hard Lemonade Co, as well as easy-to understand language and inspiring advice and guidance, Confident Data Skills will help you use your new-found data skills to give your career that cutting-edge boost. About the Confident series... From coding and web design to data, digital content and cyber security, the Confident books are the perfect beginner's resource for enhancing your professional life, whatever your career path.

This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to carry out data analysis; and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case starts with an industry-relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. Learning is then consolidated by 360 practice questions and 120 data exercises. Extensive online resources, including raw and cleaned data and codes for all analysis in Stata, R, and Python, can be found at www.gabors-data-analysis.com.

This book presents and develops the deep data analytics for providing the information needed for successful new product development. Deep Data Analytics for New Product Development has a simple theme: information about what customers need and want must be extracted from data to effectively guide new product decisions regarding concept development, design, pricing, and marketing. The benefits of reading this book are twofold. The first is an understanding of the stages of a new product development process from ideation through launching and tracking, each supported by information about customers. The second benefit is an understanding of the deep data analytics for extracting that information from data. These analytics, drawn from the statistics, econometrics, market research, and machine learning spaces, are developed in detail and illustrated at each stage of the process with simulated data. The stages of new product development and the supporting deep data analytics at each stage are not presented in isolation of each other, but are presented as a synergistic whole. This book is recommended reading for analysts involved in new product development. Readers with an analytical bent or who want to develop analytical expertise would also greatly benefit from reading this book, as well as students in business programs.

This book presents and develops the deep data analytics for providing the information needed for successful new product development. Deep Data Analytics for New Product Development has a simple theme: information about what customers need and want must be extracted from data to effectively guide new product decisions regarding concept development, design, pricing, and marketing. The benefits of reading this book are twofold. The first is an understanding of the stages of a new product development process from ideation through launching and tracking, each supported by information about customers. The second benefit is an understanding of the deep data analytics for extracting that information from data. These analytics, drawn from the statistics, econometrics, market research, and machine learning spaces, are developed in detail and illustrated at each stage of the process with simulated data. The stages of new product development and the supporting deep data analytics at each stage are not presented in isolation of each other, but are presented as a synergistic whole. This book is recommended reading for analysts involved in new product development. Readers with an analytical bent or who want to develop analytical expertise would also greatly benefit from reading this book, as well as students in business programs.

Organizational surveys are widely recognized as a powerful tool for measuring and improving employee commitment. If poorly designed and administered, however, they can create disappointment and cynicism. There are many excellent books on sampling methodology and statistical analysis, but little has been written so far for those responsible for designing and implementing surveys in organizations. Now Allan H Church and Janine Waclawski have drawn on their extensive experience in this field to develop a seven-step model covering the entire process, from initiation to final evaluation. They explain in detail how to devise and administer different types of organizational surveys, leading the reader systematically through the various stages involved. Their text is supported throughout by examples, specimen documentation, work sheets and case studies from a variety of organizational settings. They pay particular attention to the political and human sensitivities concerned and show how to surmount the many potential barriers to a successful outcome. Designing and Using Organizational Surveys is a highly practical guide to one of the most effective methods available for organizational diagnosis and change.

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.

Is college worth the cost? Should I worry about arsenic in my rice? Can we recycle pollution? Real questions of personal finance, public health, and social policy require sober, data-driven analyses. This unique text provides students with the tools of quantitative reasoning to answer such questions. The text models how to clarify the question, recognize and avoid bias, isolate relevant factors, gather data, and construct numerical analyses for interpretation. Themes and techniques are repeated across chapters, with a progression in mathematical sophistication over the course of the book, which helps the student get comfortable with the process of thinking in numbers. This textbook includes references to source materials and suggested further reading, making it user-friendly for motivated undergraduate students. The many detailed problems and worked solutions in the text and extensive appendices help the reader learn mathematical areas such as algebra, functions, graphs, and probability. End-of-chapter problem material provides practice for students, and suggested projects are provided with each chapter. A solutions manual is available online for instructors.

Climate predictions - and the computer models behind them - play a key role in shaping public opinion and our response to the climate crisis. Some people interpret these predictions as 'prophecies of doom' and some others dismiss them as mere speculation, but the vast majority are only vaguely aware of the science behind them. This book gives a balanced view of the strengths and limitations of climate modeling. It covers historical developments, current challenges, and future trends in the field. The accessible discussion of climate modeling only requires a basic knowledge of science. Uncertainties in climate predictions and their implications for assessing climate risk are analyzed, as are the computational challenges faced by future models. The book concludes by highlighting the dangers of climate 'doomism', while also making clear the value of predictive models, and the severe and very real risks posed by anthropogenic climate change.

