Data science and analysis Books

Book SynopsisKinsey Li is an accomplished HR leader with 10 years of experience and a proven track record of delivering complex transformation projects both in industry and as a consultant. Based in London, UK, she is Associate Director, HR Analytics and Insights at Ernst and Young (EY). She holds and MBA, a postgraduate certificate in business IT, a postgraduate certificate in business and a BA in commerce.

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Book SynopsisHow data-informed decision making can make colleges and universities more effective institutions. The continuing importance of data analytics is not lost on higher education leaders, who face a multitude of challenges, including increasing operating costs, dwindling state support, limits to tuition increases, and increased competition from the for-profit sector. To navigate these challenges, savvy leaders must leverage data to make sound decisions. In Big Data on Campus, leading data analytics experts and higher ed leaders show the role that analytics can play in the better administration of colleges and universities. Aimed at senior administrative leaders, practitioners of institutional research, technology professionals, and graduate students in higher education, the book opens with a conceptual discussion of the roles that data analytics can play in higher education administration. Subsequent chapters address recent developments in technology, the rapid accumulation of data assetsTable of ContentsForeword, by Christine M. KellerAcknowledgments Part I. Technology, Digitization, Big Data, and Analytics Maturity as the Enabling Conditions for Data-Informed Decision MakingChapter 1. Data Analytics and the Imperatives for Data-Informed Decision Making in Higher Education Karen L. Webber and Henry Y. ZhengChapter 2. Big Data and the Transformation of Decision Making in Higher Education Braden J. HoschChapter 3. Predictive Analytics and Its Uses in Higher Education Henry Y. Zheng and Ying ZhouPart II. The Ethical, Cultural, and Managerial Imperatives of Data-Informed Decision Making in Higher EducationChapter 4. Limitations in Data Analytics: Potential Misuse and Misunderstanding in Data Reports and Visualizations Karen L. Webber and Jillian N. MornChapter 5. Guiding Your Organization's Data Strategy: The Roles of University Senior Leaders and Trustees in Strategic Analytics Gail B. Marsh and Rachit TharianiChapter 6. Data Governance, Data Stewardship, and the Building of an AnalyticsOrganizational Culture Rana Glasgal and Valentina NestorPart III. The Application of Analytics in Higher Education Decision Making: Case StudiesChapter 7. Data Analytics and Decision Making in Admissions and Enrollment Management Tom Gutman and Brian P. HinoteChapter 8. Predictive Analytics, Academic Advising, Early Alerts, and Student Success Timothy M. RenickChapter 9. Constituent Relationship Management and Student Engagement Lifecycle Cathy A. O'Bryan, Chris Tompkins, and Carrie Hancock MarcinkevageChapter 10. Learning Analytics for Learning Assessment: Complexities in Efficacy, Implementation, and Broad Use Carrie Klein, Jaime Lester, Huzefa Rangwala, and Aditya JohriChapter 11. Using Data Analytics to Support Institutional Financial and Operational Efficiency Lindsay K. Wayt, Susan M. Menditto, J. Michael Gower, and Charles TegenPart IV. Concluding CommentsChapter 12. Data-Informed Decision Making and the Pursuit of Analytics Maturity in Higher Education Karen L. Webber and Henry Y. ZhengContributorsIndex

