Providing an authoritative assessment of the current landscape of spatial analysis in the social sciences, this cutting-edge Handbook covers the full range of standard and emerging methods across the social science domain areas in which these methods are typically applied. Accessible and comprehensive, it expertly answers the key questions regarding the dynamic intersection of spatial analysis and the social sciences. The chapters are split into insightful sections dedicated to foundational background material, methods, social science applications and the challenges on the horizon, using state-of-the-art coverage of the traditional and novel spatial methods. Leading scholars in the field use a range of applications to illustrate the diverse ways in which spatial analysis methods can inform research in the field of social sciences. Furthermore, the Handbook discusses the key challenges to that research including uncertainty, reproducibility and replicability. This Handbook of Spatial Analysis in the Social Sciences will be an excellent informative resource for scholars in the fields of geography, social sciences and public health. Established and early career researchers of the social sciences alike will appreciate the detailed overview of the methods and applications as well as the ability to expand their methodological knowledge.

The Elgar Encyclopedia of Law and Data Science represents a comprehensive mapping of the field. Comprising over 60 entries, it features contributions from eminent global scholars, drawing on expertise from multiple disciplines, including law and data science, economics, computer engineering, physics, biomedical engineering and history, philosophy, neuro-engineering, political science, and geo-informatics. This Encyclopedia brings together jurists, computer scientists, and data analysts to uncover the challenges, opportunities, and fault lines that arise as these groups are increasingly thrown together by expanding attempts to regulate and adapt to a data-driven world. It explains the concepts and tools at the crossroads of the many disciplines involved in data science and law, bridging scientific and applied domains. Entries span algorithmic fairness, consent, data protection, ethics, healthcare, machine learning, patents, surveillance, transparency and vulnerability. Comprehensive yet accessible, this Encyclopedia will be an indispensable resource for scholars of law, data science, artificial intelligence and law and technology. It also contains practical implications for a manifold of users: from domain experts to policy makers, from businesses to practitioners. Key Features: The first Encyclopedic coverage of the field of Law and Data Science Over 60 entries Entries organized alphabetically for ease of reference Full analytical index Interrelated multidisciplinary perspectives Unique accessibility for non-experts.

Taking the Fear Out of Data Analysis provides readers with the necessary knowledge and skills to understand, perform, and interpret quantitative data analysis effectively. Acknowledging that people often dislike statistics and quantitative methods, this book illustrates that statistical reasoning can be a fun and intuitive part of our lives. Key Features: Split into three sections covering how to understand data, preparing data for analysis and carrying out the analysis Blends theory with practical examples in a logical and straightforward manner to guide readers in making sense of statistical inference Offers universal knowledge that can be applied to a variety of software applications with limited technical complexity to aid the learning process Short and concise chapters focusing on the essence of the topics covered, such as analytical techniques that are typically used in behavioral and social science research Significantly revised and updated, this textbook is an essential text for both undergraduate and postgraduate students in fields such as information systems, international business and marketing. It will also be beneficial for practitioners involved in data science, data analytics, and market research.

Drawing together international experts on research methods in International Relations (IR), this Handbook answers the complex practical questions for those approaching a new research topic for the first time. Innovative in its approach, it considers the art of IR research as well as the science, offering diverse perspectives on current research methods and emerging developments in the field. Empirical chapters are split into five distinct parts guiding the reader through the research process, covering the key topics including scope and methods, concepts, data and techniques and tools and applications. Highlighting the wide-ranging differences in the topic, the illustrative case studies and research models also provide guidance on how and when to use these tools, including how to evaluate research at the start and end of projects. Furthermore, it examines how to publish research and provides advice on how to manage a research team. This informative read will provide an excellent resource for established researchers taking on new projects, rethinking their approach to IR or those interested in learning new methods. Students and scholars of international politics and public policy as well as social scientists will also find this illuminating and instructive.

Taking the Fear Out of Data Analysis provides readers with the necessary knowledge and skills to understand, perform, and interpret quantitative data analysis effectively. Acknowledging that people often dislike statistics and quantitative methods, this book illustrates that statistical reasoning can be a fun and intuitive part of our lives. Key Features: Split into three sections covering how to understand data, preparing data for analysis and carrying out the analysis Blends theory with practical examples in a logical and straightforward manner to guide readers in making sense of statistical inference Offers universal knowledge that can be applied to a variety of software applications with limited technical complexity to aid the learning process Short and concise chapters focusing on the essence of the topics covered, such as analytical techniques that are typically used in behavioral and social science research Significantly revised and updated, this textbook is an essential text for both undergraduate and postgraduate students in fields such as information systems, international business and marketing. It will also be beneficial for practitioners involved in data science, data analytics, and market research.