Make any team or business data driven with this practical guide to overcoming common challenges and creating a data culture. Businesses are increasingly focusing on their data and analytics strategy, but a data-driven culture grounded in evidence-based decision making can be difficult to achieve. Be Data Driven outlines a step-by-step roadmap to building a data-driven organization or team, beginning with deciding on outcomes and a strategy before moving onto investing in technology and upskilling where necessary. This practical guide explains what it means to be a data-driven organization and explores which technologies are advancing data and analytics. Crucially, it also examines the most common challenges to becoming data driven, from a foundational skills gap to issues with leadership and strategy and the impact of organizational culture. With case studies of businesses who have successfully used data, Be Data Driven shows managers, leaders and data professionals how to address hurdles, encourage a data culture and become truly data driven.

New and expanded edition. An International Bestseller - Over One Million Copies Sold! Shortlisted for the Financial Times/Goldman Sachs Business Book of the Year Award. Since Aristotle, we have fought to understand the causes behind everything. But this ideology is fading. In the age of big data, we can crunch an incomprehensible amount of information, providing us with invaluable insights about the what rather than the why. We're just starting to reap the benefits: tracking vital signs to foresee deadly infections, predicting building fires, anticipating the best moment to buy a plane ticket, seeing inflation in real time and monitoring social media in order to identify trends. But there is a dark side to big data. Will it be machines, rather than people, that make the decisions? How do you regulate an algorithm? What will happen to privacy? Will individuals be punished for acts they have yet to commit? In this groundbreaking and fascinating book, two of the world's most-respected data experts reveal the reality of a big data world and outline clear and actionable steps that will equip the reader with the tools needed for this next phase of human evolution.

Data Presentation with SPSS Explained provides students with all the information they need to conduct small scale analysis of research projects using SPSS and present their results appropriately in their reports. Quantitative data can be collected in the form of a questionnaire, survey or experimental study. This book focuses on presenting this data clearly, in the form of tables and graphs, along with creating basic summary statistics. Data Presentation with SPSS Explained uses an example survey that is clearly explained step-by-step throughout the book. This allows readers to follow the procedures, and easily apply each step in the process to their own research and findings. No prior knowledge of statistics or SPSS is assumed, and everything in the book is carefully explained in a helpful and user-friendly way using worked examples. This book is the perfect companion for students from a range of disciplines including psychology, business, communication, education, health, humanities, marketing and nursing - many of whom are unaware that this extremely helpful program is available at their institution for their use.

The general theme of this book is to encourage the use of relevant methodology in data mining which is or could be applied to the interplay of education, statistics and computer science to solve psychometric issues and challenges in the new generation of assessments. In addition to item response data, other data collected in the process of assessment and learning will be utilized to help solve psychometric challenges and facilitate learning and other educational applications. Process data include those collected or available for collection during the process of assessment and instructional phase such as responding sequence data, log files, the use of help features, the content of web searches, etc. Some book chapters present the general exploration of process data in large -scale assessment. Further, other chapters also address how to integrate psychometrics and learning analytics in assessment and survey, how to use data mining techniques for security and cheating detection, how to use more assessment results to facilitate student's learning and guide teacher's instructional efforts. The book includes both theoretical and methodological presentations that might guide the future in this area, as well as illustrations of efforts to implement big data analytics that might be instructive to those in the field of learning and psychometrics. The context of the effort is diverse, including K-12, higher education, financial planning, and survey utilization. It is hoped that readers can learn from different disciplines, especially those who are specialized in assessment, would be critical to expand the ideas of what we can do with data analytics for informing assessment practices.

Social media has put mass communication in the hands of normal people on an unprecedented scale, and has also given social scientists the tools necessary to listen to the voices of everyday people around the world. This book gives social scientists the skills necessary to leverage that opportunity, and transform social media's vast stream of information into social science data. The book combines the big data techniques of computer science with social science methodology. Intended as a text for advanced undergraduates, graduate students, and researchers in the social sciences, this book provides a methodological pathway for scholars who want to make use of this new and evolving source of data. It provides a framework for building one's own data collection and analysis infrastructure, a toolkit of content analysis, geographic analysis, and network analysis, and meditations on the ethical implications of social media data.

Leading tech companies such as Netflix, Amazon and Uber use data science and machine learning at scale in their core business processes, whereas most traditional companies struggle to expand their machine learning projects beyond a small pilot scope. This book enables organizations to truly embrace the benefits of digital transformation by anchoring data and AI products at the core of their business. It provides executives with the essential tools and concepts to establish a data and AI portfolio strategy as well as the organizational setup and agile processes that are required to deliver machine learning products at scale. Key consideration is given to advancing the data architecture and governance, balancing stakeholder needs and breaking organizational silos through new ways of working. Each chapter includes templates, common pitfalls and global case studies covering industries such as insurance, fashion, consumer goods, finance, manufacturing and automotive. Covering a holistic perspective on strategy, technology, product and company culture, Driving Digital Transformation through Data and AI guides the organizational transformation required to get ahead in the age of AI.