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Book SynopsisA century of education and education reform, along with more than three decades of high-stakes testing and accountability, reveals a disturbing paradox: education has a steadfast commitment to testing and grading. This commitment persists despite ample research, theory, and philosophy revealing the corrosive consequences of both testing and grading in an education system designed to support human agency and democratic principles. This revised edited volume brings together a collection of updated and new essays that confronts the failure of testing and grading. The book explores the historical failure of testing and grading; the theoretical and philosophical arguments against testing and grading; the negative influence of tests and grades on social justice, race, class, and gender; and the role that they play in perpetuating a deficit perspective of children. The chapters fall under two broad sections. Part I, Degrading Learning, Detesting Education: The Failure of High-Stake AccountabiTable of ContentsContents: Rick Wormeli: An Unexamined Pedagogy Harms – Lisa Guisbond/Monty Neill/Bob Schaeffer: NCLB’s Lost Decade for Educational Progress: What Can We Learn from This Policy Failure? – Fernando F. Padró/ Michael F. Hawke/Laurie M. Hawke: Assessment and Quality: Policy-Steering and the Making of a Deus ex Machina – Anthony Cody: Technocratic Groupthink Inflates the Testing Bubble – Lawrence Baines/Rhonda Goolsby-Smith: America’s Obsessive-Assessment Disorder – Julie A. Gorlewski/David A. Gorlewski: Solidarity and Critical Dialogue: Interrupting the Degradation of Teacher Preparation – Morna McDermott: Feeding the World = Reading the World: Let Them Eat Tests – Richard Mora: Bubble in B for Boredom – Brian R. Beabout/Andre M. Perry: Reconciling Student Outcomes and Community Self-Reliance in Modern School Reform Contexts – David L. Bolton/John M. Elmore: The Role of Assessment in Empowering/Disempowering Students in the Critical Pedagogy Classroom – Christian Z. Goering: «How Long Does This Have to Be?»: Confronting the Standardization of Writing Instruction with Teachers in National Writing Project Invitational Summer Institutes – Joe Bower: Telling Time with a Broken Clock: Moving Beyond Standardized Testing – John L. Hoben: The Grading Mousetrap: Narcissism, Abjection, and the Politics of Self-Harm – Arnold Dodge/Ruth Powers Silverberg/Katie Zahedi: Leadership Denied: Principal as Compliance Officer – Hadley J. Ferguson: Journey into Ungrading – Jennifer Magee/Mark Dziedzic: An Oath to Stop Degrading Students: A Story of De-grading an Elementary Classroom – P. L. Thomas: De-grading Writing Instruction: Closing the «Considerable Gap» – Brian Rhode: One Week, Many Thoughts – Lisa William-White: Striving Toward Authentic Teaching for Social Justice: Additional Considerations – P. L. Thomas: Yes, to Be Clear, I Am Anti-testing, Anti-grading.

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Book SynopsisTroubling Method seeks to extract narrative inquiry from method. The shift to a post-humanist, post-qualitative moment is not just another stage in modernism that seeks to improve knowledge production, but is a shift to understanding research as an ontology, a way of being in the world, rather than a mode of production. Fundamental assumptions of research: method, data, analysis, and findings are deconstructed and reconfigured as a mode of relational intra-action.Troubling Method is constructed as a dialogue between the three authors, focusing on their work as qualitative, narrative researchers. The authors revisit six previously published works in which they grapple with the contradictions and ironies of engaging in pragmatist, critical, and feminist qualitative research. After a lengthy introduction which problematizes method, the book is divided into three sections, each with two chapters that are bracketed by an introduction to the issues discussed in the Trade Review“«Troubling Method» nuances narrative research, provokes our thoughts on concepts taken for granted, and invites us all to rethink ways of inquiring from a relational perspective. The vulnerability and transparency the authors share through reflections on doing narrative research over several decades is refreshing and appreciated. Dialogue interludes in each section and the individually authored chapters shed light on the politics of doing inquiry and being a researcher. Whether you are new to narrative approaches or have been doing narrative research for years, this book will make you pause and then compel you to imagine doing inquiry differently.” Candace R. Kuby, Ph.D., Associate Professor in Learning, Teaching, and Curriculum, University of Missouri"«Troubling Method» is an insightful text about how to think about qualitative inquiry without a method. This book addresses the complexities associated with post-moves and it speaks to the dilemmas one might encounter when leaving methods behind. Instead of technical and conventional discourse, the authors approach narratives as ethical engagements in the world speaking to responsibility, race, gender, technology, spirituality, unthought and the ways we live in complex ecological and relational systems. This book challenges readers to redo their narrative methods and think about narratives differently." Mirka Koro-Ljungberg, Professor of Qualitative Research, Mary Lou Fulton Teachers College, Arizona State UniversityTable of ContentsPrologue – Acknowledgments – Introduction: Getting in Trouble – Section I: Relationships as Being in the World – Paul William Eaton: Introduction to Section I –Petra Munro Hendry: The Future of Narrative – Roland W. Mitchell: Narrative Inquiry: Stories Lived, Stories Told – Dialogue Interlude 1 – Section II: Listening as Being in the World – Paul William Eaton: Introduction to Section II – Roland W. Mitchell: "Soft Ears" and Hard Topics: Race, Disciplinarity, and Voice in Higher Education – Petra Munro Hendry: Continuing Dilemmas of Life History Research: A Reflexive Account of Feminist Qualitative Inquiry – Dialogue Interlude 2 – Section III: Unknowing as Being in the World – Paul William Eaton: Introduction to Section III – Petra Munro Hendry: Narrative as Inquiry – Becky Atkinson and Roland W. Mitchell "Why Didn’t They Get It?" "Did They Have to Get It?": What Reader Response Theory Has to Offer Narrative Research and Pedagogy – Dialogue Interlude 3 – Un-Conclusion: Entangling Narrative – Index.

