Data science and analysis Books
SAGE Publications Inc Confirmatory Factor Analysis
Book SynopsisMeasurement connects theoretical concepts to what is observable in the empirical world, and is fundamental to all social and behavioral research. In this volume, J. Micah Roos and Shawn Bauldry introduce a popular approach to measurement: Confirmatory Factor Analysis (CFA). As the authors explain, CFA is a theoretically informed statistical framework for linking multiple observed variables to latent variables that are not directly measurable. The authors begin by defining terms, introducing notation, and illustrating a wide variety of measurement models with different relationships between latent and observed variables. They proceed to a thorough treatment of model estimation, followed by a discussion of model fit. Most of the volume focuses on measures that approximate continuous variables, but the authors also devote a chapter to categorical indicators. Each chapter develops a different example (sometimes two) covering topics as diverse as racist attitudes, theological conservatism, leadership qualities, psychological distress, self-efficacy, beliefs about democracy, and Christian nationalism drawn mainly from national surveys. Data to replicate the examples are available on a companion website, along with code for R, Stata, and Mplus.Trade ReviewConfirmatory Factor Analysis is well written and easy to read, The book covers the essentials necessary for understanding and using CFA. It is appropriate for graduate students and professors new to this analysis approach. -- Jerry J. VaskeThe authors provide a masterful and fluid overview of confirmatory factor analysis that will guide readers to the best practices whether conducting their own research or evaluating the research of others. -- John HoffmannThis is a well-written and comprehensive text. -- Michael D. BidermanRoos and Bauldry lucidly set out foundations of confirmatory factor analysis (CFA) as applied in the assessment and construction of scales. Beginning with model specification, they discuss identification, estimation, and assessment of CFA models, before developing extensions to assessing measurement invariance and categorical (rather than quantitative) indicators. -- Peter V. MarsdenTable of ContentsChapter 1: Introduction Chapter 2: Model Specification Chapter 3: Identification and Estimation Chapter 4: Model Evaluation and Respecification Chapter 5: Measurement Invariance Chapter 6: Categorical Indicators Chapter 7: Conclusion Appendix: Reliability of Scales Glossary Bibliography
£30.99
SAGE Publications Inc A Primer on Partial Least Squares Structural
Book SynopsisThe third edition of A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) guides readers through learning and mastering the techniques of this approach in clear language. Authors Joseph H. Hair, Jr., G. Tomas M. Hult, Christian Ringle, and Marko Sarstedt use their years of conducting and teaching research to communicate the fundamentals of PLS-SEM in straightforward language to explain the details of this method, with limited emphasis on equations and symbols. A running case study on corporate reputation follows the different steps in this technique so readers can better understand the research applications. Learning objectives, review and critical thinking questions, and key terms help readers cement their knowledge. This edition has been thoroughly updated, featuring the latest version of the popular software package SmartPLS 3. New topics have been added throughout the text, including a thoroughly revised and extended chapter on mediation, recent research on the foundations of PLS-SEM, detailed descriptions of research summarizing the advantages as well as limitations of PLS-SEM, and extended coverage of advanced concepts and methods, such as out-of-sample versus in-sample prediction metrics, higher-order constructs, multigroup analysis, necessary condition analysis, and endogeneity. Table of ContentsPreface About the Authors Chapter 1. An Introduction to Structural Equation Modeling Chapter Preview What Is Structural Equation Modeling? Considerations in Using Structural Equation Modeling Principles of Structural Equation Modeling PLS-SEM, CB-SEM, and Regressions Based on Sum Scores Considerations When Applying PLS-SEM Guidelines for Choosing Between PLS-SEM and CB-SEM Organization of Remaining Chapters Summary Review Questions Critical Thinking Questions Key Terms Suggested Readings Chapter 2. Specifying the Path Model and Examining Data Chapter Preview Stage 1: Specifying the Structural Model Stage 2: Specifying the Measurement Models Stage 3: Data Collection and Examination Case Study Illustration—Specifying the PLS-SEM Model Summary Review Questions Critical Thinking Questions Key Terms Suggested Readings Chapter 3. Path Model Estimation Chapter Preview Stage 4: Model Estimation and the PLS-SEM Algorithm Case Study Illustration—PLS Path Model Estimation (Stage 4) Summary Review Questions Critical Thinking Questions Key Terms Suggested Readings Chapter 4. Assessing PLS-SEM Results—Part I: Evaluation of the Reflective Measurement Models Chapter Preview Overview of Stage 5: Evaluation of Measurement Models Stage 5a: Assessing Results of Reflective Measurement Models Case Study Illustration—Evaluation of the Reflective Measurement Models (Stage 5a) Summary Review Questions Critical Thinking Questions Key Terms Suggested Readings Chapter 5. Assessing PLS-SEM Results—Part II: Evaluation of the Formative Measurement Models Chapter Preview Stage 5b: Assessing Results of Formative Measurement Models Case Study Illustration—Evaluation of the Formative Measurement Models (Stage 5b) Summary Review Questions Critical Thinking Questions Key Terms Suggested Readings Chapter 6. Assessing PLS-SEM Results—Part III: Evaluation of the Structural Model Chapter Preview Stage 6: Structural Model Results Evaluation Case Study Illustration—Evaluation of the Structural Model (Stage 6) Summary Review Questions Critical Thinking Questions Key Terms Suggested Readings Chapter 7. Mediator and Moderator Analysis Chapter Preview Mediation Moderation Case Study Illustration—Moderation Summary Review Questions Critical Thinking Questions Key Terms Suggested Readings Chapter 8. Outlook on Advanced Methods Chapter Preview Importance-Performance Map Analysis Necessary Condition Analysis Higher-Order Constructs Confirmatory Tetrad Analysis Examining Endogeneity Treating Observed and Unobserved Heterogeneity Measurement Model Invariance Consistent PLS-SEM Summary Review Questions Critical Thinking Questions Key Terms Suggested Readings Glossary References Index
£50.00
Medical Group Management Association/Center for Research in Ambulatory Health Care Administration Data Sanity: A Quantum Leap to Unprecedented Results
£92.70
ESRI Press Spatial Data Science
Book SynopsisSpatial Data Science will show GIS scientists and practitioners how to add and use new analytical methods from data science in their existing GIS platforms. By explaining how the spatial domain can provide many of the building blocks, it''s critical for transforming data into information, knowledge, and solutions. This book is for those using or studying GIS and the computer scientists, engineers, statisticians, and information and library scientists leading the development and deployment of data science.
£54.14
Manning Publications Spark in Action, Second Edition
Book SynopsisThe Spark distributed data processing platform provides an easy-to-implement tool for ingesting, streaming, and processing data from any source. In Spark in Action, Second Edition, you’ll learn to take advantage of Spark’s core features and incredible processing speed, with applications including real-time computation, delayed evaluation, and machine learning. Unlike many Spark books written for data scientists, Spark in Action, Second Edition is designed for data engineers and software engineers who want to master data processing using Spark without having to learn a complex new ecosystem of languages and tools. You’ll instead learn to apply your existing Java and SQL skills to take on practical, real-world challenges. Key Features · Lots of examples based in the Spark Java APIs using real-life dataset and scenarios · Examples based on Spark v2.3 Ingestion through files, databases, and streaming · Building custom ingestion process · Querying distributed datasets with Spark SQL For beginning to intermediate developers and data engineers comfortable programming in Java. No experience with functional programming, Scala, Spark, Hadoop, or big data is required. About the technology Spark is a powerful general-purpose analytics engine that can handle massive amounts of data distributed across clusters with thousands of servers. Optimized to run in memory, this impressive framework can process data up to 100x faster than most Hadoop-based systems. Author BioAn experienced consultant and entrepreneur passionate about all things data, Jean-Georges Perrin was the first IBM Champion in France, an honor he’s now held for ten consecutive years. Jean-Georges has managed many teams of software and data engineers.
£43.19
Manning Publications Practical Data Science with R
Book SynopsisThis invaluable addition to any data scientist’s library shows you how to apply the R programming language and useful statistical techniques to everyday business situations as well as how to effectively present results to audiences of all levels. To answer the ever-increasing demand for machine learning and analysis, this new edition boasts additional R tools, modeling techniques, and more. Practical Data Science with R, Second Edition takes a practice oriented approach to explaining basic principles in the ever-expanding field of data science. You’ll jump right to real-world use cases as you apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. Key features • Data science and statistical analysis for the business professional • Numerous instantly familiar real-world use cases • Keys to effective data presentations • Modeling and analysis techniques like boosting, regularized regression, and quadratic discriminant analysis Audience While some familiarity with basic statistics and R is assumed, this book is accessible to readers with or without a background in data science. About the technology Business analysts and developers are increasingly collecting, curating, analyzing, and reporting on crucial business data. The R language and its associated tools provide a straightforward way to tackle day-to-day Nina Zumel and John Mount are co-founders of Win-Vector LLC, a San Francisco-based data science consulting firm. Both hold PhDs from Carnegie Mellon and blog on statistics, probability, and computer science at win-vector.com.
£50.37
Pearson Education Effective Data Analysis
Book SynopsisMona Khalil is a Data Science Manager at Greenhouse Software. Mona holds a degree in psychology from Fordham University and statistics at Baruch College, as well as having a decade of experience working with analytics and data science. Mona has worked with cross-functional teams in a variety of industries, including government, education, and HR technology.
£39.09
Nova Science Publishers Inc Computational Data Analysis Techniques in
Book Synopsis
£170.39
Information Age Publishing Data Analytics and Psychometrics: Informing
Book SynopsisThe 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.Table of Contents On Integrating Psychometrics and Learning Analytics in Complex Assessments, Robert J. Mislevy. Exploring Process Data in Problem-Solving Items in Computer-Based Large-Scale Assessments: Case Studies in PISA and PIAAC, Qiwei He, Matthias von Davier, and Zhuangzhuang Han. The Use of Data Mining Techniques to Detect Cheating, Sarah L. Thomas and Dennis D. Maynes. Selected Applications of Data Science in Cyber Security, Yue (Richard) Xie. Assessing Learner -Driven Constructs in Informal Learning Environments: Synergies Created by the Nexus of Psychometrics, Learning Analytics, and Educational Data Mining, Lori C. Bland. Measuring Rater Effectiveness: New Uses of Value-Added Modeling in Competency-Based Education, B. Brian Kuhlman. Ranking Documents in Online Enterprise Social Network, Alex H. Wang and Umeshwar Dayal. Methods for Measuring Learning Evaluation in the Context of E-Learning, Matthew Pietrowski, Roopa Sanwardeker, and David Witkowski. High Level Strategic Approaches for Conducting Big Data Studies in Assessment, Manfred M. Straehle, Liberty J. Munson, Austin Fossey, and Emily Kim. Integrating Survey and Learning Analytics Data for a Better Understanding of Engagement in MOOCs, Evgenia Samoilova, Florian Keusch, and Frauke Kreuter.