Probability, Statistics, and Random Signals offers a comprehensive treatment of probability, giving equal treatment to discrete and continuous probability. The topic of statistics is presented as the application of probability to data analysis, not as a cookbook of statistical recipes. This student-friendly text features accessible descriptions and highly engaging exercises on topics like gambling, the birthday paradox, and financial decision-making.

Chunyan Li is a course instructor with many years of experience in teaching about time series analysis. His book is essential for students and researchers in oceanography and other subjects in the Earth sciences, looking for a complete coverage of the theory and practice of time series data analysis using MATLAB. This textbook covers the topic's core theory in depth, and provides numerous instructional examples, many drawn directly from the author's own teaching experience, using data files, examples, and exercises. The book explores many concepts, including time; distance on Earth; wind, current, and wave data formats; finding a subset of ship-based data along planned or random transects; error propagation; Taylor series expansion for error estimates; the least squares method; base functions and linear independence of base functions; tidal harmonic analysis; Fourier series and the generalized Fourier transform; filtering techniques: sampling theorems: finite sampling effects; wavelet analysis; and EOF analysis.

Corpora are ubiquitous in linguistic research, yet to date, there has been no consensus on how to conceptualize corpus representativeness and collect corpus samples. This pioneering book bridges this gap by introducing a conceptual and methodological framework for corpus design and representativeness. Written by experts in the field, it shows how corpora can be designed and built in a way that is both optimally suited to specific research agendas, and adequately representative of the types of language use in question. It considers questions such as 'what types of texts should be included in the corpus?', and 'how many texts are required?' - highlighting that the degree of representativeness rests on the dual pillars of domain considerations and distribution considerations. The authors introduce, explain, and illustrate all aspects of this corpus representativeness framework in a step-by-step fashion, using examples and activities to help readers develop practical skills in corpus design and evaluation.

Connecting theory with practice, this systematic and rigorous introduction covers the fundamental principles, algorithms and applications of key mathematical models for high-dimensional data analysis. Comprehensive in its approach, it provides unified coverage of many different low-dimensional models and analytical techniques, including sparse and low-rank models, and both convex and non-convex formulations. Readers will learn how to develop efficient and scalable algorithms for solving real-world problems, supported by numerous examples and exercises throughout, and how to use the computational tools learnt in several application contexts. Applications presented include scientific imaging, communication, face recognition, 3D vision, and deep networks for classification. With code available online, this is an ideal textbook for senior and graduate students in computer science, data science, and electrical engineering, as well as for those taking courses on sparsity, low-dimensional structures, and high-dimensional data. Foreword by Emmanuel Candes.

Every country, every subnational government, and every district has a designated population, and this has a bearing on politics in ways most citizens and policymakers are barely aware of. Population and Politics provides a comprehensive evaluation of the political implications stemming from the size of a political unit - on social cohesion, the number of representatives, overall representativeness, particularism ('pork'), citizen engagement and participation, political trust, electoral contestation, leadership succession, professionalism in government, power concentration in the central apparatus of the state, government intervention, civil conflict, and overall political power. A multimethod approach combines field research in small states and islands with cross-country and within-country data analysis. Population and Politics will be of interest to academics, policymakers, and anyone concerned with decentralization and multilevel governance.

This book is for practitioners and academics who have learned the conventions and rules of data modelling and are looking for a deeper understanding of the discipline. The coverage of theory includes a detailed review of the extensive literature on data modelling and logical database design, referencing nearly 500 publications, with a strong focus on their relevance to practice. The practice component incorporates the largest-ever study of data modelling practitioners, involving over 450 participants in interviews, surveys and data modelling tasks. The results challenge many long-standing held assumptions about data modelling and will be of interest to academics and practitioners alike. Graeme Simsion brings to the book the practical perspective and intellectual clarity that have made his "Data Modelling Essentials" a classic in the field. He begins with a question about the nature of data modelling (design or description), and uses it to illuminate such issues as the definition of data modelling, its philosophical underpinnings, inputs and deliverables, the necessary behaviours and skills, the role of creativity, product diversity, quality measures, personal styles, and the differences between experts and novices. "Data Modeling: Theory and Practice" is essential reading for anyone involved in data modelling practice, research, or teaching.