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Book SynopsisTroubling Method seeks to extract narrative inquiry from method. The shift to a post-humanist, post-qualitative moment is not just another stage in modernism that seeks to improve knowledge production, but is a shift to understanding research as an ontology, a way of being in the world, rather than a mode of production. Fundamental assumptions of research: method, data, analysis, and findings are deconstructed and reconfigured as a mode of relational intra-action.Troubling Method is constructed as a dialogue between the three authors, focusing on their work as qualitative, narrative researchers. The authors revisit six previously published works in which they grapple with the contradictions and ironies of engaging in pragmatist, critical, and feminist qualitative research. After a lengthy introduction which problematizes method, the book is divided into three sections, each with two chapters that are bracketed by an introduction to the issues discussed in the Trade Review“«Troubling Method» nuances narrative research, provokes our thoughts on concepts taken for granted, and invites us all to rethink ways of inquiring from a relational perspective. The vulnerability and transparency the authors share through reflections on doing narrative research over several decades is refreshing and appreciated. Dialogue interludes in each section and the individually authored chapters shed light on the politics of doing inquiry and being a researcher. Whether you are new to narrative approaches or have been doing narrative research for years, this book will make you pause and then compel you to imagine doing inquiry differently.” Candace R. Kuby, Ph.D., Associate Professor in Learning, Teaching, and Curriculum, University of Missouri"«Troubling Method» is an insightful text about how to think about qualitative inquiry without a method. This book addresses the complexities associated with post-moves and it speaks to the dilemmas one might encounter when leaving methods behind. Instead of technical and conventional discourse, the authors approach narratives as ethical engagements in the world speaking to responsibility, race, gender, technology, spirituality, unthought and the ways we live in complex ecological and relational systems. This book challenges readers to redo their narrative methods and think about narratives differently." Mirka Koro-Ljungberg, Professor of Qualitative Research, Mary Lou Fulton Teachers College, Arizona State UniversityTable of ContentsPrologue – Acknowledgments – Introduction: Getting in Trouble – Section I: Relationships as Being in the World – Paul William Eaton: Introduction to Section I –Petra Munro Hendry: The Future of Narrative – Roland W. Mitchell: Narrative Inquiry: Stories Lived, Stories Told – Dialogue Interlude 1 – Section II: Listening as Being in the World – Paul William Eaton: Introduction to Section II – Roland W. Mitchell: "Soft Ears" and Hard Topics: Race, Disciplinarity, and Voice in Higher Education – Petra Munro Hendry: Continuing Dilemmas of Life History Research: A Reflexive Account of Feminist Qualitative Inquiry – Dialogue Interlude 2 – Section III: Unknowing as Being in the World – Paul William Eaton: Introduction to Section III – Petra Munro Hendry: Narrative as Inquiry – Becky Atkinson and Roland W. Mitchell "Why Didn’t They Get It?" "Did They Have to Get It?": What Reader Response Theory Has to Offer Narrative Research and Pedagogy – Dialogue Interlude 3 – Un-Conclusion: Entangling Narrative – Index.

