Advertisement
| case study data analysis: Case Studies in Neural Data Analysis Mark A. Kramer, Uri T. Eden, 2016-11-04 A practical guide to neural data analysis techniques that presents sample datasets and hands-on methods for analyzing the data. As neural data becomes increasingly complex, neuroscientists now require skills in computer programming, statistics, and data analysis. This book teaches practical neural data analysis techniques by presenting example datasets and developing techniques and tools for analyzing them. Each chapter begins with a specific example of neural data, which motivates mathematical and statistical analysis methods that are then applied to the data. This practical, hands-on approach is unique among data analysis textbooks and guides, and equips the reader with the tools necessary for real-world neural data analysis. The book begins with an introduction to MATLAB, the most common programming platform in neuroscience, which is used in the book. (Readers familiar with MATLAB can skip this chapter and might decide to focus on data type or method type.) The book goes on to cover neural field data and spike train data, spectral analysis, generalized linear models, coherence, and cross-frequency coupling. Each chapter offers a stand-alone case study that can be used separately as part of a targeted investigation. The book includes some mathematical discussion but does not focus on mathematical or statistical theory, emphasizing the practical instead. References are included for readers who want to explore the theoretical more deeply. The data and accompanying MATLAB code are freely available on the authors' website. The book can be used for upper-level undergraduate or graduate courses or as a professional reference. A version of this textbook with all of the examples in Python is available on the MIT Press website. |
| case study data analysis: Humanities Data Analysis Folgert Karsdorp, Mike Kestemont, Allen Riddell, 2021-01-12 A practical guide to data-intensive humanities research using the Python programming language The use of quantitative methods in the humanities and related social sciences has increased considerably in recent years, allowing researchers to discover patterns in a vast range of source materials. Despite this growth, there are few resources addressed to students and scholars who wish to take advantage of these powerful tools. Humanities Data Analysis offers the first intermediate-level guide to quantitative data analysis for humanities students and scholars using the Python programming language. This practical textbook, which assumes a basic knowledge of Python, teaches readers the necessary skills for conducting humanities research in the rapidly developing digital environment. The book begins with an overview of the place of data science in the humanities, and proceeds to cover data carpentry: the essential techniques for gathering, cleaning, representing, and transforming textual and tabular data. Then, drawing from real-world, publicly available data sets that cover a variety of scholarly domains, the book delves into detailed case studies. Focusing on textual data analysis, the authors explore such diverse topics as network analysis, genre theory, onomastics, literacy, author attribution, mapping, stylometry, topic modeling, and time series analysis. Exercises and resources for further reading are provided at the end of each chapter. An ideal resource for humanities students and scholars aiming to take their Python skills to the next level, Humanities Data Analysis illustrates the benefits that quantitative methods can bring to complex research questions. Appropriate for advanced undergraduates, graduate students, and scholars with a basic knowledge of Python Applicable to many humanities disciplines, including history, literature, and sociology Offers real-world case studies using publicly available data sets Provides exercises at the end of each chapter for students to test acquired skills Emphasizes visual storytelling via data visualizations |
| case study data analysis: Data Analysis for Business, Economics, and Policy Gábor Békés, Gábor Kézdi, 2021-05-06 A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data. |
| case study data analysis: Applied Functional Data Analysis J.O. Ramsay, B.W. Silverman, 2007-11-23 This book contains the ideas of functional data analysis by a number of case studies. The case studies are accessible to research workers in a wide range of disciplines. Every reader should gain not only a specific understanding of the methods of functional data analysis, but more importantly a general insight into the underlying patterns of thought. There is an associated web site with MATLABr and S?PLUSr implementations of the methods discussed. |
| case study data analysis: Case Studies in Data Analysis Jane F. Gentleman, G.A. Whitmore, 2012-12-06 This volume is a collection of eight Case Studies in Data Analysis that appeared in various issues of the Canadian Journal of Statistics (OS) over a twelve year period from 1982 to 1993. One follow-up article to Case Study No.4 is also included in the volume. The OS's Section on Case Studies in Data Analysis was initiated by a former editor who wanted to increase the analytical content of the journal. We were asked to become Section Co-Editors and to develop a format for the case studies. Each case study presents analyses of a real data set by two or more analysts or teams of analysts working independently in a simulated consulting context. The section aimed at demonstrating the process of statistical analysis and the possible diversity of approaches and conclusions. For each case study, the Co-Editors found a set of real Canadian data, posed what they thought was an interesting statistical problem, and recruited analysts working in Canada who were willing to tackle it. The published case studies describe the data and the problem, and present and discuss the analysts' solutions. For some case studies, the providers of the data were invited to contribute their own analysis. |
| case study data analysis: Multiple Case Study Analysis Robert E. Stake, 2013-04-29 Examining situational complexity is a vital part of social and behavioral science research. This engaging text provides an effective process for studying multiple cases--such as sets of teachers, staff development sessions, or clinics operating in different locations--within one complex program. The process also can be used to investigate broadly occurring phenomena without programmatic links, such as leadership or sibling rivalry. Readers learn to design, analyze, and report studies that balance common issues across the group of cases with the unique features and context of each case. Three actual case reports from a transnational early childhood program illustrate the author's approach, and helpful reproducible worksheets facilitate multicase recording and analysis. |
