| data analysis research topics: 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. |
| data analysis research topics: Data Science and Social Research N. Carlo Lauro, Enrica Amaturo, Maria Gabriella Grassia, Biagio Aragona, Marina Marino, 2017-11-17 This edited volume lays the groundwork for Social Data Science, addressing epistemological issues, methods, technologies, software and applications of data science in the social sciences. It presents data science techniques for the collection, analysis and use of both online and offline new (big) data in social research and related applications. Among others, the individual contributions cover topics like social media, learning analytics, clustering, statistical literacy, recurrence analysis and network analysis. Data science is a multidisciplinary approach based mainly on the methods of statistics and computer science, and its aim is to develop appropriate methodologies for forecasting and decision-making in response to an increasingly complex reality often characterized by large amounts of data (big data) of various types (numeric, ordinal and nominal variables, symbolic data, texts, images, data streams, multi-way data, social networks etc.) and from diverse sources. This book presents selected papers from the international conference on Data Science & Social Research, held in Naples, Italy in February 2016, and will appeal to researchers in the social sciences working in academia as well as in statistical institutes and offices. |
| data analysis research topics: The SAGE Encyclopedia of Communication Research Methods Mike Allen, 2017-04-11 Communication research is evolving and changing in a world of online journals, open-access, and new ways of obtaining data and conducting experiments via the Internet. Although there are generic encyclopedias describing basic social science research methodologies in general, until now there has been no comprehensive A-to-Z reference work exploring methods specific to communication and media studies. Our entries, authored by key figures in the field, focus on special considerations when applied specifically to communication research, accompanied by engaging examples from the literature of communication, journalism, and media studies. Entries cover every step of the research process, from the creative development of research topics and questions to literature reviews, selection of best methods (whether quantitative, qualitative, or mixed) for analyzing research results and publishing research findings, whether in traditional media or via new media outlets. In addition to expected entries covering the basics of theories and methods traditionally used in communication research, other entries discuss important trends influencing the future of that research, including contemporary practical issues students will face in communication professions, the influences of globalization on research, use of new recording technologies in fieldwork, and the challenges and opportunities related to studying online multi-media environments. Email, texting, cellphone video, and blogging are shown not only as topics of research but also as means of collecting and analyzing data. Still other entries delve into considerations of accountability, copyright, confidentiality, data ownership and security, privacy, and other aspects of conducting an ethical research program. Features: 652 signed entries are contained in an authoritative work spanning four volumes available in choice of electronic or print formats. Although organized A-to-Z, front matter includes a Reader’s Guide grouping entries thematically to help students interested in a specific aspect of communication research to more easily locate directly related entries. Back matter includes a Chronology of the development of the field of communication research; a Resource Guide to classic books, journals, and associations; a Glossary introducing the terminology of the field; and a detailed Index. Entries conclude with References/Further Readings and Cross-References to related entries to guide students further in their research journeys. The Index, Reader’s Guide themes, and Cross-References combine to provide robust search-and-browse in the e-version. |
| data analysis research topics: Predicting the Dynamics of Research Impact Yannis Manolopoulos, Thanasis Vergoulis, 2021-09-22 This book provides its readers with an introduction to interesting prediction and science dynamics problems in the field of Science of Science. Prediction focuses on the forecasting of future performance (or impact) of an entity, either a research article or a scientist, and also the prediction of future links in collaboration networks or identifying missing links in citation networks. The single chapters are written in a way that help the reader gain a detailed technical understanding of the corresponding subjects, the strength and weaknesses of the state-of-the-art approaches for each described problem, and the currently open challenges. While chapter 1 provides a useful contribution in the theoretical foundations of the fields of scientometrics and science of science, chapters 2-4 turn the focal point to the study of factors that affect research impact and its dynamics. Chapters 5-7 then focus on article-level measures that quantify the current and future impact of scientific articles. Next, chapters 8-10 investigate subjects relevant to predicting the future impact of individual researchers. Finally, chapters 11-13 focus on science evolution and dynamics, leveraging heterogeneous and interconnected data, where the analysis of research topic trends and their evolution has always played a key role in impact prediction approaches and quantitative analyses in the field of bibliometrics. Each chapter can be read independently, since it includes a detailed description of the problem being investigated along with a thorough discussion and study of the respective state-of-the-art. Due to the cross-disciplinary character of the Science of Science field, the book may be useful to interested readers from a variety of disciplines like information science, information retrieval, network science, informetrics, scientometrics, and machine learning, to name a few. The profiles of the readers may also be diverse ranging from researchers and professors in the respective fields to students and developers being curious about the covered subjects. |