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Book SynopsisNew media has brought constant evolution to professional journalism practices and news genres. Online news practices challenge the occupational jurisdiction of journalism with a multiplicity of conflicting and competing journalistic ideals. In order to prepare journalism students to meet the demands of online journalism today, journalism schools have developed courses that emphasize journalistic practice on online news platforms and tools, such as Twitter, WordPress.com, Soundslides Plus, etc.Drawing on the theoretical lens of digital literacies, Multimedia News Storytelling as Digital Literacies problematizes the emphasis on transmission of certain professional values and news formats without raising students' critical awareness that there can be diversity of values. Methodologically, the present study proposes a genre-aware, semiotic-aware, critical framework that aims at analyzing digital literacies required and practiced by online journalists. It simultaneously encTable of ContentsList of Tables – List of Figures – Acknowledgments – Introduction – Literature Review – Theoretical Framework – Methodology – Analysis of Stated Course Curriculum from the Perspective of Outcomes-Based Education – Analyzing the Teaching and Learning of Audio Slideshows – Evaluating the Teaching and Learning of Multimedia Packages – Conclusion – Appendices.

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Book SynopsisJust Plain Data Analysis teaches students statistical literacy skills that they can use to evaluate and construct arguments about public affairs issues grounded in numerical evidence. The book addresses skills that are often not taught in introductory social science research methods courses and that are often covered sketchily in the research methods textbooks: where to find commonly used measures of political and social conditions; how to assess the reliability and validity of specific indicators; how to present data efficiently in charts and tables; how to avoid common misinterpretations and misrepresentations of data; and how to evaluate causal arguments based on numerical data. With a new chapter on statistical fallacies and updates throughout the text, the new edition teaches students how to find, interpret, and present commonly used social indicators in an even clearer and more practical way. Trade ReviewThis short book is a useful supplement to traditional statistics and research method texts....Recommended. * CHOICE *In Just Plain Data Analysis, Gary Klass analyzes simple statistics that involve sophisticated reasoning. This book cuts through paradoxes, fallacies and socially-constructed statistics to uncover the basic elements of data analysis. A must-read for anyone interested in statistical literacy. -- Milo Schield, director of the W. M. Keck Statistical Literacy Project, Augsburg CollegeAs a teacher of research methods, I have been waiting for a book like Just Plain Data Analysis. By focusing on finding, presenting and interpreting data, Klass encourages students to develop the critical thinking skills that they will need once they leave the university. The writing is clear and the examples are excellent. The discussion of reliability, validity, and ecological fallacy is the best I have read in an undergraduate text. The many table and chart examples will help students improve their skills. -- Bill Wilkerson, College at Oneonta, SUNYWith humor and political balance, Just Plain Data Analysis offers a pithy guide to finding, presenting and interpreting social science data ranging from crime to elections. Recommended for students, teachers and policymakers who want to understand where the data comes from and how to use it responsibly. -- Mark Maier, author of The Data Game: Controversies in Social Science StatisticsJust Plain Data Analysis: Finding, Presenting, and Interpreting Social Science by Gary M. Klass is an exploration of the types of quantitative research (which is rooted in data and statistical analysis) that can be used to draw conclusions about such social science issues as crime rates and measuring educational achievement. Klass uses examples of statistical claims to demonstrate how changing the time frame for data collection or looking at different correlations can result in varying or misleading statements. He also has chapters to how to tabulate and display numbers and how to use graphical presentation effectively. * American Libraries *Table of ContentsPreface Chapter 1: Measuring Political, Social and Economic Conditions Chapter 2: Measuring Racial and Ethnic Inequality Chapter 3: Statistical Fallacies, Paradoxes and Threats to Validity Chapter 4: Examining a Relationship: New York City Crime Rates Chapter 5: Tabulating the Data and Writing about the Numbers Chapter 6: The Graphical Display of Data Chapter 7: Voting and Elections Chapter 8: Measuring Educational Achievement Chapter 9: Measuring Poverty and Inequality