£47.45
Information Age Publishing Data Analytics and Psychometrics: Informing
Book SynopsisThe 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.Table of Contents On Integrating Psychometrics and Learning Analytics in Complex Assessments, Robert J. Mislevy. Exploring Process Data in Problem-Solving Items in Computer-Based Large-Scale Assessments: Case Studies in PISA and PIAAC, Qiwei He, Matthias von Davier, and Zhuangzhuang Han. The Use of Data Mining Techniques to Detect Cheating, Sarah L. Thomas and Dennis D. Maynes. Selected Applications of Data Science in Cyber Security, Yue (Richard) Xie. Assessing Learner -Driven Constructs in Informal Learning Environments: Synergies Created by the Nexus of Psychometrics, Learning Analytics, and Educational Data Mining, Lori C. Bland. Measuring Rater Effectiveness: New Uses of Value-Added Modeling in Competency-Based Education, B. Brian Kuhlman. Ranking Documents in Online Enterprise Social Network, Alex H. Wang and Umeshwar Dayal. Methods for Measuring Learning Evaluation in the Context of E-Learning, Matthew Pietrowski, Roopa Sanwardeker, and David Witkowski. High Level Strategic Approaches for Conducting Big Data Studies in Assessment, Manfred M. Straehle, Liberty J. Munson, Austin Fossey, and Emily Kim. Integrating Survey and Learning Analytics Data for a Better Understanding of Engagement in MOOCs, Evgenia Samoilova, Florian Keusch, and Frauke Kreuter.
£82.80
Harvard Business Review Press The Year in Tech, 2023: The Insights You Need
Book SynopsisA year of HBR's essential thinking on tech—all in one place.Easy-to-use AI tools, contactless commerce, crypto for business, the mature metaverse—new technologies like these are reshaping organizations at the hybrid office, on factory floors, and in the C-suite. What should you and your company be doing now to take advantage of the new opportunities these technologies are creating—and avoid falling victim to disruption? The Year in Tech 2023: The Insights You Need from Harvard Business Review will help you understand what the latest and most important tech innovations mean for your organization and how you can use them to compete and win in today's turbulent business environment.Business is changing. Will you adapt or be left behind?Get up to speed and deepen your understanding of the topics that are shaping your company's future with the Insights You Need from Harvard Business Review series. Featuring HBR's smartest thinking on fast-moving issues—blockchain, cybersecurity, AI, and more—each book provides the foundational introduction and practical case studies your organization needs to compete today and collects the best research, interviews, and analysis to get it ready for tomorrow.You can't afford to ignore how these issues will transform the landscape of business and society. The Insights You Need series will help you grasp these critical ideas—and prepare you and your company for the future.
£14.24
Information Age Publishing Use of Visual Displays in Research and Testing:
Book SynopsisVisual displays play a crucial role in knowledge generation and communication. The purpose of the volume is to provide researchers with a framework that helps them use visual displays to organize and interpret data; and to communicate their findings in a comprehensible way within different research (e.g., quantitative, mixed methods) and testing traditions that improves the presentation and understanding of findings. Further, this book includes contributions from leading scholars in testing and quantitative, qualitative, and mixed methods research, and results reporting. The volume’s focal question is: What are the best principles and practices for the use of visual displays in the research and testing process, which broadly includes the analysis, organization, interpretation, and communication of data?The volume is organized into four sections. Section I provides a rationale for this volume; namely, that including visual displays in research and testing can enhance comprehension and processing efficiency. Section II includes addresses theoretical frameworks and universal design principles for visual displays. Section III examines the use of visual displays in quantitative, qualitative, and mixed methods research. Section IV focuses on using visual displays to report testing and assessment data.
£47.45
Information Age Publishing Use of Visual Displays in Research and Testing:
Book SynopsisVisual displays play a crucial role in knowledge generation and communication. The purpose of the volume is to provide researchers with a framework that helps them use visual displays to organize and interpret data; and to communicate their findings in a comprehensible way within different research (e.g., quantitative, mixed methods) and testing traditions that improves the presentation and understanding of findings. Further, this book includes contributions from leading scholars in testing and quantitative, qualitative, and mixed methods research, and results reporting. The volume’s focal question is: What are the best principles and practices for the use of visual displays in the research and testing process, which broadly includes the analysis, organization, interpretation, and communication of data?The volume is organized into four sections. Section I provides a rationale for this volume; namely, that including visual displays in research and testing can enhance comprehension and processing efficiency. Section II includes addresses theoretical frameworks and universal design principles for visual displays. Section III examines the use of visual displays in quantitative, qualitative, and mixed methods research. Section IV focuses on using visual displays to report testing and assessment data.
£82.80
Sourcebooks Data Leadership for Everyone
Book SynopsisA revolutionary approach to bringing data and business togetherData is lazy. It sits in files or databases, minding its own business but not accomplishing very much. Data is like someone in their mid-twenties, living with their parents, who won''t get off the couch and make something of their life. Data is also the closest thing we have to truth in our organizationsbut most business leaders today struggle using data to make an impact on what really matters: the success of their businesses.Data Leadership for Everyone is a game-changing book for anyone who believes in the power of data and is ready to create revolutionary change in their organization. Whether you''re a C-suite executive, a manager, or an individual contributor, this book will propel your career by unlocking the mystery of how raw data transforms into real outcomes. In this book, data leadership advocate and transformation coach Anthony J. Algmin reveals his five-step Data Leadership Fram
£12.59
Arcler Press Data Centre Management
Book SynopsisThis text provides an overview of the principles and practices involved in managing and operating data centers. It covers topics such as data center design, infrastructure management, virtualization, cloud computing, and security. The book is intended for IT professionals and data center managers who are responsible for the operation and maintenance of data centers. It provides valuable insights and best practices for optimizing data center performance, reliability, and efficiency.Table of Contents Chapter 1 Introduction to Data Center Management Chapter 2 Data Center Topologies and Network Architecture Chapter 3 Security and Compliance in Data Protection Chapter 4 Monitoring and Management Tools Chapter 5 Virtualization and Cloud Computing Chapter 6 Importance of Power and Cooling Management Chapter 7 Challenges in Data Center Management Chapter 8 Future Trends in Data Center Management
£87.20
BCS Learning & Development Limited Driving Data Projects
Book SynopsisDigital transformation and data projects are not new and yet, for many, they are a challenge. Driving Data Projects is a compelling guide that empowers data teams and professionals to navigate the complexities of data projects, fostering a more data-informed culture within their organizations.With practical insights and step-by-step methodologies, this guide provides a clear path how to drive data projects effectively in any organization, regardless of its sector or maturity level whilst also demonstrating how to overcome the overwhelming feelings of where to start and how to not lose momentum. This book offers the keys to identifying opportunities for driving data projects and how to overcome challenges to drive successful data initiatives.Driving Data Projects is highly practical and provides reflections, worksheets, checklists, activities, and tools making it accessible to students new to driving data proj
£33.24
BCS Learning & Development Limited Data Science Foundations
Book Synopsis
£28.49
Edward Elgar Publishing Ltd The Evaluation of Complex Infrastructure
Book SynopsisQualitative 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.Trade Review'Disentangling within-case complexity is a challenging task; even more so if one examines multiple cases. Gerrits and Verweij brilliantly demonstrate, using the latest conceptual and technical innovations, and through the concrete example of infrastructure projects, that QCA can produce qualitative leaps in taking on this challenge. This book is a must-read for researchers, evaluators and practitioners who take both complexity and comparison seriously.' --Benoit Rihoux, Universite catholique de Louvain, BelgiumTable of ContentsContents: 1. Not a Straightforward Path: Developing and Evaluating Infrastructure Projects 2. The Case 3. Calibration 4. Comparison 5. Complexity and Evaluation Revisited References Index
£81.70
Pelagic Publishing An Introduction to R: Data Analysis and
Book SynopsisThe modern world is awash with data. The R Project is a statistical environment and programming language that can help to make sense of it all. A huge open-source project, R has become enormously popular because of its power and flexibility. With R you can organise, analyse and visualise data. This clear and methodical book will help you learn how to use R from the ground up, giving you a start in the world of data science. Learning about data is important in many academic and business settings, and R offers a potent and adaptable programming toolbox. The book covers a range of topics, including: importing/exporting data, summarising data, visualising data, managing and manipulating data objects, data analysis (regression, ANOVA and association among others) and programming functions. Regardless of your background or specialty, you'll find this book the perfect primer on data analysis, data visualisation and data management, and a springboard for further exploration.Table of Contents1. A brief introduction to R 2. Basic math 3. Introduction to R objects 4. Making and importing data objects 5. Managing and exporting data objects 6. R object types and their properties 7. Working with data objects 8. Manipulating data objects 9. Summarizing data 10. Tabulation 11. Graphics: basic charts 12. Graphics: adding to plots 13. Graphics: advanced methods 14. Analyze data: statistical analyses 15. Programming tools Appendix Index
£35.00
Edward Elgar Publishing Ltd Working with Paradata, Marginalia and Fieldnotes:
Book SynopsisThis 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. ShermanTrade Review'This is an extremely important book that brings to the attention of social researchers and methodologists the fascinating potential and intrinsic interest of three kinds of by-product of the research process - field notes, paradata, and marginalia. Many of us are unfamiliar with all or some of these sources. The book is full of worked examples of their use which greatly enhances the book's utility for all of us. The editors and authors have done us all a great service in bringing to our attention research sources that can no longer be ignored.' --Alan Bryman, University of Leicester, UK'Paradata will become increasingly important to researchers, both as an insight into the complexity and richness of participants and contexts, but also it has great potential to improve the quality of our research. Ros Edwards and her colleagues have provided us with a wonderfully comprehensive set of essays that are both insightful and valuable. This is a book which will have great appeal to students and professional researchers from both the quantitative and qualitative traditions.' --Malcolm Williams, Cardiff University, UK'Taking an expansive and inclusive approach to its topic, Working with Paradata, Marginalia and Fieldnotes offers a stimulating tour of a neglected domain of methodology. Readers who customarily regard paradata as a ''dry and dull'' element of data archiving will be delighted to read of the hidden corners of the research enterprise that this book's understanding of paradata and marginalia illuminates. Launching what is effectively a new field of inquiry, the book shows how these materials contribute to the field's renewed process of self-discovery.' --Nigel Fielding, University of Surrey, UKTable of ContentsContents: Marginalia - A Poem by Billy Collins Preface Robert G. Burgess 1. Introduction: Working with Paradata, Marginalia and Fieldnotes John Goodwin, Henrietta O’Connor, Ann Phoenix and Rosalind Edwards 2. Paradata for Non-response Investigations in Social Surveys Gabrielle B. Durrant and Olga Maslovskaya 3. Using Paradata to Evaluate Survey Quality: Behaviour Coding the 2012 PSE UK Survey Eldin Fahmy and Karen Bell 4. ‘Another Long and Involved Story’: Narrative Themes in the Marginalia of the Poverty in the UK Survey Ann Phoenix, Janet Boddy, Rosalind Edwards and Heather Elliott 5 ‘The House Seemed to be Falling Down Around Their Ears’: Contesting and Amplifying Observations of Housing Through Qualitative Survey Paradata Daniel Kilburn 6. The Secondary Analysis of Fieldnotes, Marginalia and Paradata from Past Studies of Young People Henrietta O’Connor and John Goodwin 7. John Adam’s Marginalia: Then and Now H.J. Jackson 8. ‘Soiled by Use’ or ‘Enlivened by Association’? Attitudes Towards Marginalia William H. Sherman 9. Afterword: The Craft of Paradata, Marginalia and Fieldnotes Rosalind Edwards, Ann Phoenix, John Goodwin and Henrietta O’Connor Index
£88.35
Emerald Publishing Limited The Ethics of Online Research
Book SynopsisThis volume focuses on the ethics of internet and social networking research exploring the challenges faced by researchers making use of social media and big data in their research. The internet, the world wide web and social media – indeed all forms of online communications – are attractive fields of research across a range of disciplines. They offer opportunities for methodological initiatives and innovations in research and easily accessed, massive amounts of primary and secondary data sources. This collection examines the new challenges posed by data generated online, explores how researchers are addressing those ethical challenges, and provides rich case studies of ethical decision making in the digital age.Trade ReviewSocial science researchers from the UK and Australia provide 10 chapters on the ethical aspects of internet-mediated and social media research. They address ethical challenges of specific research issues, social media platforms, or approaches, particularly the organized ethics review process; users’ views of ethics in social media research; the roles of researchers and participants; Twitter as a data source; informed consent; the use of Tinder; publishing and sharing social media research data; an ethics framework for working with social media data; and recommendations for improving ethical standards. The volume originated in the formation of an online community of practice called “New Social Media, New Social Science?” in 2011. -- Annotation ©2018 * (protoview.com) *Table of ContentsIntroduction: The Ethics of Online Research; Kandy Woodfield and Ron Iphofen 1. The Ethical Disruptions of Social Media Data: Tales from the Field; Susan Halford 2. Users’ View of Ethics in Social Media Research: Informed Consent, Anonymity and Harm; Matthew L. Williams, Pete Burnap, Luke Sloan, Curtis Jessop and Hayley Lepps 3. The Changing Roles of Researchers and Participants in Digital and Social Media Research: Ethics Challenges and Forward Directions; Sarah Quinton and Nina Reynolds 4. Using Twitter as a Data Source: An Overview of Ethical, Legal and Methodological Challenges; Wasim Ahmed, Peter A. Bath, Gianluca Demartini 5. Getting to Yes: Informed Consent in Qualitative Social Media Research; Janet Salmons 6. The Trouble with Tinder: The Ethical Complexities of Researching Location-Aware Social Discovery Apps; Jenna Condie, Garth Lean and Brittany Wilcockson 7. Ethical Challenges of Publishing and Sharing Social Media Research Data; Libby Bishop and Daniel Gray 8. The Ethics of Using Social Media Data in Research: A New Framework; Leanne Townsend and Claire Wallace 9. Where Next for #SocialEthics?; Steven Ginnis Conclusion: Guiding the Ethics of Online Social Media Research - Adaptation or Renovation?; Ron Iphofen
£82.99
Edward Elgar Publishing Ltd Handbook of Qualitative Research Techniques and
Book SynopsisOne 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. Bøllingtoft, E. Chell, E. Díaz de León, 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. WatsonTrade Review'This is a much-needed addition to research methods in entrepreneurship. This book champions valuable practices for studying entrepreneurial phenomena in rigorous ways. Five qualitative interview methods (constant comparative technique, metaphor methodologies, critical incident technique, focus groups and repertory grids) are grounded in prior theory and research, and demonstrated in specific research situations in ways that offer scholars insightful and important approaches to exploring entrepreneurship. This is a ''must buy'' for scholars who want to utilize better and more insightful methods for exploring the ideas, context and praxis of entrepreneurship.' --William B. Gartner, Copenhagen Business School, Denmark and California Lutheran University, US'This book will appeal to all researchers interested in qualitative research within the entrepreneurship field. The editors, Neergaard and Leitch, have put together a great group of experts who provide a fantastic overview on a wide range of known and lesser-known techniques. There is much to be discovered even for the experienced researcher. A great ''how to'' guide and a must-read for all qualitative entrepreneurship researchers, be they novices or experienced researchers.' --Friederike Welter, Institut fur Mittelstandsforschung (IfM) Bonn and University of Siegen, GermanyTable of ContentsContents: Introduction PART 1 AN INTRODUCTION TO THE CONSTANT COMPARATIVE TECHNIQUE Alistair R. Anderson and Sarah L. Jack 1. Using the Constant Comparative Technique to Consider Network Change and Evolution Sarah L. Jack, Alistair R. Anderson, Sarah Drakopoulou Dodd and Susan Moult 2. Using Constant Comparison as a Method of Analysis in Entrepreneurship Research Susan M. Smith and Edward McKeever 3. Grounded Theory Analysis in Entrepreneurship Research Anne Bøllingtoft The Future for the Constant Comparative Technique Alistair R. Anderson and Sarah L. Jack PART II METAPHOR METHODOLOGIES: EXPLORING ENTREPRENEURSHIP RESEARCH, PEDAGOGY AND RESEARCHERS Sarah Drakopoulou Dodd and Alice de Koning 4. Enacting, Experimenting and Exploring Metaphor Methodologies in Entrepreneurship Sarah Drakopoulou Dodd and Alice de Koning 5. Con’text’ualising Images of Enterprise: An Examination of ‘Visual Metaphors’ used to Represent Entrepreneurship in Textbooks Robert Smith 6. Metaphors in Communication of Scholarly Work Cecilia Bjursell Metaphor Methodologies in Entrepreneurship Research Sarah Drakopoulou Dodd and Alice de Koning SECTION III THE CRITICAL INCIDENT TECHNIQUE: AN OVERVIEW Claire M. Leitch 7. Researching the Entrepreneurial Process using the Critical Incident Technique Elizabeth Chell 8. The Efficacy of the Qualitative Variant of the Critical Incident Technique (CIT) in Entrepreneurship Research Claire M. Leitch and Frances M. Hill 9. A Critical Incident Technique Approach to Entrepreneurship Research using Phenomenological Explicative Data Collection Richard T. Harrison Critical Incident Technique: Some Conclusions Claire M. Leitch PART IV PROVENANCE AND USE OF FOCUS GROUPS John Watson and Rick Newby 10. Conducting a Traditional Focus Group John Watson, Rick Newby, Helle Neergaard and Robert Smith 11. Conducting a Focus Group using Group Support System (GSS) Software Geoff Soutar, Rick Newby and John Watson 12. Conducting an On-line Focus Group Rick Newby and John Watson Focus Groups: What have we Learned? John Watson and Rick Newby PART V REPERTORY GRIDS IN ENTREPRENEURSHIP: PRACTICAL EXAMPLES FROM RESEARCH Rita G. Klapper 17. Using Repertory Grid Technique to Explore the Relationship between Business Founders and Support Agents Anja Hagedorn 18. Using Repertory Grid to Assess Intangibles: Uncertainty Reduction for Lean Start-ups in Entrepreneurship Enrique Díaz de León and Paul Guild 19. Repertory Grid Technique: An Ideographic and Nomothetic Approach to Knowledge Carmen Dima 20. Concluding Thoughts on Repertory Grids Rita G. Klapper Index
£46.50
Kogan Page Ltd Excellence in People Analytics: How to Use
Book SynopsisEffectively 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, whilst navigating the rapidly evolving world of work. Full of practical tools and advice assembled around the Insight222 Nine Dimensions in People Analytics® 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®, Excellence in People Analytics is essential reading for all HR professionals needing to unlock the potential in their people data and gain competitive advantage.Trade Review"In this book, cutting-edge practitioners share insights that you can start putting into action right away."" * Adam Grant, #1 New York Times bestselling author of THINK AGAIN and host of the TED podcast, WorkLife *"Exceptional and the standard for people analytics" * Dave Ulrich, Rensis Likert Professor, School of Business, University of Michigan Partner, The RBL Group *"A superb book with practical case studies applicable to every HR professional and business leader in the use of data analytics towards better decision making."" * Low Peck Kem, Chief Human Resources Officer, Public Service Division, Prime Minister’s Office, Singapore *"Filled with topical case studies that can support any people analytics and HR team in their pursuit of creating enterprise value." * Loren I. Shuster, Chief People Officer & Head of Corporate Affairs, The LEGO Group *"HR is closer to the business than ever, and this book shows how people analytics is a business activity that drives substantial value." * Katarina Berg, Chief Human Resources Officer, Spotify *"There is a need for companies to become more human in our increasingly digital age. I have found, as a CHRO, that analytics provides equal benefit to both employees and the business, and Excellence in People Analytics dovetails these two very well. Using analytics is clearly one of the most valuable tools for becoming more human, enabling personalization and consumerisation of the employee experience." * Leena Nair Chief Human Resources Officer, Unilever *"People analytics provides business executives with another lever to improve their strategy and operations. Jonathan and David have a deep understanding of this topic and its impact on people and performance. Their work with companies across the globe is now captured in this book, providing insight with a collection of terrific case studies and practical advice. It is an outstanding guide for executives wishing to create value using people analytics." * John Boudreau, Professor Emeritus, Marshall School of Business, University of Southern California *"Excellence in People Analytics is a delightful journey of discovery through the field of people analytics with 30 vivid case studies and practical models. Businesses have recognized that workforce data can unleash the potential of talent and create value for the company. Yet people analytics is one of the biggest capability gaps for organizations. This book inspires me and is a great guide to implement people analytics beyond the 'buzz' term." * Rosa Lee, Executive Vice President of Bosch China & Corporate HR, Head of Asia-Pacific *"Excellence in People Analytics will equip HR leaders and practitioners with the structures and use cases they need to keep up with technology and learn new skills. I have little doubt that this book will define HR's contribution to the workplace of the future." * Bernard Marr, Bestselling author of Data Strategy and Data-Driven HR, futurist and strategic advisor *"Brilliantly insightful, yet practically impactful. This is a foundational book in the field of people analytics that emphasizes a business-first approach for elevating human performance. The nine dimensions for success are complemented with powerful cases that will empower any practitioner to apply these concepts." * Michael J. Arena, VP Talent & Development, AWS and author of Adaptive Space *Table of Contents Section - PART ONE: The case for people analytics; Chapter - 00: Introduction; Chapter - 000: The business value of people analytics; Section - PART TWO: Nine Dimensions for Excellence in People Analytics; Chapter - 01: Governance; Chapter - 02: Methodology; Chapter - 03: Stakeholder Management; Chapter - 04: Skills; Chapter - 05: Technology; Chapter - 06: Data; Chapter - 07: Workforce Experiences; Chapter - 08: Business Outcomes; Chapter - 09: Culture; Section - PART THREE: The next steps for people analytics; Chapter - 10: Transforming people analytics; Chapter - 11: Epilogue - the future of people analytics; Chapter - 12: Concluding remarks; Chapter - 13: Glossary; Chapter - 14: Index