This practical book explores collaborative inquiry as an approach to research and change in organizations where internal members and external researchers work together as partners to address organizational issues and create knowledge about changing organizations. Taking a research-based approach, Abraham B. (Rami) Shani and David Coghlan analyze the challenges that participants face in building a partnership between researchers and practitioners throughout the phases of collaboration. Chapters explore how collaborative partners assess the organization's current and future capabilities by expressing the present and future in creative imagery and by making relevant changes in the organization to create that future. The book examines the theoretical foundations behind collaborative inquiry in addition to the methodologies of this approach to organization development and change. Mapping both the theory and practice of collaborative inquiry, this book will be a valuable resource for scholars and students of organization studies and research methods, particularly those with a focus on business and management. It will also be beneficial for practitioners interested in collaborative and action research modes.

This insightful book examines all aspects of the design process and implementation of questionnaire surveys on the activities of business, public sector, and non-profit organizations. Anthony Arundel discusses how different aspects of the survey method and planned statistical analysis can constrain question design, and how these issues can be effectively resolved. Throughout this engaging yet practical book, Arundel promotes good practices for questionnaire design, sample construction, and survey delivery systems including online, postal, and verbal methods, with a focus on obtaining high-quality data in line with ethics and confidentiality requirements. Chapters include constructive advice on questionnaire design and testing, survey implementation, and data processing, analysis, and reporting, with examples of time and financial cost budgets. Considering the recent developments in survey methods, the book explores how to use web probing as a substitute for cognitive testing and examines the use of tablets and smartphones in answering questionnaires. Combining theoretical and practical insights into survey design, implementation, and data processing and analysis, this book will be essential reading for business and management scholars and students, with a particular interest in research methods and organization studies. It will also be useful for practitioners and business managers seeking to understand how to create and use surveys.

This practical book explores collaborative inquiry as an approach to research and change in organizations where internal members and external researchers work together as partners to address organizational issues and create knowledge about changing organizations. Taking a research-based approach, Abraham B. (Rami) Shani and David Coghlan analyze the challenges that participants face in building a partnership between researchers and practitioners throughout the phases of collaboration. Chapters explore how collaborative partners assess the organization's current and future capabilities by expressing the present and future in creative imagery and by making relevant changes in the organization to create that future. The book examines the theoretical foundations behind collaborative inquiry in addition to the methodologies of this approach to organization development and change. Mapping both the theory and practice of collaborative inquiry, this book will be a valuable resource for scholars and students of organization studies and research methods, particularly those with a focus on business and management. It will also be beneficial for practitioners interested in collaborative and action research modes.

Data is humanity's most important new resource. It has the capacity to provide insight into every aspect of our lives, the planet and the universe at large; it changes not only what we know but also how we know it. Exploiting the value of data could improve our existence as much as - if not more than - previous technological revolutions. Yet data without empathy is useless. There is a tendency in data science to forget about the human needs and feelings of the people who make up the data, the people who work with the data, and those expected to understand the results. Without empathy, this precious resource is at best underused, at worst misused. Data: A Guide to Humans will help you understand how to properly exploit data, why this is so important, and how companies and governments are currently using data. It makes a compelling case for empathy as the crucial factor in elevating our understanding of data to something which can make a lasting and essential contribution to your business, your life and maybe even the world.

In this fascinating follow-up to the bestselling Information is Beautiful and Knowledge is Beautiful, the king of infographics David McCandless uses spectacular visuals to give us all a bit of good news. We are living in the Information Age, in which we are constantly bombarded with data - on television, in print and online. How can we relate to this mind-numbing overload? Enter David McCandless and his amazing infographics: simple, elegant ways to understand information too complex or abstract to grasp any way but visually. In his unique signature style, he creates dazzling displays that blend facts with their connections, contexts and relationships, making information meaningful, entertaining - and beautiful. In his highly anticipated third book, McCandless illustrates positive news from around the world, for an informative, engaging and uplifting collection of new infographic art.