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Book SynopsisJust Plain Data Analysis teaches students statistical literacy skills that they can use to evaluate and construct arguments about public affairs issues grounded in numerical evidence. The book addresses skills that are often not taught in introductory social science research methods courses and that are often covered sketchily in the research methods textbooks: where to find commonly used measures of political and social conditions; how to assess the reliability and validity of specific indicators; how to present data efficiently in charts and tables; how to avoid common misinterpretations and misrepresentations of data; and how to evaluate causal arguments based on numerical data. With a new chapter on statistical fallacies and updates throughout the text, the new edition teaches students how to find, interpret, and present commonly used social indicators in an even clearer and more practical way. Trade ReviewThis short book is a useful supplement to traditional statistics and research method texts....Recommended. * CHOICE *In Just Plain Data Analysis, Gary Klass analyzes simple statistics that involve sophisticated reasoning. This book cuts through paradoxes, fallacies and socially-constructed statistics to uncover the basic elements of data analysis. A must-read for anyone interested in statistical literacy. -- Milo Schield, director of the W. M. Keck Statistical Literacy Project, Augsburg CollegeAs a teacher of research methods, I have been waiting for a book like Just Plain Data Analysis. By focusing on finding, presenting and interpreting data, Klass encourages students to develop the critical thinking skills that they will need once they leave the university. The writing is clear and the examples are excellent. The discussion of reliability, validity, and ecological fallacy is the best I have read in an undergraduate text. The many table and chart examples will help students improve their skills. -- Bill Wilkerson, College at Oneonta, SUNYWith humor and political balance, Just Plain Data Analysis offers a pithy guide to finding, presenting and interpreting social science data ranging from crime to elections. Recommended for students, teachers and policymakers who want to understand where the data comes from and how to use it responsibly. -- Mark Maier, author of The Data Game: Controversies in Social Science StatisticsJust Plain Data Analysis: Finding, Presenting, and Interpreting Social Science by Gary M. Klass is an exploration of the types of quantitative research (which is rooted in data and statistical analysis) that can be used to draw conclusions about such social science issues as crime rates and measuring educational achievement. Klass uses examples of statistical claims to demonstrate how changing the time frame for data collection or looking at different correlations can result in varying or misleading statements. He also has chapters to how to tabulate and display numbers and how to use graphical presentation effectively. * American Libraries *Table of ContentsPreface Chapter 1: Measuring Political, Social and Economic Conditions Chapter 2: Measuring Racial and Ethnic Inequality Chapter 3: Statistical Fallacies, Paradoxes and Threats to Validity Chapter 4: Examining a Relationship: New York City Crime Rates Chapter 5: Tabulating the Data and Writing about the Numbers Chapter 6: The Graphical Display of Data Chapter 7: Voting and Elections Chapter 8: Measuring Educational Achievement Chapter 9: Measuring Poverty and Inequality

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Book Synopsis Recent advances in baseball statistical analysis have made it possible to assess the totality of contribution each player makes to team success or failure. Using the metric Wins Above Average (WAA)--the number of wins that the 2016 Red Sox, for example, added because they had Mookie Betts in right field, instead of an average player--the author undertakes a fascinating review of major league baseball from 1901 through 2017. The great teams are analyzed, underscoring why they were successful. The great players of each generation are identified using simple, reliable metrics--from Ty Cobb through Mike Trout, and pitchers from Christy Mathewson to Clayton Kershaw. Surprises abound. The importance of pitching is found to be vastly exaggerated. Many Hall of Fame pitchers (and some hitters) achieved immortality almost entirely on the backs of their teammates, while a few over-qualified players still await induction. Focusing on today''s rosters, the WAA assessment shows that t

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Book SynopsisThis isthedefinitive guide to doing constructivist grounded theory.From gathering rich data and conducting interviews, to undertaking coding and writing up your study, this down-to-earth book guides you through all the steps you need to do grounded theory research.This revised third edition: Showcases 9 new case studies of grounded theory research in action from scholars across the globe, including Australia, Canada, Japan and the United States. Enables you to see, at a glance, how each chapter will develop your understanding with new learning objectives. Supports you to expand your knowledge with new further reading suggestions in every chapter. Retaining Kathy Charmaz's characteristic warm and accessible style, this book is essential reading for anyone - undergraduate, postgraduate or researcher - looking to understand and do grounded theory research.