| data analysis research topics: Data Analysis for the Life Sciences with R Rafael A. Irizarry, Michael I. Love, 2016-10-04 This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The authors proceed from relatively basic concepts related to computed p-values to advanced topics related to analyzing highthroughput data. They include the R code that performs this analysis and connect the lines of code to the statistical and mathematical concepts explained. |
| data analysis research topics: 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. |
| data analysis research topics: Cutting-Edge Research Topics on Multiple Criteria Decision Making Yong Shi, Shouyang Wang, Yi Peng, Jianping Li, Yong Zeng, 2009-07-09 MCDM 2009, the 20th International Conference on Multiple-Criteria Decision M- ing, emerged as a global forum dedicated to the sharing of original research results and practical development experiences among researchers and application developers from different multiple-criteria decision making-related areas such as multiple-criteria decision aiding, multiple criteria classification, ranking, and sorting, multiple obj- tive continuous and combinatorial optimization, multiple objective metaheuristics, multiple-criteria decision making and preference modeling, and fuzzy multiple-criteria decision making. The theme for MCDM 2009 was “New State of MCDM in the 21st Century.” The conference seeks solutions to challenging problems facing the development of multiple-criteria decision making, and shapes future directions of research by prom- ing high-quality, novel and daring research findings. With the MCDM conference, these new challenges and tools can easily be shared with the multiple-criteria decision making community. The workshop program included nine workshops which focused on different topics in new research challenges and initiatives of MCDM. We received more than 350 submissions for all the workshops, out of which 121 were accepted. This includes 72 regular papers and 49 short papers. We would like to thank all workshop organizers and the Program Committee for the excellent work in maintaining the conference’s standing for high-quality papers. |
| data analysis research topics: 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 |
| data analysis research topics: Frontiers in Massive Data Analysis National Research Council, Division on Engineering and Physical Sciences, Board on Mathematical Sciences and Their Applications, Committee on Applied and Theoretical Statistics, Committee on the Analysis of Massive Data, 2013-09-03 Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data. |
| data analysis research topics: 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. |
| data analysis research topics: The Handbook of Creative Data Analysis Helen Kara, Dawn Mannay, Alastair Roy, 2024-09-11 Creative research methods for data generation have expanded over recent decades and researchers are eager to take a creative approach to data analysis. It is challenging to bring creativity into data analysis while retaining a systematic, rigorous and ethical approach. Written by experts in the field, this handbook addresses these challenges. The chapters adapt analytical techniques in creative ways for novice and expert researchers. Existing and novel methods from analysis of quantitative data to embodied, performative, visual, written, arts-based and collaborative analysis are featured with transferable case examples across disciplines. This collection offers a definitive practical guide to creative data analysis. |
| data analysis research topics: The Behavioral and Social Sciences National Research Council, Division of Behavioral and Social Sciences and Education, Commission on Behavioral and Social Sciences and Education, Committee on Basic Research in the Behavioral and Social Sciences, 1988-02-01 This volume explores the scientific frontiers and leading edges of research across the fields of anthropology, economics, political science, psychology, sociology, history, business, education, geography, law, and psychiatry, as well as the newer, more specialized areas of artificial intelligence, child development, cognitive science, communications, demography, linguistics, and management and decision science. It includes recommendations concerning new resources, facilities, and programs that may be needed over the next several years to ensure rapid progress and provide a high level of returns to basic research. |
| data analysis research topics: Computing, Internet of Things and Data Analytics Fausto Pedro García Márquez, |
| data analysis research topics: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true signals in your dataset Communicate—learn R Markdown for integrating prose, code, and results |
| data analysis research topics: Data Science for Economics and Finance Sergio Consoli, Diego Reforgiato Recupero, Michaela Saisana, 2021 This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications. |