One of the most challenging tasks in the research design process is choosing the most appropriate data collection and analysis technique. This Handbook provides a detailed introduction to five qualitative data collection and analysis techniques pertinent to exploring entrepreneurial phenomena. Techniques for collecting and analyzing data are rarely addressed in detail in published articles. In addition, the constant development of new tools and refinement of existing ones has meant that researchers often face a confusing range from which to choose. The experienced and expert group of contributors to this book provide detailed, practical accounts of how to conduct research employing focus groups, critical incident technique, repertory grids, metaphors, the constant comparative method and grounded theory. This Handbook will become the starting point for any research project. Scholars new to entrepreneurship and doctoral students as well as established academics keen to extend their research scope will find this book an invaluable and timely resource. Contributors: A.R. Anderson, C. Bjursell, A. Bollingtoft, E. Chell, E. Diaz de Leon, C. Dima, S. Drakopoulou Dodd, P. Guild, A. Hagedorn, R.T. Harrison, F.M. Hill, S.L. Jack, R.G. Klapper, A. de Koning, C.M. Leitch, E. McKeever, S. Moult, H. Neergaard, R. Newby, R. Smith, S.M. Smith, G. Soutar, J. Watson

THE NEW BOOK FROM THE BESTSELLING AUTHOR OF EVERYBODY LIES 'Don't Trust Your Gut is a tour de force - an intoxicating blend of analysis, humor, and humanity' DANIEL H. PINK 'Seth Stephens-Davidowitz is an expert on data-driven thinking, and this engaging book is full of surprising, useful insights for using the information at your fingertips to make better decisions' ADAM GRANT Big decisions are hard. We might consult friends and family, read advice online or turn to self-help books for guidance, but in the end we usually just do what feels right. But what if our gut is wrong? As economist and former Google data scientist Seth Stephens-Davidowitz argues, our gut is actually not that reliable - and data can prove this. In Don't Trust Your Gut, he unearths the startling conclusions that the right data can teach us about who we are and what will make our lives better. Over the past decade, scholars have mined enormous datasets to find remarkable new approaches to life's biggest self-help puzzles, from the boring careers that produce the most wealth, to old-school, data-backed relationship advice. While we often think we know how to better ourselves, the numbers, it turns out, disagree. Telling fascinating stories through the latest big data research, Stephens-Davidowitz reveals just how wrong we really are when it comes to improving our lives, and offers a new way of tackling our most consequential choices.

Qualitative Comparative Analysis (QCA) is an emerging research method that is highly suitable for evaluation studies. Clear and concise, this book explains how researchers and evaluators can use QCA effectively for the systematic and thorough analysis of large infrastructure projects, while also acknowledging their complexity. Lasse Gerrits and Stefan Verweij present the key steps of this methodology to identify patterns across real-life cases. From collecting and interpreting data to sharing their knowledge and presenting the results, the authors use examples of megaprojects to emphasize how QCA can be used successfully for both single infrastructure ventures as well as more extensive projects. In addition to discussing the best practices and pitfalls of the methodology, further examples from current research are given in order to illustrate how QCA works effectively in both theory and practice. Being written with researchers and evaluators in mind, this book will be of great benefit for students and scholars of evaluation studies, public administration, transport studies, policy analysis and project management. The book is also highly applicable for those working in public or private organizations involved in infrastructure projects looking for an effective, detailed and systematic method of evaluation.

This book asks the important question; Can the by-products of research activity be treated as data and of research interest in themselves? This groundbreaking interdisciplinary volume considers the analytic value of a range of 'by-products' of social research and reading. These include electronically captured paradata on survey administration, notes written in the margins of research documents and literary texts, and fieldnotes and ephemera produced by social researchers. Revealing the relational nature of paradata, marginalia and fieldnotes, contributions examine how the craft of studying and analyzing these by-products offers insight into the intellectual, social and ethical processes underpinning the activities of research and reading. Unique and engaging, this book is a must read for social researchers and sociologists, narrative analysts, literary scholars and historians. Bridging methodological boundaries, it will also prove of great value to quantitative and qualitative methodologists alike. Contributors include: K. Bell, J. Boddy, R.G. Burgess, G.B. Durrant, R. Edwards, H. Elliott, E. Fahmy, J. Goodwin, H.J. Jackson, D. Kilburn, O. Maslovskaya, H. O'Connor, A. Phoenix, W.H. Sherman

