Data Management in Large-Scale Education Research

Research data management is becoming more complicated. Researchers are collecting more data, using more complex technologies, all the while increasing the visibility of our work with the push for data sharing and open science practices. Ad hoc data management practices may have worked for us in the past, but now others need to understand our processes as well, requiring researchers to be more thoughtful in planning their data management routines. This book is for anyone involved in a research study involving original data collection. While the book focuses on quantitative data, typically collected from human participants, many of the practices covered can apply to other types of data as well. The book contains foundational context, instructions, and practical examples to help researchers in the field of education begin to understand how to create data management workflows for large-scale, typically federally funded, research studies. The book starts by describing the research life cycle and how data management fits within this larger picture. The remaining chapters are then organized by each phase of the life cycle, with examples of best practices provided for each phase. Finally, considerations on whether the reader should implement, and how to integrate those practices into a workflow, are discussed. Key Features: Provides a holistic approach to the research life cycle, showing how project management and data management processes work in parallel and collaboratively Can be read in its entirety, or referenced as needed throughout the life cycle Includes relatable examples specific to education research Includes a discussion on how to organize and document data in preparation for data sharing requirements Contains links to example documents as well as templates to help readers implement practices

Table of Contents:
1. Introduction 2. Research Data Management Overview 3. Data Organization 4. Human Subjects Data 5. Data Management Plan 6. Planning Data Management 7. Project Roles and Responsibilities 8. Documentation 9. Style Guide 10. Data Tracking 11. Data Collection 12. Data Capture 13. Data Storage and Security 14. Data Cleaning 15. Data Archiving 16. Data Sharing 17. Additional Considerations. Glossary. Appendix. References.

About the Author :
Crystal Lewis is an independent research data management consultant (cghlewis.com). Her experience spans the research life cycle including collecting, curating, sharing, and analyzing data, particularly for federally funded research studies. She is happiest working at the intersection of education research and data management planning, helping researchers build and implement organized processes that lead to more secure, reliable, and usable data.

Review :
"Imagine having a seasoned researcher's best advice at your fingertips—--this is precisely what this book offers. It's a well-crafted blend of practical know-how and scholarly wisdom, perfect for anyone about to or currently managing research data. With clarity and a touch of scholarly flair, Crystal Lewis transforms complex concepts into clear, actionable steps. It's packed with real-world examples, straightforward templates, and checklists that take the guesswork out of data management. Whether you're a seasoned investigator, a data manager, or a student stepping into the world of research, this book will undoubtedly become a staple in your professional toolkit, enhancing the quality and impact of your work. This book is a resource that speaks your language, understands your challenges, and respects the depth of your work. In summary, this book is not just a guide; it's a mentor in print form." Joscelin Rocha-Hidalgo, Pennsylvania State University "Data Management in Large-Scale Education Research is a game changer for education researchers wanting to improve their research practices. The book seamlessly blends theory and practice, offering a pragmatic guide for ethically and systematically managing and sharing data." Crystal Steltenpohl, Center for Open Science "This book is a direct answer to the critical need for practical and accessible resources for education researchers who are managing primary data collection. Crystal Lewis introduces robust methods for the collection and management of data within the broader research context. Readers will acquire knowledge and confidence in leading large-scale studies, as well as skills and strategies they can implement in their work immediately. This book should be on the shelves of all graduate students, project managers, data managers, and PIs conducting research in education!" Leigh McLean, Center for Research on Educational and Social Policy, University of Delaware "Education researchers: do not skip this book! Crystal Lewis gives you the tools you need to manage your data better in order to make your project run smoothly." Kristin Briney, Biology Librarian, California Institute of Technology, and author of “Data Management for Researchers: Organize, Maintain and Share Your Data for Research Success” "An outstanding guide for those learning how to manage large-scale data in the social sciences. The book embraces the tenets of open science, highlighting the importance of reproducible science." Lexi Swanz, Vanderbilt University "Crystal Lewis has translated years of experience in applied research into a practical and indispensable guide to all aspects of data management across the educational research cycle. This book provides everything you need to perfect your data management practices and engage in research that is consistent with open science principles and the ever evolving demands of grant funding agencies." Dan Cohen, SRI International "This book is a treasure for those in the field of Education and beyond. Many practicing statisticians and industry data scientists have never endured the process of collecting their own data, and statistical education largely assumes away such complexities, taking complete and correct data as a given. Lewis' incredibly clear and readable book can be a revelation to data analysts on the complexity, nuances, and subjectivity that goes into data collection. This framework will undoubtedly help many consider how to handle bias and complexity in their own analysis and better manage their derivative data products with the level of care that Lewis coaches her readers to give." Emily Riederer, Capital One, and author of “R Markdown Cookbook”