| data analysis research topics: Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry Chkoniya, Valentina, 2021-06-25 The contemporary world lives on the data produced at an unprecedented speed through social networks and the internet of things (IoT). Data has been called the new global currency, and its rise is transforming entire industries, providing a wealth of opportunities. Applied data science research is necessary to derive useful information from big data for the effective and efficient utilization to solve real-world problems. A broad analytical set allied with strong business logic is fundamental in today’s corporations. Organizations work to obtain competitive advantage by analyzing the data produced within and outside their organizational limits to support their decision-making processes. This book aims to provide an overview of the concepts, tools, and techniques behind the fields of data science and artificial intelligence (AI) applied to business and industries. The Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry discusses all stages of data science to AI and their application to real problems across industries—from science and engineering to academia and commerce. This book brings together practice and science to build successful data solutions, showing how to uncover hidden patterns and leverage them to improve all aspects of business performance by making sense of data from both web and offline environments. Covering topics including applied AI, consumer behavior analytics, and machine learning, this text is essential for data scientists, IT specialists, managers, executives, software and computer engineers, researchers, practitioners, academicians, and students. |
| data analysis research topics: Methods and Data Analysis for Cross-Cultural Research Fons J. R. van de Vijver, Kwok Leung, 2021-06-24 This is an integrated introduction to methods, research design, and data analysis tailored to the challenges of cross-cultural research. |
| data analysis research topics: 100 Questions (and Answers) About Action Research Luke Duesbery, Todd Twyman, 2019-03-07 100 Questions (and Answers) About Action Research by Luke Duesbery and Todd Twyman identifies and answers the essential questions on the process of systematically approaching your practice from an inquiry-oriented perspective, with a focus on improving that practice. This unique text offers progressive instructors an alternative to the research status quo and serves as a reference for readers to improve their practice as advocates for those they serve. The Question and Answer format makes this an ideal supplementary text for traditional research methods courses, and also a helpful guide for practitioners in education, social work, criminal justice, health, business, and other applied disciplines. |
| data analysis research topics: Large Scale and Big Data Sherif Sakr, Mohamed Gaber, 2014-06-25 Large Scale and Big Data: Processing and Management provides readers with a central source of reference on the data management techniques currently available for large-scale data processing. Presenting chapters written by leading researchers, academics, and practitioners, it addresses the fundamental challenges associated with Big Data processing tools and techniques across a range of computing environments. The book begins by discussing the basic concepts and tools of large-scale Big Data processing and cloud computing. It also provides an overview of different programming models and cloud-based deployment models. The book’s second section examines the usage of advanced Big Data processing techniques in different domains, including semantic web, graph processing, and stream processing. The third section discusses advanced topics of Big Data processing such as consistency management, privacy, and security. Supplying a comprehensive summary from both the research and applied perspectives, the book covers recent research discoveries and applications, making it an ideal reference for a wide range of audiences, including researchers and academics working on databases, data mining, and web scale data processing. After reading this book, you will gain a fundamental understanding of how to use Big Data-processing tools and techniques effectively across application domains. Coverage includes cloud data management architectures, big data analytics visualization, data management, analytics for vast amounts of unstructured data, clustering, classification, link analysis of big data, scalable data mining, and machine learning techniques. |
| data analysis research topics: Data Science and Analytics Brajendra Panda, Sudeep Sharma, Nihar Ranjan Roy, 2018-03-07 This book constitutes the refereed proceedings of the 4th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2017, held in Gurgaon, India, in October 2017. The 66 revised full papers presented were carefully reviewed and selected from 329 submissions. The papers are organized in topical sections on big data analysis, data centric programming, next generation computing, social and web analytics, security in data science analytics. |
| data analysis research topics: 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. |
| data analysis research topics: Data Science in Education Using R Ryan A. Estrellado, Emily Freer, Joshua M. Rosenberg, Isabella C. Velásquez, 2020-10-26 Data Science in Education Using R is the go-to reference for learning data science in the education field. The book answers questions like: What does a data scientist in education do? How do I get started learning R, the popular open-source statistical programming language? And what does a data analysis project in education look like? If you’re just getting started with R in an education job, this is the book you’ll want with you. This book gets you started with R by teaching the building blocks of programming that you’ll use many times in your career. The book takes a learn by doing approach and offers eight analysis walkthroughs that show you a data analysis from start to finish, complete with code for you to practice with. The book finishes with how to get involved in the data science community and how to integrate data science in your education job. This book will be an essential resource for education professionals and researchers looking to increase their data analysis skills as part of their professional and academic development. |