| case study data analysis: Case Studies in Neural Data Analysis Mark A. Kramer, Uri T. Eden, 2016-10-28 A practical guide to neural data analysis techniques that presents sample datasets and hands-on methods for analyzing the data. As neural data becomes increasingly complex, neuroscientists now require skills in computer programming, statistics, and data analysis. This book teaches practical neural data analysis techniques by presenting example datasets and developing techniques and tools for analyzing them. Each chapter begins with a specific example of neural data, which motivates mathematical and statistical analysis methods that are then applied to the data. This practical, hands-on approach is unique among data analysis textbooks and guides, and equips the reader with the tools necessary for real-world neural data analysis. The book begins with an introduction to MATLAB, the most common programming platform in neuroscience, which is used in the book. (Readers familiar with MATLAB can skip this chapter and might decide to focus on data type or method type.) The book goes on to cover neural field data and spike train data, spectral analysis, generalized linear models, coherence, and cross-frequency coupling. Each chapter offers a stand-alone case study that can be used separately as part of a targeted investigation. The book includes some mathematical discussion but does not focus on mathematical or statistical theory, emphasizing the practical instead. References are included for readers who want to explore the theoretical more deeply. The data and accompanying MATLAB code are freely available on the authors' website. The book can be used for upper-level undergraduate or graduate courses or as a professional reference. |
| case study data analysis: Conceptual Analyses of Curriculum Inquiry Methodologies Watson, Sandy White, Austin, Stacie, Bell, Jeremy, 2021-12-03 The field of curriculum inquiry has grown rapidly over the last four decades resulting in many new forms of curriculum inquiry to be used as tools to answer unique curriculum-related research questions. There are few texts available that include concise descriptions and elements of curriculum inquiry methodologies and directed at enabling researchers to wisely choose a form of curriculum inquiry most appropriate for their study. Conceptual Analyses of Curriculum Inquiry Methodologies presents chapters that are each devoted to a particular form of inquiry, with a conceptual analysis of the methodology, its purpose(s), its utilization, structure, and organization, all written by scholars with firsthand experience with the form of inquiry. These experts also take the liberty of citing examples of published studies that have utilized the methodology, share the types of relevant data collection instruments and forms of data produced, and also share research questions that can be answered via their form of inquiry. Covering topics such as quantitative methods of inquiry, glocalization, and educational criticism, this is an essential text for curriculum designers, doctoral students, doctoral researchers, university faculty, professors, researchers, and academicians. |
| case study data analysis: Analysis of Neural Data Robert E. Kass, Uri T. Eden, Emery N. Brown, 2014-07-08 Continual improvements in data collection and processing have had a huge impact on brain research, producing data sets that are often large and complicated. By emphasizing a few fundamental principles, and a handful of ubiquitous techniques, Analysis of Neural Data provides a unified treatment of analytical methods that have become essential for contemporary researchers. Throughout the book ideas are illustrated with more than 100 examples drawn from the literature, ranging from electrophysiology, to neuroimaging, to behavior. By demonstrating the commonality among various statistical approaches the authors provide the crucial tools for gaining knowledge from diverse types of data. Aimed at experimentalists with only high-school level mathematics, as well as computationally-oriented neuroscientists who have limited familiarity with statistics, Analysis of Neural Data serves as both a self-contained introduction and a reference work. |
| case study data analysis: Encyclopedia of Case Study Research: L - Z Albert J. Mills, Gabrielle Durepos, Elden Wiebe, 2010 |
| case study data analysis: Fundamentals of Machine Learning for Predictive Data Analytics, second edition John D. Kelleher, Brian Mac Namee, Aoife D'Arcy, 2020-10-20 The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning. |
| case study data analysis: Case Study Research John Gerring, 2016-12-24 Case Study Research: Principles and Practices provides a general understanding of the case study method as well as specific tools for its successful implementation. These tools are applicable in a variety of fields including anthropology, business and management, communications, economics, education, medicine, political science, psychology, social work, and sociology. Topics include: a survey of case study approaches; a methodologically tractable definition of 'case study'; strategies for case selection, including random sampling and other algorithmic approaches; quantitative and qualitative modes of case study analysis; and problems of internal and external validity. The second edition of this core textbook is designed to be accessible to readers who are new to the subject and is thoroughly revised and updated, incorporating recent research, numerous up-to-date studies and comprehensive lecture slides. |
| case study data analysis: Data Science For Dummies Lillian Pierson, 2021-08-20 Monetize your company’s data and data science expertise without spending a fortune on hiring independent strategy consultants to help What if there was one simple, clear process for ensuring that all your company’s data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that’s most prime for achieving profitability while also moving your company closer to its business vision? There is. Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework – A simple, proven process for leading profit-forming data science projects. Not sure what data science is yet? Don’t worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you’re already a data science expert? Then you really won’t want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book. Data Science For Dummies demonstrates: The only process you’ll ever need to lead profitable data science projects Secret, reverse-engineered data monetization tactics that no one’s talking about The shocking truth about how simple natural language processing can be How to beat the crowd of data professionals by cultivating your own unique blend of data science expertise Whether you’re new to the data science field or already a decade in, you’re sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company’s data by picking up your copy today. |
| case study data analysis: Case Study Research and Applications Robert K. Yin, 2017-09-27 Winner of the 2019 McGuffey Longevity Award from the Textbook & Academic Authors Association (TAA) Recognized as one of the most cited methodology books in the social sciences, the Sixth Edition of Robert K. Yin′s bestselling text provides a complete portal to the world of case study research. With the integration of 11 applications in this edition, the book gives readers access to exemplary case studies drawn from a wide variety of academic and applied fields. Ultimately, Case Study Research and Applications will guide students in the successful use and application of the case study research method. |
| case study data analysis: Statistical Case Studies Roxy Peck, Larry D. Haugh, Arnold Goodman, 1998-01-01 This book contains 20 case studies that use actual data sets that have not been simplified for classroom use. |
