Data Management in Large-Scale Education Research

Crystal Lewis

• Publisher: Chapman and Hall/CRC
• Year: 2024
• ISBN:
  9781040045824
• Edition: 1
• Topic: Mathematics
• Subtopic: Probability & Statistics
• Index: Mathematics

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

• Cover Page
• Half Title page
• Title Page
• Copyright Page
• Dedication
• Contents
• About the Author
• Acknowledgments
• 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
• Index