Interrogate Data: Data Exploration and Analysis

First published Open Access under a Creative Commons license as What is Quantitative Longitudinal Data Analysis?, this title is now also available as part of the Bloomsbury Research Methods series. Across the social sciences, there is widespread agreement that quantitative longitudinal research designs offer analysts powerful scientific data resources. But, to date, many texts on analysing longitudinal social analysis surveys have been written from a statistical, rather than a social science data analysis perspective and they lack adequate coverage of common practical challenges associated with social science data analyses. This book provides a practical and up-to-date introduction to influential approaches to quantitative longitudinal data analysis in the social sciences. The book introduces definitions and terms, explains the relative attractions of such a longitudinal design, and offers an introduction to the main techniques of analysis, explaining their requirements, statistical properties and their substantive contribution.

Covering the general process of data analysis to finding, collecting, organizing, and presenting data, this book offers a complete introduction to the fundamentals of data analysis. Using real-world case studies as illustrations, it helps readers understand theories behind and develop techniques for conducting quantitative, qualitative, and mixed methods data analysis. With an easy-to-follow organization and clear, jargon-free language, it helps readers not only become proficient data analysts, but also develop the critical thinking skills necessary to assess analyses presented by others in both academic research and the popular media. It includes advice on: - Data analysis frameworks - Validity and credibility of data - Sampling techniques   - Data management - The big data phenomenon   - Data visualisation - Effective data communication Whether you are new to data analysis or looking for a quick-reference guide to key principles of the process, this book will help you uncover nuances, complexities, patterns, and relationships among all types of data.

Now in its second edition, this introductory statistics textbook conveys the essential concepts and tools needed to develop and nurture statistical thinking. It presents descriptive, inductive and explorative statistical methods and guides the reader through the process of quantitative data analysis. This revised and extended edition features new chapters on logistic regression, simple random sampling, including bootstrapping, and causal inference. The text is primarily intended for undergraduate students in disciplines such as business administration, the social sciences, medicine, politics, and macroeconomics. It features a wealth of examples, exercises and solutions with computer code in the statistical programming language R, as well as supplementary material that will enable the reader to quickly adapt the methods to their own applications.

Qualitative Data Analysis with ATLAS.ti is the very first book designed to guide you step-by-step through your research project using ATLAS.ti. In the book, you will find clear, practical advice on preparing your data, setting up a new project in ATLAS.ti, developing a coding system, asking questions, finding answers and preparing your results. The book features: - methodological as well as technical advice - numerous practical exercises and examples - screenshots showing you each stage of analysis - a companion website with online tutorials and data sets Susanne Friese teaches qualitative methods at the University of Hanover and at various PhD schools, provides training and consultancy for ATLAS.ti at the intersection between developers and users.

A one-stop-shop for students new to qualitative data analysis! In this fully updated and expanded Second Edition, Carol Grbich provides a guide through current issues in the analysis of qualitative data. Packed with detailed examples, a glossary, further reading lists and a section on writing up, this easy to read text is exactly what you need to get you started in qualitative research.  

The wide range of approaches to data analysis in qualitative research can seem daunting even for experienced researchers. This handbook is the first to provide a state-of-the art overview of the whole field of QDA; from general analytic strategies used in qualitative research, to approaches specific to particular types of qualitative data, including talk, text, sounds, images and virtual data. The handbook includes chapters on traditional analytic strategies such as grounded theory, content analysis, hermeneutics, phenomenology and narrative analysis, as well as coverage of newer trends like mixed methods, reanalysis and meta-analysis. Practical aspects such as sampling, transcription, working collaboratively, writing and implementation are given close attention, as are theory and theorization, reflexivity, and ethics. Written by a team of experts in qualitative research from around the world, this handbook is an essential compendium for all qualitative researchers and students across the social sciences.