| case study data analysis: Introduction to Educational Research W. Newton Suter, 2012 W. Newton Suter argues that what is important in a changing education landscape is the ability to think clearly about research methods, reason through complex problems and evaluate published research. He explains how to evaluate data and establish its relevance. |
| case study data analysis: Case Study Research in Software Engineering Per Runeson, Martin Host, Austen Rainer, Bjorn Regnell, 2012-03-07 Based on their own experiences of in-depth case studies of software projects in international corporations, in this book the authors present detailed practical guidelines on the preparation, conduct, design and reporting of case studies of software engineering. This is the first software engineering specific book on the case study research method. |
| case study data analysis: Embedded Case Study Methods Roland W. Scholz, Olaf Tietje, 2002 In an embedded case study, the starting and end point is the comprehension of the case as a whole in its real-world context. This book bridges the gap between quantitative and qualitative approaches to complex problems when using this methodology. |
| case study data analysis: Build a Career in Data Science Emily Robinson, Jacqueline Nolis, 2020-03-24 Summary You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology What are the keys to a data scientist’s long-term success? Blending your technical know-how with the right “soft skills” turns out to be a central ingredient of a rewarding career. About the book Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee. By following clear and simple instructions, you’ll learn to craft an amazing resume and ace your interviews. In this demanding, rapidly changing field, it can be challenging to keep projects on track, adapt to company needs, and manage tricky stakeholders. You’ll love the insights on how to handle expectations, deal with failures, and plan your career path in the stories from seasoned data scientists included in the book. What's inside Creating a portfolio of data science projects Assessing and negotiating an offer Leaving gracefully and moving up the ladder Interviews with professional data scientists About the reader For readers who want to begin or advance a data science career. About the author Emily Robinson is a data scientist at Warby Parker. Jacqueline Nolis is a data science consultant and mentor. Table of Contents: PART 1 - GETTING STARTED WITH DATA SCIENCE 1. What is data science? 2. Data science companies 3. Getting the skills 4. Building a portfolio PART 2 - FINDING YOUR DATA SCIENCE JOB 5. The search: Identifying the right job for you 6. The application: Résumés and cover letters 7. The interview: What to expect and how to handle it 8. The offer: Knowing what to accept PART 3 - SETTLING INTO DATA SCIENCE 9. The first months on the job 10. Making an effective analysis 11. Deploying a model into production 12. Working with stakeholders PART 4 - GROWING IN YOUR DATA SCIENCE ROLE 13. When your data science project fails 14. Joining the data science community 15. Leaving your job gracefully 16. Moving up the ladder |
| case study data analysis: ePub - Case Study Research Dan Remenyi, 2012-01-01 Case study research is not an easy option and this book points out where the challenges are and what needs to be done to become a competent case study researcher. |
| case study data analysis: Case Study Methodology in Higher Education Baron, Annette, McNeal, Kelly, 2019-06-28 In higher education, case studies can be utilized to have students put themselves into problems faced by a protagonist and, by doing so, address academic or career-related issues. Working through these issues provides students with an opportunity to gain applied perspective and experiences. Professors in higher education who choose this method of teaching require navigational tools to ensure that students achieve stated learning objectives. Case Study Methodology in Higher Education is an essential research publication that focuses on the history and theories relating to case study methodology including techniques for writing case studies and utilizing them in university settings to prepare students for real-life career-related scenarios. This publication features a wide range of topics such as educational leadership, case writing, and teacher education. It is essential for educators, career professionals, higher education faculty, researchers, and students. |
| case study data analysis: Case Study Methods in Education Gary Thomas, 2013-06-05 This four-volume major work collates and contextualises key papers on the use of case study in education. The collection brings together methodological publications and publications which provide exemplars of case study. The methodological material provides both critical analyses of case study and those which champion its use, offering at the same time a chronological map of the development of case study from its earliest uses to today. Framed by an introductory passage in each volume which seeks to provide a synopsis of the methodological discussion and a commentary on the exemplary material, this set provides for serious students in education original source material to appraise the method together with exemplary sample material to help them plan and organize their case studies. Volume One: Methodological issues around the use of case studies in social science Volume Two: Methodological issues around the use of case studies in education Volume Three: The case study in practice - general issues and specific examples Volume Four: Examples of case studies in education |
| case study data analysis: Introduction to Data Science Rafael A. Irizarry, 2019-11-20 Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. |
| case study data analysis: Using Software in Qualitative Research Ann Lewins, Christina Silver, 2007-05 Using Software in Qualitative Research is an essential introduction to the practice and principles of Computer Assisted Qualitative Data Analysis (CAQDAS), helping the reader choose the most appropriate package for their needs and to get the most out of the software once they are using it. This step-by-step book considers a wide range of tasks and processes, bringing them together to demystify qualitative software and encourage flexible and critical choices and uses of software in supporting analysis. The book can be read as a whole or by chapters, building on one another to provide a holistic sense of the analytic journey without advocating a particular sequential process. Accessible and comprehensive, Using Software in Qualitative Research provides a practical but analytically-grounded guide to thinking about and using software and will be an essential companion for any qualitative researcher. |
| case study data analysis: Research Basics James V. Spickard, 2016-09-15 Research Basics: Design to Data Analysis in Six Steps offers a fresh and creative approach to the research process based on author James V. Spickard’s decades of teaching experience. Using an intuitive six-step model, readers learn how to craft a research question and then identify a logical process for answering it. Conversational writing and multi-disciplinary examples illuminate the model’s simplicity and power, effectively connecting the “hows” and “whys” behind social science research. Students using this book will learn how to turn their research questions into results. |