£95.00
Kogan Page Ltd Confident Data Skills: How to Work with Data and
Book SynopsisData 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.Trade Review"The most comprehensive book I have seen for those wanting to get into data science - what Harvard Business Review called 'the sexiest job of the 21st century'." * Ben Taylor, Chief AI Evangelist, DataRobot *"Kirill Eremenko's book skilfully unravels the mysteries behind all the popular analytics tools and techniques, as well as many of the algorithms that power intelligent systems. I would recommend it to anyone who wants to pursue a career in data science. " * Dan Shiebler, Senior Machine Learning Engineer, Twitter Cortex *"Kirill Eremenko has come up with an amazing, unique way of making data science simple. From novices to the most experienced, anyone wanting to learn about data science will benefit from this book. Kirill covers everything from what data is and how to wrangle it, to helping you develop your own data analysis process, to effectively communicating with data. This book has it all! " * Andy Kriebel, Head Coach, The Information Lab Data School *"Eremenko is an established voice in the field, and his book is a must-read for anyone with an interest in using data science for business. Crammed with advice, Confident Data Skills provides the means to broaden one's horizons through data." * Michael Segala, CEO and Co-Founder, SFL Scientific *"Terrific. Eremenko has a knack for rendering complex theories in clear, elegant prose. Instructive and spirited, it will help you think - not only about the world around you but also about yourself." * Damian Mingle, Chief Data Scientist, Intermedix *Table of Contents Chapter - 00: Introduction; Section - ONE: "What is it?" key principles; Chapter - 01: Defining data; Chapter - 02: How data fulfils our needs; Chapter - 03: AI and our Future; Section - TWO: "When and where can I get it?" data gathering and analysis; Chapter - 04: Identify the problem; Chapter - 05: Data preparation; Chapter - 06: Data analysis (part I); Chapter - 07: Data analysis (part II); Section - THREE: "How can I present it?" communicating data; Chapter - 08: Data visualization; Chapter - 09: Data presentation; Chapter - 10: Your career in data science
£14.44
Kogan Page Ltd Confident Data Skills: How to Work with Data and
Book SynopsisData 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.Trade Review"The most comprehensive book I have seen for those wanting to get into data science - what Harvard Business Review called 'the sexiest job of the 21st century'." * Ben Taylor, Chief AI Evangelist, DataRobot *"Kirill Eremenko's book skilfully unravels the mysteries behind all the popular analytics tools and techniques, as well as many of the algorithms that power intelligent systems. I would recommend it to anyone who wants to pursue a career in data science. " * Dan Shiebler, Senior Machine Learning Engineer, Twitter Cortex *"Kirill Eremenko has come up with an amazing, unique way of making data science simple. From novices to the most experienced, anyone wanting to learn about data science will benefit from this book. Kirill covers everything from what data is and how to wrangle it, to helping you develop your own data analysis process, to effectively communicating with data. This book has it all! " * Andy Kriebel, Head Coach, The Information Lab Data School *"Eremenko is an established voice in the field, and his book is a must-read for anyone with an interest in using data science for business. Crammed with advice, Confident Data Skills provides the means to broaden one's horizons through data." * Michael Segala, CEO and Co-Founder, SFL Scientific *"Terrific. Eremenko has a knack for rendering complex theories in clear, elegant prose. Instructive and spirited, it will help you think - not only about the world around you but also about yourself." * Damian Mingle, Chief Data Scientist, Intermedix *Table of Contents Chapter - 00: Introduction; Section - ONE: "What is it?" key principles; Chapter - 01: Defining data; Chapter - 02: How data fulfils our needs; Chapter - 03: AI and our Future; Section - TWO: "When and where can I get it?" data gathering and analysis; Chapter - 04: Identify the problem; Chapter - 05: Data preparation; Chapter - 06: Data analysis (part I); Chapter - 07: Data analysis (part II); Section - THREE: "How can I present it?" communicating data; Chapter - 08: Data visualization; Chapter - 09: Data presentation; Chapter - 10: Your career in data science
£40.00
Kogan Page Ltd Driving Digital Transformation through Data and
Book SynopsisLeading 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.Trade Review"After years of progress in AI, might the hype be growing faster than the reality? Are we about to enter an 'AI autumn'? Not if Borek and Prill have anything to say about it! Digital transformation is tough - this book improves your odds." * Thomas C Redman, "the Data Doc", Harvard Business Review Blogger and Author *"Clear and to the point in a language that works for executives. A must-read for any leader." * Holger Kömm, Senior Director Advanced Analytics, Adidas *"A great in-depth introduction to how to add value to companies using digitalization, data and AI." * Patrick Glauner, Professor of Artificial Intelligence, Deggendorf Institute of Technology *"Provides great guidance on how to think of data products instead of projects - which is a key factor in mastering the challenges of digitalization." * Carsten Bange, Founder and CEO, BARC (Business Application Research Centre) *"A must-read for everyone involved into turning digitization and AI into real value for your company. Whether you're in the middle of the process looking for some orientation or just about getting started, this book will provide you with the advice you need!" * Alexander Thamm, CEO and Founder of Alexander Thamm GmbH *Table of Contents Chapter - 01: Introduction to delivering data and AI products; Chapter - 02: Developing the data and AI product strategy and goals; Chapter - 03: Setting up the data and AI product delivery organization; Chapter - 04: Identifying and defining data and AI products; Chapter - 05: Delivering high quality data and AI products; Chapter - 06: Designing the data and AI platform and architecture; Chapter - 07: Driving transformative change with data and AI products; Chapter - 08: The future of data and AI products in your organization
£30.39
Kogan Page Ltd Driving Digital Transformation through Data and
Book SynopsisLeading 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.Trade Review"After years of progress in AI, might the hype be growing faster than the reality? Are we about to enter an 'AI autumn'? Not if Borek and Prill have anything to say about it! Digital transformation is tough - this book improves your odds." * Thomas C Redman, "the Data Doc", Harvard Business Review Blogger and Author *"Clear and to the point in a language that works for executives. A must-read for any leader." * Holger Kömm, Senior Director Advanced Analytics, Adidas *"A great in-depth introduction to how to add value to companies using digitalization, data and AI." * Patrick Glauner, Professor of Artificial Intelligence, Deggendorf Institute of Technology *"Provides great guidance on how to think of data products instead of projects - which is a key factor in mastering the challenges of digitalization." * Carsten Bange, Founder and CEO, BARC (Business Application Research Centre) *"A must-read for everyone involved into turning digitization and AI into real value for your company. Whether you're in the middle of the process looking for some orientation or just about getting started, this book will provide you with the advice you need!" * Alexander Thamm, CEO and Founder of Alexander Thamm GmbH *Table of Contents Chapter - 01: Introduction to delivering data and AI products; Chapter - 02: Developing the data and AI product strategy and goals; Chapter - 03: Setting up the data and AI product delivery organization; Chapter - 04: Identifying and defining data and AI products; Chapter - 05: Delivering high quality data and AI products; Chapter - 06: Designing the data and AI platform and architecture; Chapter - 07: Driving transformative change with data and AI products; Chapter - 08: The future of data and AI products in your organization
£90.25
Edward Elgar Publishing Ltd Handbook of Spatial Analysis in the Social
Book SynopsisProviding 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.Trade Review‘Rey and Franklin introduce this new Handbook with an allusion to a rapidly changing landscape. And perhaps because any landscape, but especially a swiftly evolving one needs solid landmarks, this collection is a welcome addition to the literature that should appeal to any researchers in the social sciences working to learn the ropes of spatial analysis.’ -- Antonio Paez, McMaster University, Canada‘A comprehensive collection of chapters, carefully curated, spanning the current state of the art of spatial analysis in the social sciences written by established experts in the field ably accompanied by those creating the expertise of the future.’ -- Danny Dorling, Oxford University, UKTable of ContentsContents: Introduction: Spatial analysis and the social sciences in a rapidly changing landscape xi Sergio J. Rey and Rachel S. Franklin PART 1 THEORY, FRAMEWORKS AND FOUNDATIONS 1 GIScience through the looking glass 2 Barbara P. Buttenfield 2 Locating spatial data in the social sciences 16 Jonathan Reades 3 Analytical environments 36 Roger Bivand 4 Complexity 64 Li An 5 Linking spatial patterns to processes 85 Colin Robertson and Jed Long PART 2 METHODS 6 Spatial econometrics 101 Luc Anselin 7 Local modeling in a regression framework 123 Mehak Sachdeva, Taylor Oshan and A. Stewart Fotheringham 8 Simulating geographical systems using cellular automata and agent-based models 142 Alison Heppenstall, Andrew Crooks, Ed Manley and Nick Malleson 9 Microsimulation 158 Nik Lomax 10 Multilevel models 173 Richard Harris 11 Context-dependent movement analysis 187 Somayeh Dodge 12 Spatial interaction modeling 208 Taylor Oshan 13 Spatial optimization 223 Alan T. Murray 14 Cluster identification 245 Edward Helderop and Tony H. Grubesic 15 Spatial point patterns 262 Stuart Sweeney and Sophia Arabadjis 16 Spatial dynamics 277 Wei Kang 17 GeoAI in social science 291 Wenwen Li 18 Exploratory spatial data analysis 305 Ran Wei 19 Geovisualization and geovisual analysis 322 Alasdair Rae 20 Immersive virtual reality and spatial analysis 336 Trevor M. Harris 21 Spatiotemporal data mining 352 Arun Sharma, Zhe Jiang and Shashi Shekhar PART 3 APPLICATIONS 22 Neighborhood change 370 Elizabeth Delmelle 23 The spatial analysis of gentrification: Formalizing geography in models of a multidimensional urban process 384 Elijah Knaap 24 Social networks in space 400 Clio Andris and Dipto Sarkar 25 Analysing the dynamics of inter-regional inequality: The case of Canada 416 Sébastien Breau 26 Spatial approaches to energy poverty 434 Caitlin Robinson 27 The shape of bias: Understanding the relationship between compactness and bias in U.S. elections 451 Levi John Wolf 28 Space and New Urbanism 470 Emily Talen 29 Space for wellbeing 481 Victoria Houlden 30 Urban analytics: History, trajectory and critique 503 Geoff Boeing, Michael Batty, Shan Jiang and Lisa Schweitzer PART 4 EMERGING CHALLENGES AND ISSUES 31 Reproducibility and replicability in spatial science 518 Michael F. Goodchild 32 An image library: The potential of imagery in (quantitative) social sciences 528 Daniel Arribas-Bel, Francisco Rowe, Meixu Chen and Sam Comber 33 Uncertainty 544 David C. Folch Index 559