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Book SynopsisYou can′t get anywhere in your statistics course without grasping statistical significance. it′s often seen as difficult but is actually a straightforward concept everyone can—and should—understand. Do your results mean something—or not? How can you measure it? Breaking it down into three building blocks, this Little Quick Fix shows students how to master: hypothesis testing normal distribution p values Students will learn how to understand the concept and also how to explain it for maximum effect in their essays and lab reports. Good for results—this is also a secret weapon for critical thinking. Little Quick Fix titles provide quick but authoritative answers to the problems, hurdles, and assessment points students face in the research course, project proposal, or design—whatever their methods learning is. Lively, ultra-modern design; full-colour, each page a tailored design. An hour′s read. Easy to dip in and out of with clear navigation enables the reader to find what she needs—quick. Direct written style gets to the point with clear language. Nothing needs to be read twice. No fluff. Learning is reinforced through a 2-minute overview summary; 3-second summaries with super-quick Q&A DIY tasks create a work plan to accomplish a task, do a self-check quiz, solve a problem, get students to what they need to show their supervisor. Checkpoints in each section make sure students are nailing it as they go and support self-directed learning. How do I know I’m done? Each Little Quick Fix wraps up with a final checklist that allows the reader to self-assess they’ve got what they need to progress, submit, or ace the test or task. Table of ContentsSection 1: What is statistical significance and inference? Section 2: Why do I have to draw a random sample? Section 3: What is a normal distribution? Section 4: What is a standard error? Section 5: How do I calculate confidence intervals with standard errors? Section 6: What is a p-value? Section 7: What does a significant p-value actually mean?

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Book SynopsisThis book presents the field of sports statistics to two very distinct target audiences, namely academicians, in order to raise their interest in this growing field, and, on the other hand, sports fans, who, even without advanced mathematical knowledge, will be able to understand the data analysis and gain new insights into their favourite sports. The book thus offers a unique perspective on this attractive topic by combining sports analytics, data visualisation and advanced statistical procedures to extract new findings from sports data such as improved rankings or prediction methods. Bringing together insights from football, tennis, basketball, track and field, and baseball, the book will appeal to aficionados of any sport, and, thanks to its cutting-edge data analysis tools, will provide the reader with completely new insights into their favourite sport in an engaging and user-friendly way.

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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.

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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.

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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.

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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.

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Book SynopsisIn the digital age, data is the new currency. However, amassing heaps of data means nothing if itdoesn't lead to actionable insights. It's not enough to just present numbers; to truly resonate withan audience, data needs a narrative. "Data Storytelling and Translation" bridges the chasm between numbers and narratives. Learn the intricacies of translating raw data into compelling stories that captivate, inform, and inspire action. The book covers proven frameworks for converting data into compelling narratives, strategies to tailor data stories to different audiences, techniques to avoid common pitfalls and biases in data representation, the balance between aesthetics and accuracy in data visualization, and uses real-world case studies illustrating the power of effective data storytelling. Whether you're a data scientist, business analyst, student or a decision-maker, this book offers the tools to articulate the true value of your data.Table of Contents 1: The Age of the Data Translator 2: All Decisions Start with People 3: Start with Good Questions and Great Listening 4: Being Fluent in the Language of Data 5: Identify, Understand, and Frame Problems 6: Simplifying Insights Through Metrics and Objectives 7: Painting Your Data Story 8: Leveraging Visuals to Share Insights and Compel Action 9: Leveraging Dashboards in Your Communication 10: Communicating Your Data Story Epilogue Index

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Book SynopsisPython 3 and Data Visualization offers readers a deep dive into the world of Python 3 programming and the art of data visualization. Chapter 1 introduces the essentials of Python, covering a vast array of topics from basic data types, loops, and functions to more advanced constructs like dictionaries, sets, and matrices. In Chapter 2, the focus shifts to NumPy and its powerful array operations, seamlessly leading into the world of data visualization using prominent libraries such as Matplotlib. Chapter 6 immerses the reader in Seaborn's rich visualization tools, offering insights into datasets like Iris and Titanic. The appendix covers other visualization tools and techniques, including SVG graphics, D3 for dynamic visualizations, and more. The book also includes companion files with numerous Python code samples and figures. From foundational Python concepts to the intricacies of data visualization, this book serves as a comprehensive resource for both beginners and seasoned professionals.FEATURES: Covers numerous tools for mastering visualization including NumPy, Pandas, SQL, Matplotlib, and Seaborn Includes an introductory chapter on Python 3 basics Features companion files with numerous Python code samples and figures Table of Contents 1: Introduction to Python 3 2: NumPyand Data Visualization 3: Pandas and Data Visualization 4: Pandas and SQL 5:Matplotlib for Data Visualization 6: Seaborn for Data Visualization Appendix:SVG and D3

