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

lgrsnf/Data Management in Large-Scale Education Research (2025).pdf

zlib/no-category/Crystal Lewis/Data Management in Large-Scale Education Research_29380561.pdf

Verlag Jugend & Volk GmbH

Taylor & Francis Ltd

CRC Press LLC

United Kingdom and Ireland, United Kingdom

CRC Press (Unlimited), [S.l.], 2024

Austria, Austria

1, PS, 2024

Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
About the Author
Acknowledgments
Chapter 1: Introduction
1.1 Why This Book?
1.1.1 Lack of Training, Resources, and Standards
1.1.2 Consequences
1.2 About This Book
1.2.1 What This Book Will Cover
1.2.2 What This Book Will Not Cover
1.3 Who This Book Is For
1.4 Final Note
Chapter 2: Research Data Management Overview
2.1 What Is Research Data Management?
2.2 Data Management Standards
2.3 Why Care about Research Data Management?
2.3.1 External Reasons
2.3.2 Personal Reasons
2.4 Existing Frameworks
2.4.1 FAIR
2.4.2 SEER
2.4.3 Open Science
2.5 Terminology
2.6 The Research Life Cycle
Chapter 3: Data Organization
3.1 Basics of a Dataset
3.1.1 Columns
3.1.1.1 Column Attributes
3.1.2 Rows
3.1.3 Cells
3.2 Dataset Organization Rules
3.3 Linking Data
3.3.1 Database Design
3.3.1.1 Horizontal Joins
3.3.1.2 Vertical Joins
3.3.2 Data Structure
3.3.2.1 Wide Format
3.3.2.2 Long Format
3.3.2.3 Choosing Wide versus Long
Notes
Chapter 4: Human Subjects Data
4.1 Identifiability of a Dataset
4.2 Data Classification
4.3 Human Subjects Data Oversight
4.3.1 Regulations and Laws
4.3.2 Institutions and Departments
4.3.3 External Permission
4.3.4 Agreements
4.3.5 Funders
4.4 Protecting Human Subjects Data
Notes
Chapter 5: Data Management Plan
5.1 History and Purpose
5.1.1 Why Are DMPs Important?
5.2 What Is It?
5.2.1 What to Include?
5.3 Creating a Data Sources Catalog
5.4 Getting Help
5.5 Budgeting
Notes
Chapter 6: Planning Data Management
6.1 Why Spend Time on Planning?
6.2 Goals of Planning
6.3 Planning Checklists
6.3.1 Decision-Making Process
6.3.2 Checklist Considerations
6.4 Data Management Workflow
6.4.1 Benefits to Visualizing a Workflow
6.4.2 Workflow Considerations
6.5 Task Management Systems
Chapter 7: Project Roles and Responsibilities
7.1 Research Project Roles
7.1.1 Investigators
7.1.2 Project Coordinator
7.1.3 Data Manager
7.1.4 Project Team Members
7.1.5 Other Roles
7.2 Assigning Roles and Responsibilities
7.3 Documenting Roles and Responsibilities
Chapter 8: Documentation
8.1 Team-Level
8.1.1 Lab Manual
8.1.2 Wiki
8.1.3 Onboarding and Offboarding
8.1.4 Data Inventory
8.1.5 Team Data Security Policy
8.1.6 Style Guide
8.2 Project-Level
8.2.1 Data Management Plan
8.2.2 Data Sources Catalog
8.2.3 Checklists and Meeting Notes
8.2.4 Roles and Responsibilities Document
8.2.5 Research Protocol
8.2.6 Supplemental Documents
8.2.6.1 Timeline
8.2.6.2 Participant Flow Diagram
8.2.6.3 Instruments
8.2.6.4 Flowchart of Data Collection Instruments
8.2.6.5 Consent Forms
8.2.7 Standard Operating Procedures
8.3 Dataset-Level
8.3.1 Readme
8.3.2 Changelog
8.3.3 Data Cleaning Plan
8.4 Variable-Level
8.4.1 Data Dictionary
8.4.1.1 Creating a Data Dictionary for an Original Data Source
8.4.1.2 Creating a Data Dictionary from an Existing Data Source
8.4.1.3 Time Well Spent
8.4.2 Codebook
8.5 Repository Metadata
8.5.1 Metadata Standards
8.6 Wrapping It Up
Notes
Chapter 9: Style Guide
9.1 General Good Practices
9.2 Directory Structure
9.3 File Naming
9.4 Variable Naming
9.4.1 Time
9.5 Value Coding