£231.80
Cognella, Inc Elementary Statistics: A Guide to Data Analysis
Book SynopsisElementary Statistics: A Guide to Data Analysis Using R provides students with an introduction to both the field of statistics and R, one of the most widely used languages for statistical computing, analysis, and graphing in a variety of fields, including the sciences, finance, banking, health care, e-commerce, and marketing.Part I provides an overview of both statistics and R. Part II focuses on descriptive statistics and probability. In Part III, students learn about discrete and continuous probability distributions with chapters addressing probability distributions, binominal probability distributions, and normal probability distributions. Part IV speaks to statistical inference with content covering confidence intervals, hypothesis testing, chi-square tests and F-distributions. The final part explores additional statistical inference and assumptions, including correlation, regression, and nonparametric statistics. Helpful appendices provide students with an index of terminology, an index of applications, a glossary of symbols, and a guide to the most common R commands.Elementary Statistics is an ideal resource for introductory courses in undergraduate statistics, graduate statistics, and data analysis across the disciplines.
£125.40
Edward Elgar Publishing Ltd How to Design, Implement, and Analyse a Survey
Book SynopsisThis 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.Trade Review‘This book by Anthony Arundel is a must read for researchers or practitioners that plan to conduct a survey. In a very understandable and insightful way, Arundel takes the reader through the intricacies of each step involved in designing and implementing a high quality survey, from questionnaire testing and design to sampling, data processing, and analysis.’ -- Carter Bloch, CFA, Aarhus University, Denmark‘Anthony Arundel has experience from decades of statistical measurement, survey design, management, and analysis of survey outcomes. He knows what works and what does not, and this guide provides the reader with valuable and accessible information. Anyone who needs to understand survey design, and results, should read this book.’ -- Fred Gault, UNU-MERIT, the Netherlands and Tshwane University of Technology, South Africa‘This is a much-needed book. It provides a complete and detailed overview of all practical steps that are required for setting-up, executing, and analysing a survey of firms and other organizations. The clear and non-technical language makes the book highly accessible also to readers not experienced in survey techniques. Everyone planning to conduct a survey should consult this book.’ -- Christian Rammer, Centre for European Economic Research (ZEW), GermanyTable of ContentsContents: 1. Introduction 2. Survey fundamentals 3. Questionnaire design 4. Questionnaire testing 5. Survey implementation 6. Data processing activities 7. Data analysis and reporting 8. Conclusion References Annexes Index
£75.00
Edward Elgar Publishing Ltd Collaborative Inquiry for Organization
Book SynopsisThis 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.Trade Review‘This book is one of the best exemplars of showing how these two practices--research and helping--can inform each other constructively. This book is a welcome exploration of how these practices have enlarged our understanding of how human systems really work, how they must be studied, and how we can constructively intervene in them.’ -- - Edgar H. Schein, Professor Emeritus, MIT Sloan School of Management, US‘As scientists, we ask ourselves how we can contribute more to the amelioration of the most challenging issues of our time, such as global pandemics, climate change, social justice. Shani and Coghlan help us understand that there is a better way for science to influence decision makers. Collaborative inquiry is built to provide a scientific approach to change and Shani and Coghlan have been the masters of that approach for decades. This new book should be read by any scientist or leader who wants to make progress instead of just bemoaning the current state of affairs.’ -- - William A. (Bill) Pasmore, Professor of Practice, Colombia University, USTable of ContentsContents: Foreword 1 Massimo S. Brunelli Foreword 2 Michael Beer Preface 1. Introducing collaborative inquiry 2. Theoretical foundations 3. Methodology and methods of inquiry 4. Transformation and design 5. Phases, mechanisms and quality 6. The researcher, theorizing and opportunities Epilogue Afterword: Collaborative inquiry: takeaways and applications Philip H. Mirvis References Index
£69.35
Edward Elgar Publishing Ltd Handbook on the Politics and Governance of Big
Book SynopsisDrawing on the theoretical debates, practical applications, and sectoral approaches in the field, this ground-breaking Handbook unpacks the political and regulatory developments in AI and big data governance. Covering the political implications of big data and AI on international relations, as well as emerging initiatives for legal regulation, it provides an accessible overview of ongoing data science discourses in politics, law and governance.With novel insights into existing and emerging debates, this cutting-edge Handbook highlights the mutual effects of big data and AI on society. Amongst other theoretical and sectoral issues, chapters analyse the liability of AI use in autonomous weapons, the role of big data in healthcare and education, the intersections between AI and gender in human rights law, and the ethics of public facial-recognition technology. Addressing the many open questions and future regulatory problems, it uses data science to investigate the dynamics between the technical aspects, societal dynamics and governance implications of big data and AI.Transdisciplinary in scope, this Handbook will be invaluable to students and researchers across the fields of politics, law, governance and data science, alongside policymakers concerned with the regulation and governance of AI and big data in public and private institutions.Trade Review‘Zwitter and Gstrein have astutely brought together an impressive collection of chapters that address key themes in the politics and governance of AI and big data. From social justice and gender to privacy and rights, the Handbook provides a solid introduction to key debates and their implications for societies.’ -- Evelyn Ruppert, Goldsmiths, University of London, UK‘This volume succeeds in bringing together a wide ranging collection of original studies in a field that is as fast developing as it is important to keep track of. The reader who is interested in normative political and governance perspectives on AI and big data will find insightful analyses and well-informed discussions of the key problems of regulation and policy making in a digital age.’ -- Jeroen van den Hoven, Delft University of Technology, the NetherlandsTable of ContentsContents: Foreword xiii PART I INTRODUCTION Introduction to the Handbook on the Politics and Governance of Big Data and Artificial Intelligence 2 Andrej Zwitter and Oskar J. Gstrein PART II CONCEPTUAL PERSPECTIVES 1 Can AI governance be progressive? Group interests, group privacy and abnormal justice 19 Linnet Taylor 2 Big Data and the humanitarian sector: emerging trends and persistent challenges 41 Susanne Schmuck, Andrej Zwitter and Oskar J. Gstrein 3 Digital twins: potentials, ethical issues and limitations 64 Dirk Helbing and Javier Argota Sánchez-Vaquerizo 4 Governing Digital Twin technology for smart and sustainable tourism: a case study in applying a documentation framework for architecture decisions 105 Eko Rahmadian, Daniel Feitosa and Andrej Zwitter PART III PRINCIPLE-BASED APPROACHES TO THE GOVERNANCE OF BIG DATA AND AI 5 Digital transitional justice: unpacking the black box 139 Christopher K. Lamont and Medlir Mema 6 Autonomous weaponry and IR theory: conflict and cooperation in the age of AI 167 Amelia Hadfield and Alex Leveringhaus 7 Understanding emergent technology, instability and power in international political economy 188 Malcolm Campbell-Verduyn 8 Governance of AI and gender: building on International Human Rights Law and relevant regional frameworks 211 Elizabeth Coombs and Halefom Abraha PART IV SECTORAL APPROACHES TO THE GOVERNANCE OF BIG DATA AND AI 9 Better technological security solutions through human-centred design and development 245 Andrew B. Wootton, Caroline L. Davey, Dagmar Heinrich and Maximilian Querbach 10 On the governance of privacy-preserving systems for the web: should Privacy Sandbox be governed? 279 Lukasz Olejnik 11 Experiments with facial recognition technologies in public spaces: in search of an EU governance framework 315 Catherine Jasserand 12 Big Data, AI and health data: between national, European, and international legal frameworks 358 Nikolaus Forgó, Emily Johnson, Iana Kazeeva and Elisabeth Steindl 13 Governing the ‘datafied’ school: bridging the divergence between universal education and student autonomy 395 Theresa Henne and Oskar J. Gstrein PART V AUTONOMOUS SYSTEMS, RIGHTS AND DUTIES 14 Artificial Intelligence and international human rights law: implications for humans and technology in the 21st century and beyond 430 Joshua C. Gellers and David J. Gunkel 15 Challenges posed by autonomous systems to liability regimes: finding a balance 456 Nynke E. Vellinga 16 Autonomous Weapons Systems in warfare: is Meaningful Human Control enough? 476 Taís Fernanda Blauth Index 504
£175.75
Edward Elgar Publishing Ltd Handbook of Big Data Research Methods
Book SynopsisThis state-of-the-art Handbook provides an overview of the role of big data analytics in various areas of business and commerce, including accounting, finance, marketing, human resources, operations management, fashion retailing, information systems, and social media. It provides innovative ways of overcoming the challenges of big data research and proposes new directions for further research using descriptive, diagnostic, predictive, and prescriptive analytics.With contributions from leading academics and practitioners, the Handbook analyses how big data analytics can be used in different sectors, including detecting credit fraud in the financial sector, identifying potential diseases in health care, and increasing customer loyalty in the telecommunication sector. Chapters explore the use of artificial intelligence in accounting, the construction of successful data science ecosystems using the public cloud, and transformational models of personal data protection in the digital era. The Handbook also discusses the difficulties of adopting a data science platform and how the public cloud can aid companies in overcoming these challenges.Exploring how industries rely on predictive analytics to improve their decision-making, this Handbook will be essential reading for students and scholars in business analytics, economics, information systems, innovation and technology, and research methods. It will also benefit data analysts, economists, human resource managers, marketers, neuroscientists, and social science researchers.Trade Review‘Big data research methods have gained dramatic momentum in the world. Researchers and practitioners extend this line of research constantly by producing journals, posts, news articles and podcasts. However, there is a paucity of a book that covers descriptive, diagnostic, predictive and prescriptive method-based research papers under one umbrella. This is one of those books which will immerse a reader in the past, present and future of big data analytics methods. It is an exceptional book that is grounded in evidence and meaningful to practice.’ -- Yogesh K. Dwivedi, Swansea University, UKTable of ContentsContents: 1 Introduction to the Handbook of Big Data Research Methods 1 Shahriar Akter, Samuel Fosso Wamba, Shahriar Sajib and Sahadat Hossain 2 Big data research methods in financial prediction 11 Md Lutfur Rahman and Shah Miah 3 Big data, data analytics and artificial intelligence in accounting: an overview 32 Sudipta Bose, Sajal Kumar Dey and Swadip Bhattacharjee 4 The benefits of marketing analytics and challenges 52 Madiha Farooqui 5 How big data analytics will transform the future of fashion retailing 72 Niloofar Ahmadzadeh Kandi 6 Descriptive analytics and data visualization in e-commerce 86 P.S. Varsha and Anjan Karan 7 Application of big data Bayesian interrupted time-series modeling for intervention analysis 105 Neha Chaudhuri and Kevin Carillo 8 How predictive analytics can empower your decision making 117 Nadia Nazir Awan 9 Gaussian process classification for psychophysical detection tasks in multiple populations (wide big data) using transfer learning 128 Hossana Twinomurinzi and Hermanus C. Myburgh 10 Predictive analytics for machine learning and deep learning 148 Tahajjat Begum 11 Building a successful data science ecosystem using public cloud 165 Mohammad Mahmudul Haque 12 How HR analytics can leverage big data to minimise employees’ exploitation and promote their welfare for sustainable competitive advantage 179 Kumar Biswas, Sneh Bhardwaj and Sawlat Zaman 13 Embracing Data-Driven Analytics (DDA) in human resource management to measure the organization performance 195 P.S. Varsha and S. Nithya Shree 14 A process framework for big data research: social network analysis using design science 214 Denis Dennehy, Samrat Gupta and John Oredo 15 Notre-Dame de Paris cathedral is burning: let’s turn to Twitter 233 Serge Nyawa, Dieudonné Tchuente and Samuel Fosso Wamba 16 Does personal data protection matter in data protection law? A transformational model to fit in the digital era 266 Gowri Harinath 17 The future of AI-based CRM 278 Khadija Alnofeli, Shahriar Akter and Venkata Yanamandram 18 Descriptive analytics methods in big data: a systematic literature review 294 Nilupulee Liyanagamage and Mario Fernando Index