| case study data analysis: Secondary Analysis of Electronic Health Records MIT Critical Data, 2016-09-09 This book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care. It formulates a more complete lexicon of evidence-based recommendations and support shared, ethical decision making by doctors with their patients. Diagnostic and therapeutic technologies continue to evolve rapidly, and both individual practitioners and clinical teams face increasingly complex ethical decisions. Unfortunately, the current state of medical knowledge does not provide the guidance to make the majority of clinical decisions on the basis of evidence. The present research infrastructure is inefficient and frequently produces unreliable results that cannot be replicated. Even randomized controlled trials (RCTs), the traditional gold standards of the research reliability hierarchy, are not without limitations. They can be costly, labor intensive, and slow, and can return results that are seldom generalizable to every patient population. Furthermore, many pertinent but unresolved clinical and medical systems issues do not seem to have attracted the interest of the research enterprise, which has come to focus instead on cellular and molecular investigations and single-agent (e.g., a drug or device) effects. For clinicians, the end result is a bit of a “data desert” when it comes to making decisions. The new research infrastructure proposed in this book will help the medical profession to make ethically sound and well informed decisions for their patients. |
| case study data analysis: Qualitative Data Analysis with NVivo Patricia Bazeley, 2007-04-12 `In plain language but with very thorough detail, this book guides the researcher who really wants to use the NVivo software (and use it now) into their project. The way is lit with real-project examples, adorned with tricks and tips, but it’s a clear path to a project' - Lyn Richards, Founder and Non-Executive Director, QSR International Doing Qualitative Data Analysis with NVivo is essential reading for anyone thinking of using their computer to help analyze qualitative data. With 15 years experience in computer-assisted analysis of qualitative and mixed-mode data, Patricia Bazeley is one of the leaders in the use and teaching of NVivo software. Through this very practical book, readers are guided on how best to make use of the powerful and flexible tools offered by the latest version of NVivo as they work through each stage of their research projects. Explanations draw on examples from her own and others' projects, and are supported by the methodological literature. Researchers have different requirements and come to their data from different perspectives. This book shows how NVivo software can accommodate and assist analysis across those different perspectives and methodological approaches. It is required reading for both students and experienced researchers alike. |
| case study data analysis: Case Study Method Roger Gomm, Martyn Hammersley, Peter Foster, 2000-10-17 This is the most comprehensive guide to the current uses and importance of case study methods in social research. The editors bring together key contributions from the field which reflect different interpretations of the purpose and capacity of case study research. The address issues such as: the problem of generalizing from study of a small number of cases; and the role of case study in developing and testing theories. The editors offer in-depth assessments of the main arguments. An annotated bibliography of the literature dealing with case study research makes this an exhaustive and indispensable guide. |
| case study data analysis: Using Case Study in Education Research Lorna Hamilton, Connie Corbett-Whittier, 2012-11-19 This book provides an accessible introduction to using case studies. It makes sense of literature in this area, and shows how to generate collaborations and communicate findings. The authors bring together the practical and the theoretical, enabling readers to build expertise on the principles and practice of case study research, as well as engaging with possible theoretical frameworks. They also highlight the place of case study as a key component of educational research. With the help of this book, M-Level students, teacher educators and practitioner researchers will gain the confidence and skills needed to design and conduct a high quality case study. Dr Lorna Hamilton is a Senior Lecturer in Education Research at the University of Edinburgh. Dr Connie Corbett-Whittier is an Associate Professor of English and Humanities at Friends University, Topeka, Kansas. ′Drawing on a wide range of their own and others′ experiences, the authors offer a comprehensive and convincing account of the value of case study in educational research. What comes across - quite passionately - is the way in which a case study approach can bring to life some of the complexities, challenges and contradictions inherent in educational settings. The book is written in a clear and lively manner and should be an invaluable resource for those teachers and students who are incorporating a case study dimension into their research work.′ -Ian Menter, Professor of Teacher Education, University of Oxford ′This book is comprehensive in its coverage, yet detailed in its exposition of case study research. It is a highly interactive text with a critical edge and is a useful tool for teaching. It is of particular relevance to practitioner researchers, providing accessible guidance for reflective practice. It covers key matters such as: purposes, ethics, data analysis, technology, dissemination and communities for research. And it is a good read!′ - Professor Anne Campbell, formerly of Leeds Metropolitan University ′This excellent book is a principled and theoretically informed guide to case study research design and methods for the collection, analysis and presentatin of evidence′ - Professor Andrew Pollard, Institute of Education, University of London Research Methods in Education series: Each book in this series maps the territory of a key research approach or topic in order to help readers progress from beginner to advanced researcher. Each book aims to provide a definitive, market-leading overview and to present a blend of theory and practice with a critical edge. All titles in the series are written for Master′s-level students anywhere and are intended to be useful to the many diverse constituencies interested in research on education and related areas. Other books in the series: - Qualitative Research in Education, Atkins and Wallace - Action Research in Education, McAteer - Ethnography in Education, Mills and Morton |
| case study data analysis: Qualitative Techniques for Workplace Data Analysis Gupta, Manish, Shaheen, Musarrat, Reddy, K. Prathap, 2018-07-13 In businesses and organizations, understanding the social reality of individuals, groups, and cultures allows for in-depth understanding and rich analysis of multiple research areas to improve practices. Qualitative research provides important insight into the interactions of the workplace. Qualitative Techniques for Workplace Data Analysis is an essential reference source that discusses the qualitative methods used to analyze workplace data, as well as what measures should be adopted to ensure the credibility and dependability of qualitative findings in the workplace. Featuring research on topics such as collection methods, content analysis, and sampling, this book is ideally designed for academicians, development practitioners, business managers, and analytic professionals seeking coverage on quality measurement techniques in the occupational settings of emerging markets. |
