Atlas.ti is used for collaborative qualitative analysis of text and audiovisual data. The software uses OpenAI to power its analytical tools.
Qualitative Data Analysis with ATLAS. ti
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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.
Covidence is an online tool that makes scoping reviews and the systematic review process easier.
Piecing Together Systematic Reviews and Other Evidence Syntheses
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Systematic reviews and other evidence syntheses have a vital role in summarizing the literature, exploring gaps in research, prioritizing new research, and providing literature to support decision-making and evidence-based practices. Librarians adapt their practices as members of the higher education and research community. If they consult and teach with researchers, faculty, and students, review methods will likely be a part of their work. Piecing Together Systematic Reviews and Other Evidence Syntheses: A Guide for Librarians aims to be the definitive text on systematic reviews for librarians, information professionals, and expert searchers. Starting with an introduction to evidence syntheses, the book follows the acronym PIECCESS, a framework for the 8 phases which flow through 8 processes. The 8 phases are (1) Proposal of scope; (2) Protocol registration; (3) Preliminary findings; (4) Paper completion; (5) Preserve project; (6) Promote to stakeholders; (7) Impact compilation; (8) Updating the review. The 8 processes are Plan, Identify, Evaluate, Collect, Combine, Explain, Summarize, and Share. After the processes of a review project are covered, guidance for developing and running a service is provided as well as teaching reviews and training for librarians. The intended audience for this book is any librarian interested in consulting, collaborating, completing, or teaching reviews. It has several applications: for training librarians new to reviews, for those developing a new review service, for those wanting to establish policies for current service, and as a reference for those conducting reviews or running a service. Participating in reviews is a new frontier of librarianship, with expanded opportunities for new service, research areas, and professional activities. This book is part of the effort to standardize best practices when engaging in evidence syntheses.
Systematic Reviews and Meta-Analysis
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When used in tandem, systematic reviews and meta-analysis-- two distinct but highly compatible approaches to research synthesis-- form a powerful, scientific approach to analyzing previous studies. But to see their full potential, a social work researcher must be versed in the foundational processes underlying them. This pocket guide to Systematic Reviews and Meta-Analysis illuminates precisely that practical groundwork. In clear, step-by-step terms, the authors explain how to format topics, locate and screen studies, extract and assess data, pool effect sizes, determine bias, and interpret the results, showing readers how to combine reviewing and meta-analysis correctly and effectively. Each chapter contains vivid social work examples and concludes with a concise summary and notes on further reading, while the book's glossary and handy checklists and sample search and data extraction forms maximize the boo'ks usefulness. Highlighting the concepts necessary to understand, critique, and conduct research synthesis, this brief and highly readable introduction is a terrific resource for students and researchers alike.
Dedoose is used for collaborative mixed methods and qualitative analysis. It allows data to be organized and analyzed, no matter what form the data has.
Qualitative and Mixed Methods Data Analysis Using Dedoose
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Qualitative and Mixed Methods Data Analysis Using Dedoose®: A Practical Approach for Research Across the Social Sciencesprovides both new and experienced researchers with a guided introduction to dealing with the methodological complexity of mixed methods and qualitative inquiry using Dedoose® software. The authors use their depth of experience designing and updating Dedoose® as well as their published research to give the reader practical strategies for using Dedoose® from a wide range of research studies. Case study contributions by outside researchers provide readers with rich examples of how to use Dedoose® in practical, applied social science and health settings.
Social Data Analysis
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Social data analysis enables you, as a researcher, to organize the facts you collect during your research. Your data may have come from a questionnaire survey, a set of interviews, or observations. They may be data that have been made available to you from some organization, national or international agency or other researchers. Whatever their source, social data can be daunting to put together in a way that makes sense to you and others. This book is meant to help you in your initial attempts to analyze data. In doing so it will introduce you to ways that others have found useful in their attempts to organize data. You might think of it as like a recipe book, a resource that you can refer to as you prepare data for your own consumption and that of others. And, like a recipe book that teaches you to prepare simple dishes, you may find this one pretty exciting. Analyzing data in a revealing way is at least as rewarding, we’ve found, as it is to cook up a yummy cashew carrot paté or a steaming corn chowder. We’d like to share our pleasure with you.
Kinme is a free, open source data analytics and data science tool. It lets the user build data workflows with any complexity using drag-and-drop, no-code visual programming.
MAXQDA is used for qualitative and mixed methods analysis of text and audiovisual data.
Microsoft Power BI
Nvivo is used for collaborative qualitative analysis of text and audiovisual data.
Qualitative Data Analysis with NVivo
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Engaging and accessible, this book offers students a complete guide to using NVivo for qualitative data analysis. Drawing on their wealth of expertise, the authors offer detailed, practical advice that relates to students' own experience and research projects. Packed with real-world examples and case studies, the book supports students through every stage of qualitative data analysis. The Third Edition: Contains fully integrated instructions for using NVivo on both Mac and PC, with screenshots and click-by-click guidance. Seamlessly interweaves theory and practice in easy-to-follow steps. Empowers students to develop their critical thinking. Accompanied by video tutorials for both Mac and PC, web links and a host of other helpful online resources, this step-by-step book removes students′ anxiety about tackling data analysis. Whether for advanced researchers or those approaching the task for the first time, this clear, yet comprehensive guide is the perfect companion for anyone doing qualitative data analysis with NVivo.
R is a programming language that was developed to teach introductory statistics. R has become a very popular language for statistical computing and data visualization.
R for Data Science
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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. You can also access the book at https://r4ds.hadley.nz/, where it is made available by the authors through a Creative Commons license.
Bookdown; authoring books and technical documents with R Markdown
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bookdown: Authoring Books and Technical Documents with R Markdown presents a much easier way to write books and technical publications than traditional tools such as LaTeX and Word. The bookdown package inherits the simplicity of syntax and flexibility for data analysis from R Markdown, and extends R Markdown for technical writing, so that you can make better use of document elements such as figures, tables, equations, theorems, citations, and references. Similar to LaTeX, you can number and cross-reference these elements with bookdown. Your document can even include live examples so readers can interact with them while reading the book. The book can be rendered to multiple output formats, including LaTeX/PDF, HTML, EPUB, and Word, thus making it easy to put your documents online. The style and theme of these output formats can be customized. We used books and R primarily for examples in this book, but bookdown is not only for books or R. Most features introduced in this book also apply to other types of publications: journal papers, reports, dissertations, course handouts, study notes, and even novels. You do not have to use R, either. Other choices of computing languages include Python, C, C++, SQL, Bash, Stan, JavaScript, and so on, although R is best supported. You can also leave out computing, for example, to write a fiction. This book itself is an example of publishing with bookdown and R Markdown, and its source is fully available on GitHub.
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