| data analysis research topics: Research Methods and Data Analysis for Business Decisions James E. Sallis, Geir Gripsrud, Ulf Henning Olsson, Ragnhild Silkoset, 2021-10-30 This introductory textbook presents research methods and data analysis tools in non-technical language. It explains the research process and the basics of qualitative and quantitative data analysis, including procedures and methods, analysis, interpretation, and applications using hands-on data examples in QDA Miner Lite and IBM SPSS Statistics software. The book is divided into four parts that address study and research design; data collection, qualitative methods and surveys; statistical methods, including hypothesis testing, regression, cluster and factor analysis; and reporting. The intended audience is business and social science students learning scientific research methods, however, given its business context, the book will be equally useful for decision-makers in businesses and organizations. |
| data analysis research topics: Advances in Quantitative Ethnography Yoon Jeon Kim, |
| data analysis research topics: Quantitative Health Research: Issues and Methods Elizabeth Curtis, Jonathan Drennan, 2013-09-16 This book is a detailed and comprehensive guide to undertaking quantitative health research at postgraduate and professional level. It takes you through the entire research process, from designing the project to presenting the results and will help you execute high quality quantitative research that improves and informs clinical practice. Written by a team of research experts, this book covers common practical problems such as applying theory to research and analysing data. It also includes chapters on communicating with ethics committees, recruiting samples from vulnerable populations, audit as a research approach, quasi-experimental designs and using cognitive interviewing, making it a new and innovative offering for health researchers. Other topics covered in this book include: Ethical considerations of research Designing and planning quantitative research projects Data measurement and collection Analyzing and presenting resultsWith a strong practical focus, each chapter features examples of real-life research to illustrate the quantitative research process, as well as tips and insights into research planning and execution. This book is an essential guide for all health care professionals undertaking a postgraduate degree, as well as health researchers and practitioners who need to carry out research as part of their professional role. Contributors: Ruth Belling, Michelle Butler, Catherine Comiskey, Siobhan Corrigan, Gloria Crispino, Orla Dempsey, Suzanne Guerin, Maree Johnson, Carmel Kelly, Elaine Lehane, Maria Lohan, Susan McLaren, Deirdre Mongan, Corina Naughton, Rhona O'Connell, Elaine Pierce, Gary Rolfe, Eileen Savage, Anne Scott, Emma Stokes, Roger Watson “Learning quantitative research is taken much for granted. This is probably why there are fewer generic books on quantitative than qualitative research. This book is long overdue. Clearly- written and well structured, it takes us through the whole journey of a research project from developing 'research questions' to 'presenting the findings', passing through philosophical underpinnings, recruitment of participants and ethical considerations. Written by an array of well-known researchers and teachers, this book will certainly appeal to new as well as seasoned researchers. Those who will use it, will not be disappointed. Kader Parahoo, University of Ulster The title of this text is somewhat misleading. It is not only an excellent and thorough guide to qualitative health research methods; it is also an excellent introduction to all forms of qualitative research. It takes the reader gently through theoretical and ethical concerns to the practicalities and benefits of utilising qualitative approaches. As such it is that rare thing; a text that can be used by novice researchers to learn their craft, and a key reference resource for experienced research practitioners. Dr. John Cullen, School of Business, National University of Ireland, Maynooth, UK This is a first-rate collection of essays that promotes an informed understanding of both underpinning principles and widely used techniques. A great deal of effort has clearly been invested in co-ordinating the contributions, and this has delivered clarity, complementarity and effective coverage. This is a welcome, carefully-crafted and very accessible resource that will appeal to students and researchers in healthcare and beyond. Martin Beirne, Professor of Management and Organizational Behaviour, University of Glasgow, Adam Smith Business School, UK |
| data analysis research topics: Coastal environmental and ecological data analysis Meilin Wu, Yu-Pin Lin, Biraja Kumar Sahu, Ana Carolina Ruiz-Fernández, 2023-04-17 |
| data analysis research topics: Big Data Analytics in Bioinformatics and Healthcare Wang, Baoying, 2014-10-31 As technology evolves and electronic data becomes more complex, digital medical record management and analysis becomes a challenge. In order to discover patterns and make relevant predictions based on large data sets, researchers and medical professionals must find new methods to analyze and extract relevant health information. Big Data Analytics in Bioinformatics and Healthcare merges the fields of biology, technology, and medicine in order to present a comprehensive study on the emerging information processing applications necessary in the field of electronic medical record management. Complete with interdisciplinary research resources, this publication is an essential reference source for researchers, practitioners, and students interested in the fields of biological computation, database management, and health information technology, with a special focus on the methodologies and tools to manage massive and complex electronic information. |