| case study data analysis: Data Mining with R Luis Torgo, 2016-11-30 Data Mining with R: Learning with Case Studies, Second Edition uses practical examples to illustrate the power of R and data mining. Providing an extensive update to the best-selling first edition, this new edition is divided into two parts. The first part will feature introductory material, including a new chapter that provides an introduction to data mining, to complement the already existing introduction to R. The second part includes case studies, and the new edition strongly revises the R code of the case studies making it more up-to-date with recent packages that have emerged in R. The book does not assume any prior knowledge about R. Readers who are new to R and data mining should be able to follow the case studies, and they are designed to be self-contained so the reader can start anywhere in the document. The book is accompanied by a set of freely available R source files that can be obtained at the book’s web site. These files include all the code used in the case studies, and they facilitate the do-it-yourself approach followed in the book. Designed for users of data analysis tools, as well as researchers and developers, the book should be useful for anyone interested in entering the world of R and data mining. About the Author Luís Torgo is an associate professor in the Department of Computer Science at the University of Porto in Portugal. He teaches Data Mining in R in the NYU Stern School of Business’ MS in Business Analytics program. An active researcher in machine learning and data mining for more than 20 years, Dr. Torgo is also a researcher in the Laboratory of Artificial Intelligence and Data Analysis (LIAAD) of INESC Porto LA. |
| case study data analysis: Handbook of Research on Engineering Innovations and Technology Management in Organizations Gaur, Loveleen, Solanki, Arun, Jain, Vishal, Khazanchi, Deepak, 2020-04-17 As technology weaves itself more tightly into everyday life, socio-economic development has become intricately tied to these ever-evolving innovations. Technology management is now an integral element of sound business practices, and this revolution has opened up many opportunities for global communication. However, such swift change warrants greater research that can foresee and possibly prevent future complications within and between organizations. The Handbook of Research on Engineering Innovations and Technology Management in Organizations is a collection of innovative research that explores global concerns in the applications of technology to business and the explosive growth that resulted. Highlighting a wide range of topics such as cyber security, legal practice, and artificial intelligence, this book is ideally designed for engineers, manufacturers, technology managers, technology developers, IT specialists, productivity consultants, executives, lawyers, programmers, managers, policymakers, academicians, researchers, and students. |
| case study data analysis: From Neuron to Cognition via Computational Neuroscience Michael A. Arbib, James J. Bonaiuto, 2016-11-04 A comprehensive, integrated, and accessible textbook presenting core neuroscientific topics from a computational perspective, tracing a path from cells and circuits to behavior and cognition. This textbook presents a wide range of subjects in neuroscience from a computational perspective. It offers a comprehensive, integrated introduction to core topics, using computational tools to trace a path from neurons and circuits to behavior and cognition. Moreover, the chapters show how computational neuroscience—methods for modeling the causal interactions underlying neural systems—complements empirical research in advancing the understanding of brain and behavior. The chapters—all by leaders in the field, and carefully integrated by the editors—cover such subjects as action and motor control; neuroplasticity, neuromodulation, and reinforcement learning; vision; and language—the core of human cognition. The book can be used for advanced undergraduate or graduate level courses. It presents all necessary background in neuroscience beyond basic facts about neurons and synapses and general ideas about the structure and function of the human brain. Students should be familiar with differential equations and probability theory, and be able to pick up the basics of programming in MATLAB and/or Python. Slides, exercises, and other ancillary materials are freely available online, and many of the models described in the chapters are documented in the brain operation database, BODB (which is also described in a book chapter). Contributors Michael A. Arbib, Joseph Ayers, James Bednar, Andrej Bicanski, James J. Bonaiuto, Nicolas Brunel, Jean-Marie Cabelguen, Carmen Canavier, Angelo Cangelosi, Richard P. Cooper, Carlos R. Cortes, Nathaniel Daw, Paul Dean, Peter Ford Dominey, Pierre Enel, Jean-Marc Fellous, Stefano Fusi, Wulfram Gerstner, Frank Grasso, Jacqueline A. Griego, Ziad M. Hafed, Michael E. Hasselmo, Auke Ijspeert, Stephanie Jones, Daniel Kersten, Jeremie Knuesel, Owen Lewis, William W. Lytton, Tomaso Poggio, John Porrill, Tony J. Prescott, John Rinzel, Edmund Rolls, Jonathan Rubin, Nicolas Schweighofer, Mohamed A. Sherif, Malle A. Tagamets, Paul F. M. J. Verschure, Nathan Vierling-Claasen, Xiao-Jing Wang, Christopher Williams, Ransom Winder, Alan L. Yuille |
| case study data analysis: Applied Qualitative Research Design Margaret R. Roller, Paul J. Lavrakas, 2015-02-23 This unique text provides a comprehensive framework for creating, managing, and interpreting qualitative research studies that yield valid and useful information. Examples of studies from a wide range of disciplines illustrate the strengths, limitations, and applications of the primary qualitative methods: in-depth interviews, focus group discussions, ethnography, content analysis, and case study and narrative research. Following a consistent format, chapters show students and researchers how to implement each method within a paradigm-neutral and flexible Total Quality Framework (TQF) comprising four interrelated components: Credibility, Analyzability, Transparency, and Usefulness. Unlike other texts that relegate quality issues to one or two chapters, detailed discussions of such crucial topics as construct validity, interresearcher reliability, researcher bias, and verification strategies are featured throughout. The book also addresses applications of the TQF to the writing, review, and evaluation of qualitative research proposals and manuscripts. Pedagogical Features *Summary tables that highlight important content, such as the application of a method to vulnerable or hard-to-reach populations. *Case studies that illustrate TQF standards in practice for each method. *Guidelines for effective documentation (via thick descriptions) of each type of study. *End-of-chapter discussion topics, exercises, and suggested further reading and Web resources. *Chapters open with a preview and close with a bulleted summary of key ideas. *Extensive glossary. 2021 Winner--American Association for Public Opinion Research (AAPOR) Book Award |