Jump-start your career as a data scientist--learn to develop datasets for exploration, analysis, and machine learning SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis is a resource that's dedicated to the Structured Query Language (SQL) and dataset design skills that data scientists use most. Aspiring data scientists will learn how to how to construct datasets for exploration, analysis, and machine learning. You can also discover how to approach query design and develop SQL code to extract data insights while avoiding common pitfalls. You may be one of many people who are entering the field of Data Science from a range of professions and educational backgrounds, such as business analytics, social science, physics, economics, and computer science. Like many of them, you may have conducted analyses using spreadsheets as data sources, but never retrieved and engineered datasets from a relational database using SQL, which is a programming language designed for managing databases and extracting data. This guide for data scientists differs from other instructional guides on the subject. It doesn't cover SQL broadly. Instead, you'll learn the subset of SQL skills that data analysts and data scientists use frequently. You'll also gain practical advice and direction on "how to think about constructing your dataset." Gain an understanding of relational database structure, query design, and SQL syntax Develop queries to construct datasets for use in applications like interactive reports and machine learning algorithms Review strategies and approaches so you can design analytical datasets Practice your techniques with the provided database and SQL code In this book, author Renee Teate shares knowledge gained during a 15-year career working with data, in roles ranging from database developer to data analyst to data scientist. She guides you through SQL code and dataset design concepts from an industry practitioner's perspective, moving your data scientist career forward!

The generation and use of data in society has seen exponential growth in recent years. The emergent field of data science, concerned with understanding and analyzing this data, can be applied to applications spanning from healthcare and urban planning to smart household devices. The legal questions which accompany the rise of these technologies, however, remains underexplored. Breaking new ground this Research Handbook maps the legal implications of the emergence of data science. Drawing on comparative perspectives, this Research Handbook approaches the subject from different legal domains, considering the possibilities and limitations of the current legal framework. Reflecting on whether further regulation is needed to address the ethical and legal problems raised by data science, the contributors examine how the practice is, and should be, regulated and how it influences the law, judiciary, and legal research. The book makes a vital contribution to the emerging field of data science and law as a discipline, and covers data science methodologies and tools essential for both legal practice and scholarship. The Research Handbook in Data Science and Law will be an important resource for students interested in data and technology law, as well as for legal scholars and practitioners in the field. Data scientists seeking an introduction to the law surrounding the field will also find this Research Handbook invaluable. Contributors include: A. Berlee, C. Busch, A. Carlson, M.O. Cuevas, B. Custers, A. Daly, A. De Franceschi, W. Kaufmann, A. Klop, S. Kreifels, K.M. Kryla-Cudna, A.J.F. Lafarre, V. Mak, M. Mattioli, R. Nurullaev, R. Podszun, M.G. Porcedda, C. Prins, S. Ranchordas, R. Russo, K.K.E.C.T. Swinnen, P. Szulewski, E.T.T. Tai, H. U

Don't simply show your data tell a story with it! Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples ready for immediate application to your next graph or presentation. Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to: * Understand the importance of context and audience * Determine the appropriate type of graph for your situation * Recognize and eliminate the clutter clouding your information * Direct your audience's attention to the most important parts of your data * Think like a designer and utilize concepts of design in data visualization * Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data Storytelling with Data will give you the skills and power to tell it!

As a result of the COVID-19 pandemic, medical statistics and public health data have become staples of newsfeeds worldwide, with infection rates, deaths, case fatality and the mysterious R figure featuring regularly. However, we don't all have the statistical background needed to translate this information into knowledge. In this lively account, Stephen Senn explains these statistical phenomena and demonstrates how statistics is essential to making rational decisions about medical care. The second edition has been thoroughly updated to cover developments of the last two decades and includes a new chapter on medical statistical challenges of COVID-19, along with additional material on infectious disease modelling and representation of women in clinical trials. Senn entertains with anecdotes, puzzles and paradoxes, while tackling big themes including: clinical trials and the development of medicines, life tables, vaccines and their risks or lack of them, smoking and lung cancer, and even the power of prayer.

As the analysis of big datasets in sports performance becomes a more entrenched part of the sporting landscape, so the value of sport scientists and analysts with formal training in data analytics grows. Sports Analytics: Analysis, Visualisation and Decision Making in Sports Performance provides the most authoritative and comprehensive guide to the use of analytics in sport and its application in sports performance, coaching, talent identification and sports medicine available. Employing an approach-based structure and integrating problem-based learning throughout the text, the book clearly defines the difference between analytics and analysis and goes on to explain and illustrate methods including: Interactive visualisation Simulation and modelling Geospatial data analysis Spatiotemporal analysis Machine learning Genomic data analysis Social network analysis Offering a mixed-methods case study chapter, no other book offers the same level of scientific grounding or practical application in sports data analytics. Sports Analytics is essential reading for all students of sports analytics, and useful supplementary reading for students and professionals in talent identification and development, sports performance analysis, sports medicine and applied computer science.