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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

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Book SynopsisThe growing importance of data science, and the increasing role of information professionals in the management and use of data, are brought together in Practical Data Science for Information Professionals to provide a practical introduction specifically designed for information professionals.Data science has a wide range of applications within the information profession, from working alongside researchers in the discovery of new knowledge, to the application of business analytics for the smoother running of a library or library services. Practical Data Science for Information Professionals provides an accessible introduction to data science, using detailed examples and analysis on real data sets to explore the basics of the subject.This book will be of interest to all types of libraries around the world, from large academic libraries to small research libraries. By focusing on the application of open source software, the book aims to reduce barriers for readers to use the lessons learned within.Trade Review'If libraries and librarians are to be serious about the ‘I’ in LIS, then analysing data to find meaning for our customers will be a core component of the service offering. David Stuart’s book is an excellent entry point to the discipline.' -- Ian McCallum * Journal of the Australian Library and Information Association *Table of Contents1. What is an Ontology? 2. Ontologies and the Semantic Web 3. Existing Ontologies 4. Adopting Ontologies 5. Building Ontologies 6. Interrogating Ontologies 7. The Future of Ontologies and the Information Professional

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Book SynopsisPractical Data Science for Information Professionals provides an accessible introduction to a potentially complex field, providing readers with an overview of data science and a framework for its application. It provides detailed examples and analysis on real data sets to explore the basics of the subject in three principle areas: clustering and social network analysis; predictions and forecasts; and text analysis and mining.As well as highlighting a wealth of user-friendly data science tools, the book also includes some example code in two of the most popular programming languages (R and Python) to demonstrate the ease with which the information professional can move beyond the graphical user interface and achieve significant analysis with just a few lines of code. After reading, readers will understand:· the growing importance of data science · the role of the information professional in data science · some of the most important tools and methods that information professionals can use.Bringing together the growing importance of data science and the increasing role of information professionals in the management and use of data, Practical Data Science for Information Professionals will provide a practical introduction to the topic specifically designed for the information community. It will appeal to librarians and information professionals all around the world, from large academic libraries to small research libraries. By focusing on the application of open source software, it aims to reduce barriers for readers to use the lessons learned within.Trade Review'If libraries and librarians are to be serious about the ‘I’ in LIS, then analysing data to find meaning for our customers will be a core component of the service offering. David Stuart’s book is an excellent entry point to the discipline.' -- Ian McCallum * Journal of the Australian Library and Information Association *Table of ContentsContentsFigures Tables Boxes Preface 1 What is data science? Data, information, knowledge, wisdom Data everywhere The data deserts Data science The potential of data science From research data services to data science in libraries Programming in libraries Programming in this book The structure of this book 2 Little data, big data Big data Data formats Standalone files Application programming interfaces Unstructured data Data sources Data licences 3 The process of data science Modelling the data science process Frame the problem Collect data Transform and clean data Analyse data Visualise and communicate data Frame a new problem 4 Tools for data analysis Finding tools Software for data science Programming for data science 5 Clustering and social network analysis Network graphs Graph terminology Network matrix Visualisation Network analysis 6 Predictions and forecasts Predictions and forecasts beyond data science Predictions in a world of (limited) data Predicting and forecasting for information professionals Statistical methodologies 7 Text analysis and mining Text analysis and mining, and information professionals Natural language processing Keywords and n-grams 8 The future of data science and information professionalsEight challenges to data scienceTen steps to data science librarianship The final word: playReferences Appendix – Programming concepts for data science Variables, data types and other classes Import libraries Functions and methods Loops and conditionals Final words of advice Further reading Index

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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