| data analysis research topics: Past, Present and Future of Computing Education Research Mikko Apiola, Sonsoles López-Pernas, Mohammed Saqr, 2023-04-17 This book presents a collection of meta-studies, reviews, and scientometric analyses that together reveal a fresh picture about the past, present, and future of computing education research (CER) as a field of science. The book begins with three chapters that discuss and summarise meta-research about the foundations of CER, its disciplinary identity, and use of research methodologies and theories. Based on this, the book proceeds with several scientometric analyses, which explore authors and their collaboration networks, dissemination practices, international collaboration, and shifts in research focus over the years. Analyses of dissemination are deepened in two chapters that focus on some of the most influential publication venues of CER. The book also contains a series of country-, or region-level analyses, including chapters that focus on the evolution of CER in the Baltic Region, Finland, Australasia, Israel, and in the UK & Ireland. Two chapters present case studies of influential CER initiatives in Sweden and Namibia. This book also includes chapters that focus on CER conducted at school level, and cover crucially important issues such as technology ethics, algorithmic bias, and their implications for CER.In all, this book contributes to building an understanding of the past, present and future of CER. This book also contributes new practical guidelines, highlights topical areas of research, shows who to connect with, where to publish, and gives ideas of innovative research niches. The book takes a unique methodological approach by presenting a combination of meta-studies, scientometric analyses of publication metadata, and large-scale studies about the evolution of CER in different geographical regions. This book is intended for educational practitioners, researchers, students, and anyone interested in CER. This book was written in collaboration with some of the leading experts of the field. |
| data analysis research topics: Using Microsoft Excel for Social Research Charlotte Brookfield, 2021-01-20 Full of practical advice and real-world examples, this step-by-step guide offers you an accessible introduction to doing quantitative social research using Microsoft Excel. |
| data analysis research topics: Analytics Phil Simon, 2017-07-03 For years, organizations have struggled to make sense out of their data. IT projects designed to provide employees with dashboards, KPIs, and business-intelligence tools often take a year or more to reach the finish line...if they get there at all. This has always been a problem. Today, though, it's downright unacceptable. The world changes faster than ever. Speed has never been more important. By adhering to antiquated methods, firms lose the ability to see nascent trends—and act upon them until it's too late. But what if the process of turning raw data into meaningful insights didn't have to be so painful, time-consuming, and frustrating? What if there were a better way to do analytics? Fortunately, you're in luck... Analytics: The Agile Way is the eighth book from award-winning author and Arizona State University professor Phil Simon. Analytics: The Agile Way demonstrates how progressive organizations such as Google, Nextdoor, and others approach analytics in a fundamentally different way. They are applying the same Agile techniques that software developers have employed for years. They have replaced large batches in favor of smaller ones...and their results will astonish you. Through a series of case studies and examples, Analytics: The Agile Way demonstrates the benefits of this new analytics mind-set: superior access to information, quicker insights, and the ability to spot trends far ahead of your competitors. |
| data analysis research topics: Topics in Applied Multivariate Analysis D. M. Hawkins, 1982-04-22 Multivariate methods are employed widely in the analysis of experimental data but are poorly understood by those users who are not statisticians. This is because of the wide divergence between the theory and practice of multivariate methods. This book provides concise yet thorough surveys of developments in multivariate statistical analysis and gives statistically sound coverage of the subject. The contributors are all experienced in the theory and practice of multivariate methods and their aim has been to emphasize the major features from the point of view of applicability and to indicate the limitations and conditions of the techniques. Professional statisticians wanting to improve their background in applicable methods, users of high-level statistical methods wanting to improve their background in fundamentals, and graduate students of statistics will all find this volume of value and use. |
| data analysis research topics: Your Statistical Consultant Rae R. Newton, Kjell Erik Rudestam, 2013 How do you bridge the gap between what you learned in your statistics course and the questions you want to answer in your real-world research? Oriented towards distinct questions in a How do I? or When should I? format, Your Statistical Consultant is the equivalent of the expert colleague down the hall who fields questions about describing, explaining, and making recommendations regarding thorny or confusing statistical issues. The book serves as a compendium of statistical knowledge, both theoretical and applied, that addresses the questions most frequently asked by students, researchers and instructors. Written to be responsive to a wide range of inquiries and levels of expertise, the book is flexibly organized so readers can either read it sequentially or turn directly to the sections that correspond to their concerns. |