£171.00
Emerald Publishing Limited The Development of Open Government Data:
Book SynopsisOpen government data (OGD) has developed rapidly in recent years due to various benefits that can be derived through transparency and public access. However, researchers emphasize a lack of use instead of lack of disclosure as a key problem in OGD’s present development. Previous studies have approached this issue either from the supply-side, focusing on data quantity and quality, or from the demand-side, focusing on factors that affect users’ acceptance of OGD, but seldom consider both sides at the same time. This unique study compares the supply and demand sides of OGD and explores possible directions for the future development of OGD portals based on the discovered mismatches between the two. The authors improve OGD utilization by balancing the supply-side and demand-side according to citizens’ demands through OGD portals. Based on the concept of an OGD ecosystem, four connected studies are explored. The first study built an evaluation framework for understanding the development of the OGD supply-side. The second study focuses on a survey conducted to analyze the awareness and utilization of OGD portals by citizens, who are the primary users and major beneficiaries of OGD on the demand-side. A third study compares the supply and demand sides based on Diffusion of Innovation theory. A final study tests the proposed usability criteria for building an OGD portal by carrying out a between-subjects experiment including a virtual agent. Each case study examines a unique aspect of OGD in China, and also offers reflections on future directions for developing OGD. Providing a unique and enhanced theoretical and practical understanding of OGD and its usage, as well as proposing directions for OGD portals’ future development in order to encourage citizens’ OGD utilization, this is a must-read for researchers and policymakers examining the impact and possibilities of OGD.Table of ContentsChapter 1. Introduction Chapter 2. Key Concepts & Literature Review Chapter 3. An Evaluation of the Supply-Side of OGD Chapter 4. Understanding citizens’ demands for OGD and OGD utilization Chapter 5. A comparison of the supply-side and demand-side of OGD portals Chapter 6. The usability of OGD portals Chapter 7. Citizens’ acceptance and utilization of OGD portals: An experiment using a virtual agent Chapter 8. Discussion Chapter 9. Conclusion Appendix A. Complete Survey for Stage 2 Appendix B. Dialogues of the Virtual Agent for Stage 4 Appendix C. Complete Instruments for Stage 4
£75.04
Edward Elgar Publishing Ltd Collaborative Inquiry for Organization
Book SynopsisThis 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.Trade Review‘This book is one of the best exemplars of showing how these two practices--research and helping--can inform each other constructively. This book is a welcome exploration of how these practices have enlarged our understanding of how human systems really work, how they must be studied, and how we can constructively intervene in them.’ -- - Edgar H. Schein, Professor Emeritus, MIT Sloan School of Management, US‘As scientists, we ask ourselves how we can contribute more to the amelioration of the most challenging issues of our time, such as global pandemics, climate change, social justice. Shani and Coghlan help us understand that there is a better way for science to influence decision makers. Collaborative inquiry is built to provide a scientific approach to change and Shani and Coghlan have been the masters of that approach for decades. This new book should be read by any scientist or leader who wants to make progress instead of just bemoaning the current state of affairs.’ -- - William A. (Bill) Pasmore, Professor of Practice, Colombia University, USTable of ContentsContents: Foreword 1 Massimo S. Brunelli Foreword 2 Michael Beer Preface 1. Introducing collaborative inquiry 2. Theoretical foundations 3. Methodology and methods of inquiry 4. Transformation and design 5. Phases, mechanisms and quality 6. The researcher, theorizing and opportunities Epilogue Afterword: Collaborative inquiry: takeaways and applications Philip H. Mirvis References Index
£22.75
Edward Elgar Publishing Ltd Taking the Fear Out of Data Analysis: Completely
Book SynopsisTaking 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.Trade Review‘Written with wry wit and incredible clarity, the authors provide the reader with a detailed understanding of seminal issues in data analysis. A masterful work that truly does “take the fear out of data analysis” – this book is a rare treat indeed.’ -- David A. Griffith, Mays Business School, Texas A&M University, US‘Written by a proficient team of authors, Taking the Fear out of Data Analysis is a fascinating … ah, forget the marketing blurb. This is a great text, you should read it! There is no doubt that you will devour this book in no time and learn a lot about statistics on the way.' -- Marko Sarstedt, Ludwig-Maximilians-University (LMU), Germany‘Statistics. I know – you hate it. It’s hard and confusing. Students of all levels find the topic hard. I tell them to get this book. And no! They cannot borrow mine, I don’t want to lose it. Diamantopoulos, Schlegelmilch and Halkias knock another one out of the park with this excellent introduction to a great array of statistical issues. They start right at the beginning – which is always a good place to start if you’re a beginner – and gently, often hilariously, and successfully guide the reader through the various learning moments that need to be negotiated if one is to become fearless in the face of columns of data. Priceless.’ -- John Cadogan, School of Business and Economics, Loughborough University, UK‘The new edition of this book provides excellent guidance to data knowledge and competence using a problem-solving approach. With the digital becoming increasingly important, analytical skills should be key competencies in everybody’s daily life. To achieve this goal, Taking the Fear out of Data Analysis is highly recommended.’ -- Zhongming Wang, Zhejiang University, China‘The significantly extended, new edition is increasingly relevant as the world of quantitative methods has kept on expanding, in part due to an explosion in software programs that scholars can use seemingly without much understanding. Do not let the light-hearted nature of this book fool you. It is a statistics book that carefully leads authors through all the necessary stages of analysis. It effortlessly explains the analysis details and assumptions that PhD examiners, journal reviewers, and conference presentation audience members insist on raising. This excellent new edition is destined to be very well thumbed.’ -- Matthew Robson, Cardiff Business School, UKTable of ContentsContents: Pre-publication reviews from around the world Introduction to Taking the Fear out of Data Analysis PART I UNDERSTANDING DATA 1. What is data (and can you do it in your sleep)? 2. Does sampling have a purpose other than providing employment for statisticians? 3. Why should you be concerned about different types of measurement? PART II PREPARING DATA FOR ANALYSIS 4. Have you cleaned your data and found the mistakes you made? 5. Why do you need to know your objective before you fail to achieve it? PART III CARRYING OUT THE ANALYSIS 6. Why not take it easy initially and describe your data? 7. Can you use few numbers in place of many to summarize your data? 8. What about using estimation to see what the population looks like? 9. How about sitting back and hypothesizing? 10. Simple things first: One variable, one sample 11. Getting experienced: Making comparisons 12. Getting adventurous: Searching for relationships 13. Getting hooked: A look into multivariate analysis 14. Getting obsessed: A further look into multivariate analysis 15 It’s all over … or is it? Index
£104.50
Transworld Making Numbers Count
Book Synopsis''Concise, breezy and pragmatic'' Wall Street Journal''Remarkably practical techniques for comprehending and communicating the maths that really matters' Adam GrantUntil very recently, most languages had no words for numbers greater than five anything from six to infinity was known as ''lots''. Understanding numbers is essential in the modern world, but we simply aren't built to understand them.What does 5GB of storage actually mean? (Two months of commutes, without repeating a song.)What's the size of a nucleus compared to a cell? (Imagine a bee in a cathedral.)How much bigger is a billion than a million? (Well, a million seconds is twelve days. A billion seconds isthirty-two years.)Drawing on years of research into making ideas stick, Chip Heath and Karla Starr outline six critical principles that will give anyone the tools to understand and communicate numbers with more transparency and meaning. Offering
£11.69
Troubador Publishing Present Sense: A Practical Guide to the Science
Book SynopsisIn this provocative yet practical guidebook Steve Morlidge demonstrates why the approach and methods of performance reporting that all information professionals have been taught fails, and what we need to do differently to help us make sense of the dynamic, complex and data rich world in which we now live and work. Reporting on performance should not be treated as worthy but dull, requiring no more than routine comparisons of actual against targets. This traditional approach is based on the false premise organisations can be managed as if they were a simple mechanical system operating in a predictable environment. And the methods associated with it, such as variance analyses and data tables that are used to measure and communicate performance, are completely inadequate. Instead, Morlidge argues performance reporting should be reconceived as an act of perception conducted on behalf of the organisation, helping to make sense of the sensory inputs (data) that it has at its disposal. And to do so effectively performance reporters need to learn from and exploit the strengths of our own brains, compensate for its weaknesses and communicate in a way that makes it easy for their audience’s brains to assimilate. Drawing on the latest insights from cognitive science in this book you will learn: • how to bring a dynamic perspective into performance reporting • how to deploy a set of simple tools to help speared the signal from the noise inherent in large data sets and to make sound inferences • how to set goals intelligently • about the grammar of data visualization and how use it to design powerful and simple reports In this way information professionals are uniquely charged with the responsibility for creating the shared consciousness that is a prerequisite for organisations to effectively respond and adapt to their environments.
