Qualitative and Mixed Methods Data Analysis Using Dedoose®: A Practical Approach for Research Across the Social Sciences 2nd Revised edition [Pehme köide]

Foreword by Eli Lieber
Preface
Acknowledgements
Glossary: Dedoose Common Terms
About the Authors
Part I: Foundations of Research
Chapter 1: Introduction
1.1 Overview of the Book
1.2 What is Dedoose?
1.3 Course Adoption
1.4 Dedoose for Literature Review: A Guide for Researchers
1.5 Appendix: Keyboard Shortcuts
Chapter 2: Qualitative Data Analysis
2.1 Framing the study
2.2 Aligning Theory to the Analytic Approach
2.3 Getting Started with the Analysis of Raw Data
2.4 Building Connections and Finding Relationships
2.5 Analytic Rabbit holes
Chapter 3: Mixed Methods Data Analysis
3.1 Mixed Methods and Mixed Paradigms
3.2 Identifying Mixed Methods Analysis Strategies
3.3 Preparing for Mixed Methods Analysis
Chapter 4: Data Management
4.1 Gathering data
4.2 Numbers as data
4.3 Memos as data
4.4 Preparing data for import
4.5 Conclusion
Appendix: Types of interview data
Part II: Data Interaction and Analysis
Chapter 5: Doing Qualitative Analysis in Dedoose
5.1 Working with media and excerpts in Dedoose
5.2 Working with codes in Dedoose
5.3 Memos in Dedoose
5.4 Qualitative coding tips
5.5 Conclusion
Chapter 6: Doing Mixed Methods Analysis in Dedoose
6.1 Working with Numeric and Categorical Data
6.2 Recognizing and Managing Complexity in Analysis
6.3 Data Complexity in Your Project
6.4 Mixed methods code tips | Integrating mixed methods data during
analysis
6.5 Mixing Qualitative and Quantitative Data by Hannah Calvert
6.6 Conclusion
Chapter 7: Analysis Through Visualization
7.1 Using Visualization Tools for Analysis
7.2 Code Charts
7.3 Code and Descriptor Charts
7.4 Descriptor Charts
7.5 Moving Through and Filtering Your Data
7.6 Conclusion
Chapter 8: Advanced Tools and Automation in Dedoose
8.1 Advanced Codebook Management
8.2 Text Analytics
8.3 Automation Tools in Dedoose
8.4 Using Artificial Intelligence
8.5 Summary
Chapter 9: Teamwork Analysis Techniques
9.1 Team development
9.2 Collaborative Interpretations
9.3 Team Guidelines
Chapter 10: Collaborating Successfully in Dedoose
10.1 When to Work with Others
10.2 Approaches to Team Coding in Dedoose
10.3 Developing a Team Coding Process | Tips and Guidelines
10.4 Conclusion
10.5 Appendix | Access Group Categories in Dedoose
Conclusion to Part Two: Data Interaction and Analysis
Part III: Reporting Credible Results and Sharing Findings
Chapter 11: Sharing Data with a Larger Audience
11.1 Reaching a Larger Audience
11.2 Sharing Qualitative Social Science Data by QDR
11.3 Data Anonymization by Hannah Calvert
11.4 Changing Reporting Practices: Open Access
11.5 Conclusion
Chapter 12: Reporting Your Findings
12.1 Reaching Your Audience
12.2 Qualitative Methods Procedural Checklist
12.3 Mixed Methods Procedural Checklist
12.4 Reporting to Multiple Audiences
12.5 Effective Research Communication Across Diverse Audiences
Chapter 13: Qualitative Analysis and AI: What does the future hold?
13.1 Introduction
13.2 Qualitative Practices Shifting from Past to Present
13.3 AI Adoption in Qualitative Analysis
13.4 An Epistemological Conundrum
13.5 Overcoming Limitations of AI
13.6 Building a Framework for the Future
Chapter 14: Ending the Book
14.1 Navigating the Evolving Landscape of Research
14.2 Revisiting Our Path
14.3 Key Takeaways
14.4 The Road Ahead
14.5 Final Word
Afterword
References
Index

Dr. Michelle Salmona serves as President (co-founder) of the Institute for Mixed Methods Research (IMMR) with an academic appointment as Adjunct Professor at the University of Canberra, Australia. She has authored multiple books and academic papers including her book co-authored with Dan Kaczynski and Eli Lieber Qualitative and Mixed Methods Data Analysis Using Dedoose: A Practical Approach for Research Across the Social Sciences. Michelle has been working for over 20 years as a mentor in writing about strong research, and a teacher in qualitative data analysis and the use of Qualitative Data Analysis Software (QDAS). In addition, she is a credentialed project management professional (PMP) and senior fellow of the Higher Education Academy, United Kingdom.

Michelle is a specialist in qualitative and mixed methods research design and analysis, and works as an international consultant in: program evaluation; research design; and mixed-methods and qualitative data analysis using digital tools. Her research focus is to better understand how to support doctoral success and strengthen the research process; and build data-driven decision-making capacity through technological innovation. Recent research includes exploring the changing practices of qualitative research during the dissertation phase of doctoral studies, and investigating how we bring learning into the use of technology during the research process. Michelle is currently working on projects with researchers from education, information systems, business communication, leadership, and finance.



Professor Dan Kaczynski is Professor Emeritus at Central Michigan University and a senior research fellow at the IMMR. He is currently an adjunct professor supervising doctoral candidates at the University of Canberra, Australia. His research interests promote technological innovations in qualitative and mixed meth­ods data analysis in the social sciences in the United States and Australia.



Dan is a program evaluation consultant and has more than 20 years experience conducting state, national, and international evaluations. Leadership roles include K-12 and higher education administration and research center director with extensive experience as principal investigator of more than $35 million in grant awards. His work has been shared professionally with more than 250 professional presentations nationally and internationally. He has written more than 50 published research articles and eight books and book chapters. In addition, he has supervised over 100 doctoral dissertations and professional specialist theses.