| data analysis research topics: Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges Aboul Ella Hassanien, Ashraf Darwish, 2020-12-14 This book is intended to present the state of the art in research on machine learning and big data analytics. The accepted chapters covered many themes including artificial intelligence and data mining applications, machine learning and applications, deep learning technology for big data analytics, and modeling, simulation, and security with big data. It is a valuable resource for researchers in the area of big data analytics and its applications. |
| data analysis research topics: Practical Research Methods for Nonprofit and Public Administrators Elizabethann O'Sullivan, 2016-06-03 Organized around the four types of studies typically conducted by effective managers and programs, Practical Research Methods for Nonprofit and Public Administrators integrates traditional research methods topics with specific management applications. This unique text includes extensive end-of-chapter exercises highlighting the importance of qualitative methods and emphasizing practical skills managers should be able to easily and correctly apply. |
| data analysis research topics: Survival Skills for Thesis and Dissertation Candidates Robert S. Fleming, Michelle Kowalsky, 2021-09-14 This is a must-have preparation and reference guide for students embarking on the challenging journey of completing a thesis or dissertation. The authors, who are both “students of thesis and dissertation travel,” combine their expertise and insights to offer wise travel guidance designed to enhance both the success and satisfaction of this likely once-in-a-lifetime journey. The various chapters provide a realistic preview of how to prepare for and how to complete each stage of this travel journey successfully. Individual chapters on each of the major tasks each serve as an important reference for students to review as they progress, thus providing a guide which will be consulted many times throughout their program. The book provides advice on the most common aspects of the thesis or dissertation process, and it is written in a user-friendly manner designed to engage students and to enhance their comfort level as they journey through their candidacy. The importance of each task in the thesis or dissertation journey is addressed, along with its role in contributing to a successful outcome, and is accompanied by advice and suggestions from previous travellers. The challenges inherent in all stages of the journey are examined, along with proactive strategies for avoiding potential “bumps in the road.” You will not want to depart on this monumental travel adventure without this valuable survival guide! |
| data analysis research topics: The Handbook of Social Work Research Methods Bruce Thyer, 2009-10-15 Click on the Supplements tab above for further details on the different versions of SPSS programs. The canonical Handbook is completely updated with more student-friendly features The Handbook of Social Work Research Methods is a cutting-edge volume that covers all the major topics that are relevant for Social Work Research methods. Edited by Bruce Thyer and containing contributions by leading authorities, this Handbook covers both qualitative and quantitative approaches as well as a section that delves into more general issues such as evidence based practice, ethics, gender, ethnicity, International Issues, integrating both approaches, and applying for grants. New to this Edition More content on qualitative methods and mixed methods More coverage of evidence-based practice More support to help students effectively use the Internet A companion Web site at www.sagepub.com/thyerhdbk2e containing a test bank and PowerPoint slides for instructors and relevant SAGE journal articles for students. This Handbook serves as a primary text in the methods courses in MSW programs and doctoral level programs. It can also be used as a reference and research design tool for anyone doing scholarly research in social work or human services. |
| data analysis research topics: Researching Society and Culture Clive Seale, 2017-11-17 Written by internationally renowned experts, each chapter provides a full introduction to a key aspect of research methodology. From starting out to generating, analysing, and presenting data, this new edition covers foundational concepts in social research while also keeping students on the pulse of topics like digital social research, social surveys, and big data. Packed with international examples from across the social sciences, it shows how to interpret and work with data generated from real-world research. It gives you the tools to: - Design the right research question for your project - Access, understand, and use existing data - Effectively write up projects and assignments - Be confident in the A to Z of the research process Supported by an interactive website with videos, datasets, templates, and additional exercises, this book is the perfect hand-holder for any social science student starting a methods course or project. |