The global data market is estimated to be worth $64 billion dollars, making it a more valuable resource than oil. But data is useless without the analysis, interpretation and innovations of data scientists. With Confident Data Science, learn the essential skills and build your confidence in this sector through key insights and practical tools for success. In this book, you will discover all of the skills you need to understand this discipline, from primers on the key analytic and visualization tools to tips for pitching to and working with clients. Adam Ross Nelson draws upon his expertise as a data science consultant and, as someone who made moved into the industry late in his career, to provide an overview of data science, including its key concepts, its history and the knowledge required to become a successful data scientist. Whether you are considering a career in this industry or simply looking to expand your knowledge, Confident Data Science is the essential guide to the world of data science. About the Confident series... From coding and data science to cloud and cyber security, the Confident books are perfect for building your technical knowledge and enhancing your professional career.

This textbook presents an innovative new perspective on the economics of development, including insights from a broad range of disciplines. It starts with the current state of affairs, a discussion of data availability, reliability, and analysis, and an historic overview of the deep influence of fundamental factors on human prosperity. Next, it focuses on the role of human interaction in terms of trade, capital, and knowledge flows, as well as the associated implications for institutions, contracts, and finance. The book also highlights differences in the development paths of emerging countries in order to provide a better understanding of the concepts of development and the Millennium Development Goals. Insights from other disciplines are used help to understand human development with regard to other issues, such as inequalities, health, demography, education, and poverty. The book concludes by emphasizing the importance of connections, location, and human interaction in determining future prosperity.

The SPSS Survival Manual throws a lifeline to students and researchers grappling with this powerful data analysis software. In her bestselling guide, Julie Pallant takes you through the entire research process, helping you choose the right data analysis technique for your project. This edition has been updated to include up to SPSS version 26. From the formulation of research questions, to the design of the study and analysis of data, to reporting the results, Julie discusses basic and advanced statistical techniques. She outlines each technique clearly, with step-by-step procedures for performing the analysis, a detailed guide to interpreting data output and an example of how to present the results in a report. For both beginners and experienced users in Psychology, Sociology, Health Sciences, Medicine, Education, Business and related disciplines, the SPSS Survival Manual is an essential text. It is illustrated throughout with screen grabs, examples of output and tips, and is also further supported by a website with sample data and guidelines on report writing. This seventh edition is fully revised and updated to accommodate changes to IBM SPSS procedures.

Provides a thorough coverage and comparison of a wide array of time series models and methods: Exponential Smoothing, Holt Winters, ARMA and ARIMA, deep learning models including RNNs, LSTMs, GRUs, and ensemble models composed of combinations of these models. Introduces the factor table representation of ARMA and ARIMA models. This representation is not available in any other book at this level and is extremely useful in both practice and pedagogy. Uses real world examples that can be readily found via web links from sources such as the US Bureau of Statistics, Department of Transportation and the World Bank. There is an accompanying R package that is easy to use and requires little or no previous R experience. The package implements the wide variety of models and methods presented in the book and has tremendous pedagogical use.

Data literacy is one of the key skills that companies are looking for but it's a specialist skill - currently. This book is your comprehensive guide to becoming data literate: understand data analytics, how to use data insights effectively in your organisation, and how to talk about data with experts and non-experts confidently.

Value-Driven Data explains how data and business leaders can co-create and deploy data-driven solutions for their organizations. Value-Driven Data explores how organizations can understand their problems and come up with better solutions, aligning data storytelling with business needs. The book reviews the main challenges that plague most data-to-business interactions and offers actionable strategies for effective data value implementation, including methods for tackling obstacles and incentivizing change. Value-Driven Data is supported by tried-and-tested frameworks that can be applied to different contexts and organizations. It features cutting-edge examples relating to digital transformation, data strategy, resolving conflicts of interests, building a data P&L and AI value prediction methodology. Recognizing different types of data value, this book presents tangible methodologies for identifying, capturing, communicating, measuring and deploying data-enabled opportunities. This is essential reading for data specialists, business stakeholders and leaders involved in capturing and executing data value opportunities for organizations and for informing data value strategies.