| case study data analysis: Case Study Strategies for Architects and Designers Marja Sarvimaki, 2017-06-27 Case Study Strategies for Architects and Designers explains methods in evidence-based design, also called practice-based research, to show you the value of research to your designs. Topics covered pertain to data collection and analysis techniques, including surveys, interviews, fieldwork, participatory design, occupancy evaluations, and memory sketching. Integrative data evaluation, theoretical sampling, triangulation, pattern matching logic, and analytical generalization are also discussed. Global research precedents, exercises, further reading, section summaries, sidebars, more than 30 black and white images and tables will help you conduct empirical inquiries in real-life contexts. |
| case study data analysis: Analyzing Neural Time Series Data Mike X Cohen, 2014-01-17 A comprehensive guide to the conceptual, mathematical, and implementational aspects of analyzing electrical brain signals, including data from MEG, EEG, and LFP recordings. This book offers a comprehensive guide to the theory and practice of analyzing electrical brain signals. It explains the conceptual, mathematical, and implementational (via Matlab programming) aspects of time-, time-frequency- and synchronization-based analyses of magnetoencephalography (MEG), electroencephalography (EEG), and local field potential (LFP) recordings from humans and nonhuman animals. It is the only book on the topic that covers both the theoretical background and the implementation in language that can be understood by readers without extensive formal training in mathematics, including cognitive scientists, neuroscientists, and psychologists. Readers who go through the book chapter by chapter and implement the examples in Matlab will develop an understanding of why and how analyses are performed, how to interpret results, what the methodological issues are, and how to perform single-subject-level and group-level analyses. Researchers who are familiar with using automated programs to perform advanced analyses will learn what happens when they click the “analyze now” button. The book provides sample data and downloadable Matlab code. Each of the 38 chapters covers one analysis topic, and these topics progress from simple to advanced. Most chapters conclude with exercises that further develop the material covered in the chapter. Many of the methods presented (including convolution, the Fourier transform, and Euler's formula) are fundamental and form the groundwork for other advanced data analysis methods. Readers who master the methods in the book will be well prepared to learn other approaches. |
| case study data analysis: The Practice of Reproducible Research Justin Kitzes, Daniel Turek, Fatma Deniz, 2018 The Practice of Reproducible Research presents concrete examples of how researchers in the data-intensive sciences are working to improve the reproducibility of their research projects. In each of the thirty-one case studies in this volume, the author or team describes the workflow that they used to complete a real-world research project. Authors highlight how they utilized particular tools, ideas, and practices to support reproducibility, emphasizing the very practical how, rather than the why or what, of conducting reproducible research. Part 1 provides an accessible introduction to reproducible research, a basic reproducible research project template, and a synthesis of lessons learned from across the thirty-one case studies. Parts 2 and 3 focus on the case studies themselves. The Practice of Reproducible Research is an invaluable resource for students and researchers who wish to better understand the practice of data-intensive sciences and learn how to make their own research more reproducible. |
| case study data analysis: Doing Case Study Research Bob Algozzine, Dawson Hancock, 2017 Reflecting recent knowledge and developments in the field, this very practical, easy-to-use guide emphasizes learning how to do case study research—from the first step of deciding whether a case study is the way to go to the last step of verifying and confirming findings before disseminating them. The authors show students how to determine an appropriate research design, conduct informative interviews, record observations, document analyses, delineate ways to confirm case study findings, describe methods for deriving meaning from data, and communicate their findings. Featuring many new examples, the Third Edition offers step-by-step guidance to help beginning researchers through the stages of planning and implementing a thesis, dissertation, or independent project. This succinct “how-to” guide is an excellent place for anyone to begin doing case study research. Book Features: Straightforward introduction to the science of doing case study research. A step-by-step approach that speaks directly to the novice investigator. Many concrete examples to illustrate key concepts. Questions, illustrations, and activities to reinforce what has been learned. |
| case study data analysis: An Introductory Course in Computational Neuroscience Paul Miller, 2018-10-09 A textbook for students with limited background in mathematics and computer coding, emphasizing computer tutorials that guide readers in producing models of neural behavior. This introductory text teaches students to understand, simulate, and analyze the complex behaviors of individual neurons and brain circuits. It is built around computer tutorials that guide students in producing models of neural behavior, with the associated Matlab code freely available online. From these models students learn how individual neurons function and how, when connected, neurons cooperate in a circuit. The book demonstrates through simulated models how oscillations, multistability, post-stimulus rebounds, and chaos can arise within either single neurons or circuits, and it explores their roles in the brain. The book first presents essential background in neuroscience, physics, mathematics, and Matlab, with explanations illustrated by many example problems. Subsequent chapters cover the neuron and spike production; single spike trains and the underlying cognitive processes; conductance-based models; the simulation of synaptic connections; firing-rate models of large-scale circuit operation; dynamical systems and their components; synaptic plasticity; and techniques for analysis of neuron population datasets, including principal components analysis, hidden Markov modeling, and Bayesian decoding. Accessible to undergraduates in life sciences with limited background in mathematics and computer coding, the book can be used in a “flipped” or “inverted” teaching approach, with class time devoted to hands-on work on the computer tutorials. It can also be a resource for graduate students in the life sciences who wish to gain computing skills and a deeper knowledge of neural function and neural circuits. |
| case study data analysis: Case Study Evaluations , 1990 |
Case Study - Methods, Examples and Guide - Research Method
Mar 26, 2024 · Key Characteristics of Case Studies: Focus on a specific case, individual, or event. Provide in-depth analysis and contextual understanding. Useful for exploring new or complex …
Qualitative case study data analysis: an example from ...
This paper illustrates specific strategies that can be employed when conducting data analysis in case study research and other qualitative research designs.