£24.00
Zaffre The Love Algorithm: The perfect witty romcom, new
Book Synopsis'A superb romcom, with relatable and loveable characters that literally burst from the pages. With laugh-out-loud moments, woven between a heartwarming and hopeful story, it charmed me from the first page'Carmel HarringtonTrue love is only just a swipe away? Right?Iris lives by numbers. The only thing missing from her perfectly calibrated life is a partner - and not for lack of trying. After decades of disappointment, Iris practically has a PhD in online dating. But something still eludes her: that unquantifiable spark.Kim is too busy being the life of the party to look for love. Her terrible dates make great stories for her friends and co-workers, as long as she's not caught by her tyrannical boss, Iris.Connie, Kim's recently widowed mum, is single for the first time since the 1970s. The dating game has changed a lot since her day . . .Sick of being let down, Iris takes matters into her own hands - using her analytical skills to create the first real formula for love. With Kim and Connie on board, they launch Analyzed, a dating app like no other.As Analyzed takes the world by storm, are the three women in over their heads? Is love really just a numbers game?'A laugh on every page, entirely empathetic characters and a warm heart in the middle of the story. I found this book highly entertaining and totally satisfying' Liz NugentPraise for Claudia Carroll:'Brilliantly funny' Sun'An immensely talented writer' Sinéad Moriarty'Brilliant' BellaTrade ReviewA superb romcom, with relatable and loveable characters that literally burst from the pages. With laugh-out-loud moments, woven between a heartwarming and hopeful story, it charmed me from the first page. In short, I couldn't put it down, the perfect summer read! * Carmel Harrington *A laugh on every page, entirely empathetic characters and a warm heart in the middle of the story. I found this book highly entertaining and totally satisfying * Liz Nugent *
£12.59
Edward Elgar Publishing Ltd Elgar Encyclopedia of Law and Data Science
Book SynopsisThe 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. Table of ContentsContents: Introduction to the Encyclopedia of Law and Data Science: ‘Directions for Use’ ix Giovanni Comand. 1 Access 1 Giulia Schneider 2 Accountability 7 Giulia Schneider 3 Algorithm 12 Letizia Milli and Giulio Rossetti 4 Algorithmic Discrimination 17 Bettina Berendt 5 Algorithmic Fairness 32 Salvatore Ruggieri 6 Anonymity 36 Dino Pedreschi, Roberto Pellungrini, Francesca Pratesi 7 Anonymous Data 41 Lorenzo Dalla Corte 8 Argument Mining 48 Vern R. Walker 9 Artificial General Intelligence 53 Bettina Berendt 10 Bias [definition] 60 Antonio Davola 11 Children (in the Digital Environment) 64 Denise Amram 12 Clustering (see Data Mining and Clustering) 70 13 Computer Programs 70 Guido Noto La Diega 14 Confidentiality 73 Arianna Rossi, Itzel Vazquez Sandoval, Gabriele Lenzini 15 Consent 81 Cesare Bartolini 16 Copyright 83 Caterina Sganga 17 Cybersecurity (in Distributed Computing Systems) 92 Tommaso Cucinotta 18 Database Protection 98 Caterina Sganga 19 Data Breach 105 Anna Monreale, Roberto Pellungrini, Francesca Pratesi 20 Data Mining and Clustering 110 Athanasios Kiourtis, Argyro Mavrogiorgou, Dimosthenis Kyriazis 21 Data Protection 122 Cesare Bartolini 22 Data Protection Impact Assessment 125 Anna Monreale, Roberto Pellungrini, Francesca Pratesi 23 Data Quality 130 Michela Natilli, Salvatore Rinzivillo, Franco Turini 24 Data Subject 134 Gloria Gonz.lez Fuster 25 Decision-making 139 Gianclaudio Malgieri 26 Discrimination Data Analysis 142 Salvatore Ruggieri 27 Disparate Impact (from Software-Based Decision-Making Systems) 146 Jeanna Neefe Matthews 28 Erasure 150 Cesare Bartolini 29 Ethics 153 Giorgia Pozzi and Juan M. Dur.n 30 Explainability 160 Riccardo Guidotti, Fosca Giannotti, Dino Pedreschi 31 Fairness 168 Giulia Schneider 32 Forgotten (Right to Be) 175 Paul De Hert and Vagelis Papakonstantinou 33 Freedom of Information (Freedom of Expression – Access to Public Data) 181 Matteo Monti 34 Governance (of Personal Data Flows) 186 Denise Amram 35 Healthcare (Data Science in) 192 Martina Finocchiaro, Tommaso Banfi, Matteo Vissani, Alberto Mazzoni, Gastone Ciuti 36 Informed Consent 199 Danielle da Costa Leite Borges 37 Lawfulness and Necessity (of Possible Limitations on the Fundamental Rights to Privacy and to the Protection of Personal Data) in the EU Legal Order 203 Mario Guglielmetti 38 Legitimate Interest 209 Christopher F. Mondschein and Cosimo Monda 39 Liability 215 Andrea Parziale 40 Machine Learning 223 Luca Pappalardo 41 Mobility Data (Knowledge Discovery from) 227 Agnese Bonavita and Giovanni Comand. 42 Necessity (see Lawfulness and Necessity (of Possible Limitations on the Fundamental Rights to Privacy and to the Protection of Personal Data) in the EU Legal Order) 241 43 Open Data and Public Sector Information 241 Lorenzo Dalla Corte and Bastiaan van Loenen 44 Patents 253 Enrico Bonadio and Hannes Sigurgeirsson 45 Personal Data in the EU Legal System 259 Lorenzo Dalla Corte 46 Portability (of Data) 267 Gianclaudio Malgieri 47 Predictive Analytics 271 Mirco Nanni 48 Privacy 275 Carlotta Rigotti and Alessandra Calvi 49 Privacy by Design 281 Marina Sokolova and Stan Matwin 50 Privacy-Preserving Technologies 291 Josep Domingo-Ferrer 51 Profile/Profiling 300 Salvatore Ruggieri 52 Proportionality 305 Giuseppe Martinico 53 Pseudonymization 310 Anna Monreale, Roberto Pellungrini, Francesca Pratesi 54 Public Interest (Scientific Research and the Legal Grounds) 314 Hanne Elsen, Wessel Damen, Audrey Van Scharen 55 Public Sector Information (see Open Data and Public Sector Information) 318 56 Reasonable Safeguards 318 Andr.s Chomczyk Penedo 57 Scoring 323 Frank Pasquale 58 Software (Computer Programs) 326 Guido Noto La Diega 59 Supervisory Authorities (Powers) 329 Roberto Lattanzi 60 Surveillance 341 Juraj Sajfert 61 Trade Secrets and Data-Driven Innovation in the EU 347 Silvia Scalzini 62 Transparency 354 Giulia Schneider 63 Unfairness [definition] 360 Antonio Davola 64 Vulnerability 363 Gianclaudio Malgieri Analytical index 371 Index
£182.40
Vintage Publishing 99 Maps to Save the Planet: With an introduction
Book Synopsis'Terrifying yet funny, surprising yet predictable, simple yet poignant' Chris PackhamA shocking but informative, eye-catching and witty book of maps that illustrate the perilous state of our planet.The maps in this book are often shocking, sometimes amusing, and packed with essential information:· Did you know that just 67 companies worldwide are responsible for 67 per cent of global greenhouse emissions? · Or that keeping a horse has the same carbon footprint as a 23,500-kilometre road trip? · Did you know how many countries use less energy than is consumed globally by downloading porn from the internet?· Do you know how much of the earth's surface has been concreted over?· Or how many trees would we have to plant to make our planet carbon-neutral?Presenting a wealth of innovative scientific research and data in stunning, beautiful infographics, 99 Maps to Save the Planet provides us with instant snapshots of the destruction of our environment. At one glance, we can see the precarious state of our planet - but also realise how easy it would be to improve it Enlightening, a bit frightening, but definitely inspiring, 99 Maps to Save the Planet doesn't provide practical tips on how to save our planet: it just presents the facts. And the facts speak for themselves. Once we know them, what excuse do we have for failing to act?Trade ReviewYou'll never look at the fight for our common home in the same way again after seeing the images in this remarkable book * Big Issue *Impressively imaginative and effectively alarming * Wanderlust *Terrifying yet funny, surprising yet predictable, simple yet poignant -- Chris Packham
£15.29
ISTE Ltd and John Wiley & Sons Inc Data Analysis
Book SynopsisThe first part of this book is devoted to methods seeking relevant dimensions of data. The variables thus obtained provide a synthetic description which often results in a graphical representation of the data. After a general presentation of the discriminating analysis, the second part is devoted to clustering methods which constitute another method, often complementary to the methods described in the first part, to synthesize and to analyze the data. The book concludes by examining the links existing between data mining and data analysis.Trade Review"The first part of this book is devoted to methods seeking relevantdimensions of data. The variables thus obtained provide a synthetic description which often results in a graphical representation of the data. After a general presentation of the discriminating analysis, the second part is devoted to clustering methods which constitute another method, often complementary to the methods described in the first part, to synthesize and to analyze the data." (Zentralblatt MATH 2016)Table of ContentsPreface xiii Chapter 1. Principal Component Analysis: Application to Statistical Process Control 1 Gilbert SAPORTA, Ndèye NIANG 1.1. Introduction 1 1.2. Data table and related subspaces 2 1.3. Principal component analysis 8 1.4. Interpretation of PCA results 11 1.5. Application to statistical process control 18 1.6. Conclusion 22 1.7. Bibliography 23 Chapter 2. Correspondence Analysis: Extensions and Applications to the Statistical Analysis of Sensory Data 25 Jérôme PAGÈS 2.1. Correspondence analysis 25 2.2. Multiple correspondence analysis 39 2.3. An