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.

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.

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.

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.

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.

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!

Research in the domains of learning analytics and educational data mining has prototyped an approach where methodologies from data science and machine learning are used to gain insights into the learning process by using large amounts of data. As many training and academic institutions are maturing in their data-driven decision making, useful, scalable, and interesting trends are emerging. Organizations can benefit from sharing information on those efforts. Applying Data Science and Learning Analytics Throughout a Learner's Lifespan examines novel and emerging applications of data science and sister disciplines for gaining insights from data to inform interventions into learners' journeys and interactions with academic institutions. Data is collected at various times and places throughout a learner's lifecycle, and the learners and the institution should benefit from the insights and knowledge gained from this data. Covering topics such as learning analytics dashboards, text network analysis, and employment recruitment, this book is an indispensable resource for educators, computer scientists, faculty of higher education, government officials, educational administration, students of higher education, pre-service teachers, business professionals, researchers, and academicians.

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

The ""Handbook of Research on Electronic Surveys and Measurements"" is the comprehensive reference source for innovative knowledge on electronic surveys. This commanding handbook of research provides complete coverage of the challenges associated with the use of the Internet to develop online surveys, administer Web-based instruments, and conduct computer-mediated assessments. Many internationally renowned experts in the field of electronic surveys and measurements have contributed to this comprehensive publication, and each chapter contains multiple references to published works in the field. The ""Handbook of Research on Electronic Surveys and Measurements"" is the only work with cutting edge descriptions of the design, implementation, and use of electronic surveys, and also includes discussions on the challenges associated with online data collection and profiles of selected online measures. This combination of how-to information about online research coupled with profiles of specific measures makes it an indispensable reference for every library.

Patterns can be any number of items that occur repeatedly, whether in the behaviour of animals, humans, traffic, or even in the appearance of a design. As technologies continue to advance, recognizing, mimicking, and responding to all types of patterns becomes more precise. Pattern Recognition and Classification in Time Series Data focuses on intelligent methods and techniques for recognizing and storing dynamic patterns. Emphasizing topics related to artificial intelligence, pattern management, and algorithm development, in addition to practical examples and applications, this publication is an essential reference source for graduate students, researchers, and professionals in a variety of computer-related disciplines.

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"Auerbach and Silverstein write at a level that can be understood by beginners but is sophisticated enough for scholars...informative and interesting."
--"Choice"

Qualitative Data is meant for the novice researcher who needs guidance on what specifically to do when faced with a sea of information. It takes readers through the qualitative research process, beginning with an examination of the basic philosophy of qualitative research, and ending with planning and carrying out a qualitative research study. It provides an explicit, step-by-step procedure that will take the researcher from the raw text of interview data through data analysis and theory construction to the creation of a publishable work.

The volume provides actual examples based on the authors' own work, including two published pieces in the appendix, so that readers can follow examples for each step of the process, from the project's inception to its finished product. The volume also includes an appendix explaining how to implement these data analysis procedures using NVIVO, a qualitative data analysis program.

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 invited authors of this edited volume have been prolific in the arena of Real Data Analysis (RDA) as it applies to the social and behavioral sciences, especially in the disciplines of education and psychology. Combined, this brain trust represents 3,247 articles in refereed journals, 127 books published, US $45.3 Million in extramural research funding, 34 teaching and 92 research awards, serve(d) as Editor/Assistant Editor/Editorial Board Member for 95 peer reviewed journals, and provide( d) ad hoc reviews for 362 journals. Their enormous footprint on real data analysis is showcased for professors, researchers, educators, administrators, and graduate students in the second text in the AERA/SIG ES Quantitative Methods series.

Business and medical professionals rely on large data sets to identify trends or other knowledge that can be gleaned from the collection of it. New technologies concentrate on data's management, but do not facilitate users' extraction of meaningful outcomes. Pattern and Data Analysis in Healthcare Settings investigates the approaches to shift computing from analysis on-demand to knowledge on-demand. By providing innovative tactics to apply data and pattern analysis, these practices are optimized into pragmatic sources of knowledge for healthcare professionals. This publication is an exhaustive source for policy makers, developers, business professionals, healthcare providers, and graduate students concerned with data retrieval and analysis.

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.

A guide for how to tell clear, data-driven stories that will make an impact. People with important evidence-based ideas often struggle to translate data into stories their readers can relate to and understand. And if leaders can't communicate well to their audience, they will not be able to make important changes in the world. Why do some evidence-based ideas thrive while others die? And how do we improve the chances of worthy ideas? In Because Data Can't Speak for Itself, accomplished educators and writers David Chrisinger and Lauren Brodsky tackle these questions head-on. They reveal the parts and functions of effective data-driven stories and explain myriad ways to turn your data dump into a narrative that can inform, persuade, and inspire action. Chrisinger and Brodsky show that convincing data-driven stories draw their power from the same three traits, which they call people, purpose, and persistence. Writers need to find the real people behind the numbers and share their stories. At the same time, they need to remember their own purpose and be honest about what data says--and, just as importantly, what it does not. Compelling and concise, this fast-paced tour of success stories--and several failures--includes examples on topics such as COVID-19, public diplomacy, and criminal justice. Chrisinger and Brodsky's easy-to-apply tool kit will turn anyone into an effective and persuasive evidence-based writer. Aimed at policy analysts, politicians, journalists, teachers, and business leaders, Because Data Can't Speak for Itself will transform the way you communicate ideas.

Recently, there has been a rapid increase in interest regarding social network analysis in the data mining community. Cognitive radios are expected to play a major role in meeting this exploding traffic demand on social networks due to their ability to sense the environment, analyze outdoor parameters, and then make decisions for dynamic time, frequency, space, resource allocation, and management to improve the utilization of mining the social data. Cognitive Social Mining Applications in Data Analytics and Forensics is an essential reference source that reviews cognitive radio concepts and examines their applications to social mining using a machine learning approach so that an adaptive and intelligent mining is achieved. Featuring research on topics such as data mining, real-time ubiquitous social mining services, and cognitive computing, this book is ideally designed for social network analysts, researchers, academicians, and industry professionals.

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.