Qualitative Case Study Methodology: Study Design and ...
Jan 1, 2010 · Qualitative case study methodology provides tools for researchers to study complex phenomena within their contexts. When the approach is applied correctly, it becomes a …
Qualitative Analysis in Case Study - SAGE Publications Inc
To enable the researcher to present the findings and answer the research questions, these data need to be systematically dissected, rearranged, organized, and interpreted. Data analysis, …
Analyzing Case Study Evidence - SAGE Publications Inc
Tip: How do I start analyzing my case study data? You might start with questions (e.g., the questions in your case study protocol) rather than with the data. Start with a small question …
Case Study Methods and Examples - Sage Research Methods …
Feb 8, 2021 · What is case study methodology? It is unique given one characteristic: case studies draw from more than one data source. In this post find definitions and a collection of …
Thematic Analysis for Case Studies | Guide & Examples
Read this guide to get a foundational understanding of thematic analysis and its contribution to qualitative research. Thematic analysis and case study research are widely used qualitative …
Kurt Schoch I - SAGE Publications Ltd
cipline-specific applications of case study methods and describe the appropriate research questions addressed by case studies. I follow this description with methods considerations, …
Analysis of Toyota Motor Corporation - Scholars at Harvard
3.3 SWOT Analysis 3.4 BCG Matrix: Internal Analysis of Toyota Portfolio 3.5 VRIO Framework Analysis 3.6 Toyota’s Efforts in Emerging Economies 3.7 Case Study: Toyota’s Successful …
Data Analysis - Springer
In case study research, the most preferred data analysis strategy is relying on the theoretical propositions which lead to the case study. In this book, the original propositions shaped the …
Methods in Case Study Analysis - Webanketa
with conducting case studies are the intensity of the data collection process and the overload of information obtained. Part of the intensity is related to the fact that in case studies, data …
Design and Analysis of Case-Control Studies - UC Davis Health
For a given disease, a case-control study can investigate multiple exposures (when the real exposure is not known). A case-control study is preferred when the disease is rare because …
Case Study Applications of Statistics in Institutional Research
Case Study Applications of . Statistics in Institutional Research . Introduction . Statistics has been defined as "a collection of methods for planning experiments, obtaining data, and then …
Weibull Analysis Guide & Case Study
Calculate Weibull Parameters and Reliability Metrics – Case Study The steps taken to perform a Weibull analysis with suspended data is given in the flow from Figure 3. This example will use …
A Narrative Approach to Qualitative Inquiry - ASCLS
fashion, in all classification schemes. These include case studies, ethnography, grounded theory, narrative, and phenomenology. This article will provide the reader ... become the findings …
A (VERY) BRIEF REFRESHER ON THE CASE STUDY METHOD
Besides discussing case study design, data collection, and analysis, the refresher addresses several key features of case study research. First, an abbreviated definition of a “case study” …
Privacy Issues and Data Protection in Big Data: A Case …
Keywords—big data; data analysis; privacy; data protection; GDPR; data anonymization; information security, biometric privacy I. INTRODUCTION The term big data describes large or …
Chapter 3 Research methodology - University of Pretoria
descriptive case studies. In an exploratory case study, the collection of data occurs before theories or specific research questions are formulated: it is followed up by analysis of data and …
CHAPTER 1 Single Cases - Guilford Press
study. To study a case, we carefully examine its functioning and activities, but the first objective of a case study is to understand the case. In time, we may move on to studying its functioning …
Qualitative Case Study Methodology: Study Design and …
questions, study propositions and the different types of case study designs are provided. Key Words: Case Study and Qualitative Methods . Introduction . To graduate students and …
Designing and Conducting Case Studies - Colorado State …
Unlike more statistically-based studies which search for quantifiable data, the goal of a case study is to offer new variables and questions for further research. F.H. Giddings, a sociologist in the …
CHECKLISTS FOR READING AND REVIEWING CASE STUDIES
43. Are the data collection procedures sufficient for the purpose of the case study (data sources, collection, validation)? 11, 13, 16, 18, 21, 31 44. Is sufficient raw data presented to provide …
K.: Study IX). - JSTOR
94 Rezensionen times invites thereader to flipthrough thepages, but itisa helpful tool kiton how to proceed inconducting a case study.There isa similar effectin chapter 4 where the procedures of …
PUBLIC SECTOR DATA ANALYTICS - Princeton Dialogues on …
Some, however, may prefer not to have their data shared at all. In the New Leviathan case, how should individual privacy interests be weighed against WCG and Mayor Hobbes’ expectations …
: A Case Study Analysis of TikTok - ResearchGate
Objective : This paper proposes TikTok's business model and strategy as a case study analysis. Method : This article was reviewed using systematic literature. The scholarly publications from
Big Data in Governance in India: Case Studies
Each case study has been chosen based on the use of big data in the area and the opportunity that is present for policy recommendation and reform. Each case study will seek to answer a …
How to Improve the Validity and Reliability of a Case Study …
Feb 22, 2020 · cases. Nevertheless, in a case study, quantitative data can be used to quantify occurrences of a given phenomenon, but in which the context is not controlled by the …
The utility of case study as a methodology for work …
be atypical of other cases. A collective case study explores differences within and between cases ideally to replicate findings across them. Yin (2013) strongly advocates for the power of a case …
IN INTERNATIONAL STUDIES - JSTOR
354 PragmaticGuidetoQualitativeHistoricalAnalysis theirunderstanding.Ontheotherhand,latenteventsrefertoeventsthatcon- …
Kurt Schoch I - ResearchGate
Case study research involves a detailed and intensive analysis of a particular event, situation, orga- nization, or social unit. Typically, a case has a defined space and time frame: “a ...