In this important new text, Paul Sullivan introduces readers to a qualitative methodology rooted in the analysis of dialogue and subjectivity: the dialogical approach. Sullivan unpacks the theory behind a dialogical approach to qualitative research, and relates issues of philosophy and methodology to the practical process of actually doing qualitative research. Sullivan′s book foregrounds the role of atmosphere, subjectivity and authorial reflection within texts. His work also enables the researcher to attend to the conflicts, judgments and interpretive activities that take place in language use. Practically speaking, the dialogical approach enables analysis of direct and indirect discourse, speech genres, hesitations, irony and a variety of other conditions that shape our understanding of dialogue in context. As well as exploring the theory behind this innovative method, Sullivan provides sound practical advice that recognises the everyday analytic needs of the reader. Topics include: - The theoretical foundations of the approach - The role of subjectivity in qualitative research - Data preparation and analysis - The future of the approach Theoretical discussion is consistently accompanied by research examples and suggestions as to how the dialogical approach could be used in the reader′s own research. This important and timely book is ideal for any reader who wants to do research with dialogue and who is keen to attend to the full nuances and complexities of discourse.

The first volume to introduce the techniques and methods of reading digital material for research Digital Humanities has become one of the new domains of academe at the interface of technological development, epistemological change, and methodological concerns. This volume explores how digital material might be read or utilized in research, whether that material is digitally born as fanfiction, for example, mostly is, or transposed from other sources. The volume asks questions such as what happens when text is transformed from printed into digital matter, and how that impacts on the methods we bring to bear on exploring that technologized matter, for example in the case of digital editions. Issues such as how to analyse visual material in digital archives or Twitter feeds, how to engage in data mining, what it means to undertake crowd-sourcing, big data, and what digital network analyses can tell us about online interactions are dealt with. This will give Humanities researchers ideas for doing digitally based research and also suggest ways of engaging with new digital research methods. Key features First volume centred on the navigation and interpretation of digital material as research methods in the Humanities Up-to-date analyses of issues and methods including big data, crowdsourcing, digital network analysis, working with digital additions Based on actual research projects such as para-textual work with fanfiction, reading twitter, different kinds of distant and close readings

This book will introduce digital humanists at all levels of education to Python. It provides background and guidance on learning the Python computer programming language, and as it presumes no knowledge on the part of the reader about computers or coding concepts allows the reader to gradually learn the more complex tasks that are currently popular in the field of digital humanities. This book will be aimed at undergraduates, graduates, and faculty who are interested in learning how to use Python as a tool within their workflow. An Introduction to Python for Digital Humanists will act as a primer for students who wish to use Python, allowing them to engage with more advanced textbooks. This book fills a real need, as it is first Python introduction to be aimed squarely at humanities students, as other books currently available do not approach Python from a humanities perspective. It will be designed so that those experienced in Python can teach from it, in addition to allowing those who are interested in being self-taught can use it for that purpose. Key Features: Data analysis Data science Computational humanities Digital humanities Python Natural language processing Social network analysis App development

Advancing Digital Humanities moves beyond definition of this dynamic and fast growing field to show how its arguments, analyses, findings and theories are pioneering new directions in the humanities globally.

This book covers computationally innovative methods and technologies including data collection and elicitation, data processing, data analysis, data visualizations, and data presentation. It explores how digital humanists have harnessed the hypersociality and social technologies, benefited from the open-source sharing not only of data but of code, and made technological capabilities a critical part of humanities work.  Chapters are written by researchers from around the world, bringing perspectives from diverse fields and subject areas. The respective authors describe their work, their research, and their learning. Topics include semantic web for cultural heritage valorization, machine learning for parody detection by classification, psychological text analysis, crowdsourcing imagery coding in natural disasters, and creating inheritable digital codebooks. Designed for researchers and academics, this book is suitable for those interested in methodologies and analytics that can be applied in literature, history, philosophy, linguistics, and related disciplines. Professionals such as librarians, archivists, and historians will also find the content informative and instructive.