9.5.1 Missing Value Coding
9.6 Coding
Note
Chapter 10: Data Tracking
10.1 Benefits
10.2 Building Your Database
10.2.1 Comparing Database Types
10.2.1.1 Relational Database
10.2.1.2 Non-Relational Database
10.2.2 Designing the Database
10.2.3 Choosing Fields
10.2.3.1 Structuring Fields
10.2.4 Choosing a Tool
10.3 Entering Data
10.3.1 Entering Data in a Tabular View
10.3.2 Entering Data in a Form
10.4 Creating Unique Identifiers
10.5 Summary
Notes
Chapter 11: Data Collection
11.1 Quality Assurance and Control
11.2 Quality Assurance
11.2.1 Questionnaire Design
11.2.2 Pilot the Instrument
11.2.3 Choose Quality Data Collection Tools
11.2.4 Build with the End in Mind
11.2.4.1 Electronic Data Collection
11.2.4.2 Paper Data Collection
11.2.4.3 Identifiers
11.2.4.3.1 Electronic Data
11.2.4.3.2 Paper Data
11.2.5 Ensure Compliance
11.2.5.1 Building Consent Forms
11.3 Quality Control
11.3.1 Field Data Management
11.3.2 Ongoing Data Checks
11.3.3 Tracking Data Collection
11.3.4 Collecting Data Consistently
11.4 Bot Detection
11.5 Review
Notes
Chapter 12: Data Capture
12.1 Electronic Data Capture
12.1.1 Documenting Electronic Data Capture
12.2 Paper Data Capture
12.2.1 Choose a Quality Data Entry Tool
12.2.2 Build with the End in Mind
12.2.3 Develop a Data Entry Procedure
12.2.3.1 Double Entry
12.2.4 Documenting Paper Data Capture
12.2.5 Scanning Forms
12.3 Extant Data
12.3.1 Non-Public Data Sources
12.3.2 Public Data Sources
12.3.3 Documenting External Data Capture
Note
Chapter 13: Data Storage and Security
13.1 Planning Short-Term Data Storage
13.1.1 Electronic Data
13.1.2 Paper Data
13.1.3 Oversight
13.2 Documentation and Dissemination
Chapter 14: Data Cleaning
14.1 Data Cleaning for Data Sharing
14.2 Data Quality Criteria
14.3 Data Cleaning Checklist
14.3.1 Checklist Steps
14.4 Data Cleaning Workflow
14.4.1 Preliminary Steps
14.4.2 Cleaning Data Using Code
14.4.3 Cleaning Data Manually
14.4.4 Data Versioning Practices
Note
Chapter 15: Data Archiving
15.1 Long-Term Storage
15.1.1 Paper Data
15.1.2 Electronic Data
15.1.3 Oversight and Documentation
15.2 Internal Data Use
15.3 Using a Repository
Notes
Chapter 16: Data Sharing
16.1 Why Share Your Data?
16.2 Data Sharing Flow Chart
16.2.1 Are You Able to Share?
16.2.2 Where to Share?
16.2.2.1 Choosing a Repository
16.2.3 What Data to Share
16.2.3.1 Processing of Files
16.2.3.2 Organizing Files
16.2.3.3 File Formats
16.2.3.4 Assess Disclosure Risk
16.2.3.4.1 Mitigating Disclosure Risk
16.2.3.4.2 Sharing Controlled Access Data
16.2.4 What Documentation to Share
16.2.4.1 File Formats
16.2.5 When to Share
16.3 Repository File Structure
16.4 Roles and Responsibilities
16.5 Revisions
Notes
Chapter 17: Additional Considerations
17.1 Multi-Site Collaborations
17.2 Multi-Project Teams
17.3 Summary
Glossary
Appendix
References
Index

Research data management is becoming more complicated. Researchers are collecting more data, using more complex technologies, all 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 begins 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
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, as well as 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.

This book is for those involved in a research study involving original data collection. Whilst it focuses on quantitative data, collected from human participants, many of the practices covered apply to other types of data. It contains foundational context, instructions, and examples.

2024-05-30