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Book SynopsisA comprehensive guide to everything scientists need to know about data management, this book is essential for researchers who need to learn how to organize, document and take care of their own data. Researchers in all disciplines are faced with the challenge of managing the growing amounts of digital data that are the foundation of their research. Kristin Briney offers practical advice and clearly explains policies and principles, in an accessible and in-depth text that will allow researchers to understand and achieve the goal of better research data management. Data Management for Researchers includes sections on: * The data problem – an introduction to the growing importance and challenges of using digital data in research. Covers both the inherent problems with managing digital information, as well as how the research landscape is changing to give more value to research datasets and code. * The data lifecycle – a framework for data’s place within the research process and how data’s role is changing. Greater emphasis on data sharing and data reuse will not only change the way we conduct research but also how we manage research data. * Planning for data management – covers the many aspects of data management and how to put them together in a data management plan. This section also includes sample data management plans. * Documenting your data – an often overlooked part of the data management process, but one that is critical to good management; data without documentation are frequently unusable. * Organizing your data – explains how to keep your data in order using organizational systems and file naming conventions. This section also covers using a database to organize and analyze content. * Improving data analysis – covers managing information through the analysis process. This section starts by comparing the management of raw and analyzed data and then describes ways to make analysis easier, such as spreadsheet best practices. It also examines practices for research code, including version control systems. * Managing secure and private data – many researchers are dealing with data that require extra security. This section outlines what data falls into this category and some of the policies that apply, before addressing the best practices for keeping data secure. * Short-term storage – deals with the practical matters of storage and backup and covers the many options available. This section also goes through the best practices to insure that data are not lost. * Preserving and archiving your data – digital data can have a long life if properly cared for. This section covers managing data in the long term including choosing good file formats and media, as well as determining who will manage the data after the end of the project. * Sharing/publishing your data – addresses how to make data sharing across research groups easier, as well as how and why to publicly share data. This section covers intellectual property and licenses for datasets, before ending with the altmetrics that measure the impact of publicly shared data. * Reusing data – as more data are shared, it becomes possible to use outside data in your research. This chapter discusses strategies for finding datasets and lays out how to cite data once you have found it. This book is designed for active scientific researchers but it is useful for anyone who wants to get more from their data: academics, educators, professionals or anyone who teaches data management, sharing and preservation. "An excellent practical treatise on the art and practice of data management, this book is essential to any researcher, regardless of subject or discipline." —Robert Buntrock, Chemical Information BulletinTrade ReviewApparently, NASA lost much of the early data from space exploration, including high quality video footage of the first moon landing. All the more reason to do as it says in the sub-title to the book. -- Alan Crowden * BES Bulletin *For researchers and consumers of data who are often fraught with managing excess information, Briney's book offers valuable techniques, strategies and standards to help achieve proficient data management and successful outcomes. This book can be useful to both novice researchers and well-established scientists alike. -- Mary F. Miles * Medical Reference Services Quarterly *... recommended as a textbook for graduate-level research techniques courses. It's an important resource for academic and special library shelves and a vital reference for anyone working with data. -- Kristen LaBonte * Issues in Science and Technology Librarianship *Briney has written a useful primer on data management for researchers which provides practical advice throughout on managing data. It is easy to read and clearly structured. http://www.ariadne.ac.uk/issue75/cole -- Gareth Cole, Loughborough University Library * Ariadne *Kristin Briney’s Data Management for Researchers is a book that should be on the shelf (physical or virtual) of every librarian, researcher and research administrator. Scientists, engineers, social scientists, humanists — anyone who’s work involves generating and keeping track of digital data. This is the book for you. .... I recommend this book without hesitation for all academic libraries. Individual researchers, research administrators, funding agency employees and academic librarians would all find much useful information. Simply giving a copy to new graduate students is probably a worthwhile investment at any institution. http://scienceblogs.com/confessions/2016/01/11/reading-diary-data-management-for-researchers-organize-maintain-and-share-your-data-for-research-success-by-kristin-briney/ -- John Dupuis, York University Library, Toronto * ScienceBlogs *Briney takes the reader through a pragmatic and sensible route through the activities of data management. -- David Bawden, AlexandriaThis practical handbook can help bring new researchers quickly up-to-speed on the topic, as well as serve as a reference to meet specific data management needs they encounter throughout the data life cycle. * Enid Karr, Journal of eScience Librarianship *I cannot recommend this slender, seemingly innocent looking book enough – it will literally change how you think about data management. -- CBatt[I] intend to give a copy of this book to each graduate student / trainee that joins my lab -- Christie Bahlai, Practical Data Management for Bug Counters

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