| data analysis research topics: Action Research for English Language Arts Teachers Mary Buckelew, Janice Ewing, 2019-03-13 Offering preservice and inservice teachers a guide to navigate the rapidly changing landscape of English Language Arts education, this book provides a fresh perspective on what it means to be a teacher researcher in ELA contexts. Inviting teachers to view inquiry and reflection as intrinsic to their identity and mission, Buckelew and Ewing walk readers through the inquiry process from developing an actionable focus, to data collection and analysis to publication and the exploration of ongoing questions. Providing thoughtful and relevant protocols and models for teacher inquiry, this book establishes a theoretical foundation and offers practical, ready-to-use tools and strategies for engaging in the inquiry process in the context of teachers’ communities. Action Research for English Language Arts Teachers: Invitation to Inquiry includes a variety of examples and scenarios of ELA teachers in diverse contexts, ensuring that this volume is relevant and accessible to all educators. |
| data analysis research topics: Microarray Image and Data Analysis Luis Rueda, 2018-09-03 Microarray Image and Data Analysis: Theory and Practice is a compilation of the latest and greatest microarray image and data analysis methods from the multidisciplinary international research community. Delivering a detailed discussion of the biological aspects and applications of microarrays, the book: Describes the key stages of image processing, gridding, segmentation, compression, quantification, and normalization Features cutting-edge approaches to clustering, biclustering, and the reconstruction of regulatory networks Covers different types of microarrays such as DNA, protein, tissue, and low- and high-density oligonucleotide arrays Examines the current state of various microarray technologies, including their availability and affordability Explains how data generated by microarray experiments are analyzed to obtain meaningful biological conclusions An essential reference for academia and industry, Microarray Image and Data Analysis: Theory and Practice provides readers with valuable tools and techniques that extend to a wide range of biological studies and microarray platforms. |
| data analysis research topics: Topology in Real-World Machine Learning and Data Analysis Kathryn Hess, Frédéric Chazal, Umberto Lupo, 2022-11-07 |
Thesis topics for the master thesis Data Science and Business …
This thesis is about a regression model for a continuous Y and many covariates. Variable selection (or feature selection) can be done in various ways. Especially in high-dimensional settings this is challenging when one wants certain guarantees about the selected model. The use of knockoffs guarantees that a … See more
Ten Research Challenge Areas in Data Science - Harvard Data …
Sep 30, 2020 · To drive progress in the field of data science, we propose 10 challenge areas for the research community to pursue.
Sample Course Syllabus for Research Methods, Data Analysis, …
The main purpose of the Research Methods, Data Analyisis, and Reporting to Support DoD Security Programs course is to introduce students to quantitative and qualitative methods for …
From data to big data in production research: the past and …
To identify the most popular topics on data research in production, tag clouds from the keywords of 289 papers for four different periods – 2005–2009, 2010–2013, 2014–2016 and from 2017 – …
Introduction to Data Analysis Handbook - ed
in Section V of the Handbook we examine data analysis using examples of data from each of the Head Start content areas. We explore examples of how data analysis could be done. We …
Data Analysis Case Studies - Data Action Lab
In this report, we provide examples of data analysis and quantitative methods applied to “real-life” problems. We emphasize qualitative aspects of the projects as well as significant results and …
15 Methods of Data Analysis in Qualitative Research
15 Methods of Data Analysis in Qualitative Research Compiled by Donald Ratcliff 1. Typology - a classification system, taken from patterns, themes, or other kinds of groups of data. (Patton …
Advanced Topics in Data Science - Pompeu Fabra University
research topics in Statistics that are relevant for Data Science, at a level sufficient to critically appraise, modify and apply novel methods, and improved oral and written presentation skills. …
Big data analytics for data-driven industry: a review of data …
To fill this gap, this paper makes the following contributions: presents an all-inclusive survey of current trends of BDA tools, methods, their strengths, and weaknesses. Identify and discuss …
Review of Data-centric Time Series Analysis from Sample, …
To fill the gap, in this paper, we systemati-cally review different data-centric methods in time series analysis, covering a wide range of research topics. Based on the time-series data …
Education big data and learning analytics: a bibliometric …
Data is taken from the Scopus database to answer the following research questions: What is the distribution of education big data and learning analytics publications in the years 2012–2021?...
An Overview of Data Analysis and Interpretations in Research
Data analysis proves to be crucial in this process, provides a meaningful base to critical decisions, and helps to create a complete dissertation proposal. So, after analyzing the data the result …
The SAGE Handbook of Qualitative Data Analysis - SAGE …
Qualitative data analysis is the classification and interpretation of linguistic (or visual) material to make statements about implicit and explicit dimensions and structures of meaning-making in …
Exploring research trends in big data - SAGE Journals
In this article, we applied topic modeling and word co-occurrence analysis methods to identify key topics from more than 36,000 big data publications across all academic disciplines between …
Secondary Data Analysis: Using existing data to answer new …
Mar 29, 2024 · Secondary data analysis is a valuable research approach that can be used to advance knowledge across many disciplines through the use of quantitative, qualitative, or …
Dissertation projects: introduction to secondary analysis for ...