Target Cyber Attack: A Columbia University Case Study
This case study will first consider Target’s vulnerabilities to an external attack in 2013 and explain how the attackers stole the data. Second, this case study will discuss the importance of …
Case Study: Improving Decision Making, Accountability, and …
The project kick-started with a comprehensive analysis of various data sources along with stakeholder consultations to pinpoint key challenges and key performance indicators (KPIs). ...
Case Study 3 - NONMEM - University of Maryland, Baltimore
Sequential PKPD modeling approach is used in this case study. Based on the drug’s mechanism of action, an inhibitory indirect response model was chosen. Creating data file for PK analysis …
CASE STUDY APPROACH IN QUALITATIVE RESEARCH - ijrcs.org
definitions of the case study as a method in qualitative research. Merriam ( 1988) defines µa qualitative case study as an intensive, holistic description and analysis of a single instance, …
The SAGE Handbook of Qualitative Data Analysis - SAGE …
Qualitative data analysis is the classification and interpretation of linguistic (or visual) material ... This can focus on the case (indi-vidual or group) and its special features and the links between …
A Case Study of the Capital One Data Breach
1. This case study containing a detailed analysis to identify and understand the technical modus operandi of the attack, as well as what conditions allowed a breach and the related regulations; …
Qualitative & Mixed Methods Data Analysis with MAXQDA
Dec 23, 2020 · Qualitative Data Data Analysis Transcripts, articles, documents, data from standardized surveys, tables, audio file, video file, websites, field notes, geographical …
Core Guide: Longitudinal Data Analysis - Duke University
Oct 5, 2017 · Core Guide: Longitudinal Data Analysis 2 | P a g e the most common longitudinal study designs, as well as the most appropriate methods for analyzing the resulting data. Since …
THE CASE STUDY IN EDUCATIONAL RESEARCH: A REVIEW OF …
TheJournal ofEducational Thought, Vol. 19, No. 3, December 1985 205 ology sections of research reports, chapters on case study in research texts, and recent texts on qualitative or naturalistic …
9 Multiple Case Research Design - Springer
general answer to whether a single case study or multiple case study is preferable. This always depends on the specic aim, the cases, and the resources. We distinguish sev-eral units of …
A qualitative multiple case study investigating information …
A qualitative multiple case study investigating information exchange at lung cancer consultations . Allison Smith . Department of Nursing & Midwifery
Journal of Artificial Intelligence, Machine Learning and Data …
Machine Learning in Cybersecurity: A Multi-Industry Case Study Analysis for Enhanced Threat Detection and Response Victor Oriakhi Nosakhare1, Bisola Kayode2, Samuel Akerele3, …
Qualitative Case Study Guidelines - Nova Southeastern …
Case study research involves “intensive study of a single unit for the purpose of understanding a larger class of (similar) units … observed at a single point in time or over ... propositions to …
Completed in 2024-2025 - upike.edu
2023 and 2021-2022 completers were also included in the study. Because we analyzed data from completers from the past three years, we do not formally compare data from the previous case …
Principles of Qualitative Research: Designing a Qualitative Study
Data Collection • Developing an in-depth analysis of a single case or multiple cases • Describing and interpreting a cultural or social group • Developing a theory grounded from data in the field …
3. Case Studies Using ACS Data - Census.gov
research and data science teams also rely on critical data inputs from the Census Bureau to analyze the full impli-cations of changing home values and rents for American households. …
Case study as a research method - CORE
By including both quantitative and qualitative data, case study helps explain both the process and outcome of a phenomenon through complete observation, reconstruction and analysis of the …
MA Research Report _body_1 - University of the Witwatersrand
%PDF-1.3 %âãÏÓ 123 0 obj /Linearized 1 /O 125 /H [ 828 684 ] /L 187801 /E 81350 /N 36 /T 185222 >> endobj xref 123 19 0000000016 00000 n 0000000731 00000 n 0000001512 00000 n …
Validity, Reliability and Triangulation in Case Study Method: …
validity and reliability of the case study evidence which are; (1) multiple sources of evidence; (2) create a case study database; and (3) maintain a chain of evidence. With regards to rigour and …
Longitudinal Data Analysis - Harvard T.H. Chan School of …
founding, introductory treatments of causal inference often consider only the case of fixed exposures. This chapter provides an introduction to causal inference for time-varying …
The Interpretative Phenomenological Analysis (IPA): A …
used in conducting his thesis research study to help inform and guide new researchers to a more comprehensive qualitative methodological tradition, The Interpretative Phenomenological …
Robert K. Yin. (2014). Case study research design and …
Case study research design and methods (5th ed.). Thousand Oaks, CA: Sage. 282 pages. ... preparation, data collection, analysis and reporting. Each of these features forms the topic
A CASE STUDY ON ZOMATO - IRJMETS
This study's analysis is based on secondary data. As a result, textual sources are considered for data collecting. The research examines Zomato's marketing practises and proposes strategies …
An Exercise in Rigor: A Review of Robert K. Yin’s Case Study …
Case Study Research, Generalizability, Methodology, Rigor, Qualitative Research . ... preparation, data collection, analysis, and reporting. Each chapter is introduced by a series of …
Chapter 9 Qualitative Methods - City University of New York
It further expands on the data analysis steps and the methods used for presenting the data, interpreting it, validating it, and indicating the potential ... phenomenology); explore processes, …
Agoda Case Study - Vertica
Case Study Agoda Agoda uses OpenText technology to accelerate the analysis of billions of data points from all customer journeys, powering con - tinuous, ultra-intelligent website optimization. …
Doing Participatory Action Research in a Multicase Study: A ...
stood. Case study researchers, therefore, should carefully describe their methods of data collection and analysis to enhance reliability and provide exemplars for other case study …