example of application at the crossroads of CA and MCA 50 2.4. Conclusion: two other extensions 63 2.5. Bibliography 64 Chapter 3. Exploratory Projection Pursuit 67 Henri CAUSSINUS, Anne RUIZ-GAZEN 3.1. Introduction 67 3.2. General principles 68 3.3. Some indexes of interest: presentation and use 71 3.4. Generalized principal component analysis 76 3.5. Example 81 3.6. Further topics 86 3.7. Bibliography 89 Chapter 4. The Analysis of Proximity Data 93 Gerard D’AUBIGNY 4.1. Introduction 93 4.2. Representation of proximity data in a metric space 97 4.3. Isometric embedding and projection 103 4.4. Multidimensional scaling and approximation 108 4.5. Afielded application 122 4.6. Bibliography 139 Chapter 5. Statistical Modeling of Functional Data 149 Philippe BESSE, Hervé CARDOT 5.1. Introduction 149 5.2. Functional framework152 5.3. Principal components analysis 156 5.4. Linear regression models and extensions 161 5.5. Forecasting 169 5.6. Concluding remarks 176 5.7. Bibliography 177 Chapter 6. Discriminant Analysis 181 Gilles CELEUX 6.1. Introduction 181 6.2. Main steps in supervised classification 182 6.3. Standard methods in supervised classification 190 6.4. Recent advances 204 6.5. Conclusion 211 6.6. Bibliography 212 Chapter 7. Cluster Analysis 215 Mohamed NADIF, Gérard GOVAERT 7.1. Introduction 215 7.2. General principles 217 7.3. Hierarchical clustering 224 7.4. Partitional clustering: the k-means algorithm 233 7.5. Miscellaneous clustering methods 239 7.6. Block clustering 245 7.7. Conclusion 251 7.8. Bibliography 251 Chapter 8. Clustering and the Mixture Model 257 Gérard GOVAERT 8.1. Probabilistic approaches in cluster analysis 257 8.2. The mixture model 261 8.3. EM algorithm 263 8.4. Clustering and the mixture model 267 8.5.Gaussian mixture model 271 8.6. Binary variables 275 8.7. Qualitative variables 279 8.8. Implementation 282 8.9. Conclusion 284 8.10. Bibliography 284 Chapter 9. Spatial Data Clustering 289 Christophe AMBROISE, Mo DANG 9.1. Introduction 289 9.2. Non-probabilistic approaches 293 9.3. Markov random fields as models 295 9.4. Estimating the parameters for a Markov field 305 9.5. Application to numerical ecology 313 9.6. Bibliography 316 List of Authors 319 Index 323
£145.30
LID Publishing The Fifth Phase: An insight-driven approach to
Book SynopsisThe connected world offers the potential for radical new business insights gleaned from previously unimaginable volumes of data. But business has got bogged down in the process of collecting and storing that data; money has been wasted on data lakes in which many IT departments have drowned without being able to deliver useful insights to business leaders. Big data has new and exciting answers to offer, but business leaders must first decide what questions it would like to see answered. Data may be the new oil, but to date we have only built oil depots. This book analyses the new, Fourth Wave of business transformation, which will build the refineries that turn data into useful products. Business has started from 'data up' and needs to start again from 'value down', going back to the drivers of real business value and deciding what insights would help realize that value. Only then can we begin to interrogate data with purpose.
£11.99
Springer Nature Switzerland AG Learning Analytics Cookbook: How to Support Learning Processes Through Data Analytics and Visualization
Book SynopsisThis book offers an introduction and hands-on examples that demonstrate how Learning Analytics (LA) can be used to enhance digital learning, teaching and training at various levels. While the majority of existing literature on the subject focuses on its application at large corporations, this book develops and showcases approaches that bring LA closer to smaller organizations, and to educational institutions that lack sufficient resources to implement a full-fledged LA infrastructure. In closing, the book introduces a set of software tools for data analytics and visualization, and explains how they can be employed in several LA scenarios.
£49.49
Springer Nature Switzerland AG Financial Data Analytics: Theory and Application
Book SynopsisThis book presents both theory of financial data analytics, as well as comprehensive insights into the application of financial data analytics techniques in real financial world situations. It offers solutions on how to logically analyze the enormous amount of structured and unstructured data generated every moment in the finance sector. This data can be used by companies, organizations, and investors to create strategies, as the finance sector rapidly moves towards data-driven optimization.This book provides an efficient resource, addressing all applications of data analytics in the finance sector. International experts from around the globe cover the most important subjects in finance, including data processing, knowledge management, machine learning models, data modeling, visualization, optimization for financial problems, financial econometrics, financial time series analysis, project management, and decision making. The authors provide empirical evidence as examples of specific topics. By combining both applications and theory, the book offers a holistic approach. Therefore, it is a must-read for researchers and scholars of financial economics and finance, as well as practitioners interested in a better understanding of financial data analytics. Table of ContentsPART 1. INTRODUCTION AND ANALYTICS MODELS.- Retraining and Reskilling Financial Participators in the Digital Age.- Basics of Financial Data Analytics.- Predictive Analytics Techniques: Theory and Applications in Finance.- Prescriptive Analytics Techniques: Theory and Applications in Finance.- Forecasting Returns of Crypto Currency - Analyzing Robustness of Auto Regressive and Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANNS).- PART 2. MACHINE LEARNING.- Machine Learning in Financial Markets: Dimension Reduction and Support Vector Machine.- Pruned Random Forests for Effective and Efficient Financial Data Analytics.- Foreign Currency Exchange Rate Prediction Using Long Short Term Memory.- Natural Language Processing (NLP) for Exploring Culture in Finance: Theory and Applications.- PART 3. TECHNOLOGY DRIVEN FINANCE.- Financial Networks: A Review of Models and the Use of Network Similarities.- Optimization of Regulatory Economic-Capital Structured Portfolios: Modeling Algorithms, Financial Data Analytics and Reinforcement Machine Learning in Emerging Markets.- Transforming Insurance Business with Data Science.- A General Cyber Hygiene Approach for Financial Analytical Environment.
£132.99
De Gruyter Leading by Weak Signals: Using Small Data to
Book SynopsisMaster complex problems and face radical uncertainty by unleashing the power of small data Is your business using data to its optimum potential? In complicated well-structured problem situations, executives rely on Big Data. However, when faced with complexity and uncertainty they are challenged to skillfully handle Small Data. Leading by Weak Signals argues that impending dangers, new business opportunities or innovative ideas may be missed when data are classified as simply not "big enough." This insightful book with its new approach initiates a radical shift in perspective from running the business to changing the business. While Big Data are very well suited to run a business efficiently, Small Data lay open phenomena which are connected to transforming a company, like inflection points, scale changes, or critical transitions. The authors present practical business examples and an 8-step framework to implement their ideas in teams and on the individual level. This offers reflective practitioners a guideline for leveraging the enormous potential of weak signals for effective strategy development and operational execution in times of uncertainty – and gives them the competitive edge they need to succeed.
£18.75
Peter Lang AG Theory and Practice Management and Organization
Book SynopsisThis book is a collection of empirical and theoretical research papers on Theory and Practice in Management and Organisation Studies, written by researchers from various universities. The book is aimed at educators, researchers, and students interested in this field.
£51.30
New Era Publications International APS Investigations
Book SynopsisMany people go through life in a rather hit-or-miss fashion, casting about for ideas to explain why their projects improve or decline, why they are successful or why they are not. Guessing and "hunches," however, are not very reliable. And without the knowledge of how to actually investigate situations, good or bad, and get the true facts, a person is set adrift in a sea of unevaluated data. Accurate investigation is, in fact, a rare commodity. Man's tendency in matters he doesn't understand is to accept the first proffered explanation, no matter how faulty. Thus investigatory technology had not actually been practiced or refined. However, L. Ron Hubbard made a breakthrough in the subject of logic and reasoning which led to his development of the first truly effective way to search for and consistently find the actual causes for things. Knowing how to investigate gives one the power to navigate through the random facts and opinions and emerge with the real reasons behind success or failure in any aspect of life. By really finding out why things are the way they are, one is therefore able to remedy and improve a situation-any situation. This is an invaluable technology for people in all walks of life.
£5.35