• For quantitative projects, you’ll need to harmonize data to ensure validity • For qualitative projects, consider study-level context to ensure data are comparable
A Framework for Analysis of Data Quality Research
advance the research on data quality, however, one must first understand the work that has been conducted to date and iden- tify specific topics that merit further investigation. The objectives …
Secondary analysis of qualitative data: a valuable method for …
Therefore, this paper aims to illustrate the process of carrying out a secondary analysis of primary data collected using qualitative methods for the purpose of exploring a sensitive topic with an …
Applications of Topology to Data Analysis - CMI
This thesis aims to serve as an introduction to Topological Data Analysis (TDA), a collec-tion of methods that seek to quantify the topological and geometric features of data using algebraic …
Basic Concepts in Research and Data Analysis - SAS Support
After completing this chapter, you should be familiar with the fundamental issues and terminology of data analysis, and be prepared to learn about using JMP for data analysis. Research in the …
Thesis topics for the master thesis Data Science and …
Thesis topics for the master thesis Data Science and Business Analytics Topic 1: Logistic regression for modern data structures Promotor Gerda Claeskens Description Logistic …
Ten Research Challenge Areas in Data Science - Harvard …
Sep 30, 2020 · To drive progress in the field of data science, we propose 10 challenge areas for the research community to pursue.
Sample Course Syllabus for Research Methods, Data …
The main purpose of the Research Methods, Data Analyisis, and Reporting to Support DoD Security Programs course is to introduce students to quantitative and qualitative methods for …
From data to big data in production research: the past and …
To identify the most popular topics on data research in production, tag clouds from the keywords of 289 papers for four different periods – 2005–2009, 2010–2013, 2014–2016 and from 2017 – …
Introduction to Data Analysis Handbook - ed
in Section V of the Handbook we examine data analysis using examples of data from each of the Head Start content areas. We explore examples of how data analysis could be done. We …
Data Analysis Case Studies - Data Action Lab
In this report, we provide examples of data analysis and quantitative methods applied to “real-life” problems. We emphasize qualitative aspects of the projects as well as significant results and …
15 Methods of Data Analysis in Qualitative Research
15 Methods of Data Analysis in Qualitative Research Compiled by Donald Ratcliff 1. Typology - a classification system, taken from patterns, themes, or other kinds of groups of data. (Patton …
Advanced Topics in Data Science - Pompeu Fabra University
research topics in Statistics that are relevant for Data Science, at a level sufficient to critically appraise, modify and apply novel methods, and improved oral and written presentation skills. …
Big data analytics for data-driven industry: a review of data …
To fill this gap, this paper makes the following contributions: presents an all-inclusive survey of current trends of BDA tools, methods, their strengths, and weaknesses. Identify and discuss …
Review of Data-centric Time Series Analysis from Sample, …
To fill the gap, in this paper, we systemati-cally review different data-centric methods in time series analysis, covering a wide range of research topics. Based on the time-series data …
Education big data and learning analytics: a bibliometric …
Data is taken from the Scopus database to answer the following research questions: What is the distribution of education big data and learning analytics publications in the years 2012–2021?...
An Overview of Data Analysis and Interpretations in Research
Data analysis proves to be crucial in this process, provides a meaningful base to critical decisions, and helps to create a complete dissertation proposal. So, after analyzing the data the result …
The SAGE Handbook of Qualitative Data Analysis - SAGE …
Qualitative data analysis is the classification and interpretation of linguistic (or visual) material to make statements about implicit and explicit dimensions and structures of meaning-making in …
Exploring research trends in big data - SAGE Journals
In this article, we applied topic modeling and word co-occurrence analysis methods to identify key topics from more than 36,000 big data publications across all academic disciplines between …
Secondary Data Analysis: Using existing data to answer new …
Mar 29, 2024 · Secondary data analysis is a valuable research approach that can be used to advance knowledge across many disciplines through the use of quantitative, qualitative, or …
Dissertation projects: introduction to secondary analysis for ...
• For quantitative projects, you’ll need to harmonize data to ensure validity • For qualitative projects, consider study-level context to ensure data are comparable
A Framework for Analysis of Data Quality Research
advance the research on data quality, however, one must first understand the work that has been conducted to date and iden- tify specific topics that merit further investigation. The objectives …
Secondary analysis of qualitative data: a valuable method for …
Therefore, this paper aims to illustrate the process of carrying out a secondary analysis of primary data collected using qualitative methods for the purpose of exploring a sensitive topic with an …
Applications of Topology to Data Analysis - CMI
This thesis aims to serve as an introduction to Topological Data Analysis (TDA), a collec-tion of methods that seek to quantify the topological and geometric features of data using algebraic …
Basic Concepts in Research and Data Analysis - SAS Support
After completing this chapter, you should be familiar with the fundamental issues and terminology of data analysis, and be prepared to learn about using JMP for data analysis. Research in the …
