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El. knyga: Qualitative and Mixed Methods Data Analysis Using Dedoose: A Practical Approach for Research Across the Social Sciences

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  • Formatas: PDF+DRM
  • Išleidimo metai: 16-Aug-2019
  • Leidėjas: SAGE Publications Inc
  • Kalba: eng
  • ISBN-13: 9781506397825
  • Formatas: PDF+DRM
  • Išleidimo metai: 16-Aug-2019
  • Leidėjas: SAGE Publications Inc
  • Kalba: eng
  • ISBN-13: 9781506397825

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"Qualitative and Mixed Methods Data Analysis using Dedoose will provide 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 significant research experience to give the reader practical strategies for using Dedoose from a wide range of research studies. Qualitative and Mixed Methods Data Analysisusing Dedoose walks researchers, students and evaluators through designing a study, conducting fieldwork and reporting credible findings. In the first section the book gives a quick overview of qualitative and mixed methods research and designing studiesto work easily with available software, including Dedoose. The authors pay significant attention to data analysis in the second section, addressing the challenges of working in teams, working with just qualitative data, and analyzing qualitative and quantitative data in a mixed method study. The final section is devoted to reporting results and data visualization within Dedoose. Throughout the book, case studies are presented to illustrate the topics discussed with real research examples. Working throughthis book will give researchers improved technological skills to use Dedoose effectively in their research"--

Qualitative and Mixed Methods Data Analysis Using Dedoose®: A Practical Approach for Research Across the Social Sciences by Michelle Salmona, Eli Lieber, Dan Kaczynski provides 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.


Recenzijos

"Extremely helpful information that will inspire and educate both those who are just learning and those who have been using Dedoose® for years." -- Julie Kugel "Great overview of Dedoose® tools and mixed methods functionality make this a great book for beginners." -- Shaunna Smith

Foreword xii
Lyn Richards
Preface xv
Acknowledgments xvii
Glossary: Dedoose Common Terms xviii
About the Authors xxi
PART I FOUNDATIONS OF MIXED METHODS RESEARCH
1(66)
Chapter 1 Using Mixed Methods and Dedoose
3(9)
1.1 About This Book
3(2)
1.2 Mixed Methods and Mixed Paradigms
5(2)
1.2.1 Mixed Methods Considerations
6(1)
1.2.2 Combining Paradigms
6(1)
1.3 Using Cloud Technology to Support Mixed Methods Research
7(1)
1.4 What Is Dedoose?
8(1)
1.4.1 Relational Database
9(1)
1.4.2 Research and Evaluation Data Applications
9(1)
1.5 Dedoose: A Historical Journey
9(3)
Chapter 2 Adopting Dedoose
12(33)
2.1 Successful Adoption of Digital Tools
12(4)
2.1.1 Trusting a Digital Tool
13(1)
2.1.2 Building Successful Skills
14(1)
2.1.3 Strategies for Successful Adoption of Dedoose
15(1)
2.2 Framing the Purpose and Focus
16(2)
2.3 Dedoose: Starting Your Project
18(9)
2.3.1 Creating an Account in Dedoose
18(1)
2.3.2 Creating a Dedoose Project
19(2)
2.3.3 Saving and Storing a Dedoose Project
21(1)
2.3.4 Managing Users in a Dedoose Project
21(2)
2.3.5 Preparing Data for Import
23(2)
2.3.6 Auto Linking Qualitative and Quantitative Data in Dedoose
25(2)
2.4 Case Study: Using the Five-Level QDAP Method With Dedoose
27(18)
2.4.1 The Five-Level QDA Principles
28(3)
2.4.2 The Components of Dedoose
31(1)
2.4.3 Translation in Action: An Example From a Discourse Analysis
32(4)
2.4.4 Alternative Choices
36(2)
2.4.5 Case Study Conclusion
38(1)
2.4.6 Case Study Appendix
39(5)
2.4.7 Information About the Case Study Authors
44(1)
Chapter 3 Bringing Data Into Dedoose
45(22)
3.1 Gathering Mixed Data
45(4)
3.1.1 Data as Evidence
45(1)
3.1.2 Qualitative Data
46(1)
3.1.3 Quantitative Data
47(1)
3.1.4 Mixed Methods Data
47(2)
3.2 Numbers as Data
49(2)
3.2.1 Using Numbers Qualitatively in Dedoose
49(1)
3.2.2 Using Numbers Quantitatively in Dedoose
50(1)
3.3 Memos as Data
51(4)
3.4 Case Study: Incorporating Mixed Analysis Into Your Study
55(9)
3.4.1 Setting Up the Project
56(1)
3.4.2 Data Sources
57(2)
3.4.3 Data Management
59(1)
3.4.4 Analysis Processes
60(2)
3.4.5 Reporting the Project
62(1)
3.4.6 Looking Back
63(1)
3.4.7 Case Study Conclusion
63(1)
3.4.8 Information About the Case Study Authors
64(1)
3.4.9 Notes
64(1)
3.5 Conclusion
64(1)
Appendix: Types of Interview Data
65(2)
PART II DATA INTERACTION AND ANALYSIS
67(144)
Chapter 4 Teamwork Analysis Techniques
69(34)
4.1 Team Management
69(5)
4.1.1 Team Dynamics
70(4)
4.2 Collaborative Interpretations
74(2)
4.3 Coding in Teams
76(2)
4.3.1 The Head Start Literacy Study and Teamwork
76(1)
4.3.2 Coding With the Literacy Project Team
77(1)
4.4 Bringing Procedures Into the Dedoose Environment
78(9)
4.4.1 Team Coding and Establishing Consistency in Dedoose
79(2)
4.4.2 The Case for "Chunking"
81(1)
4.4.3 Coding Blind
82(2)
4.4.4 Using Document Cloning for "Apples-to-Apples" Coding Comparison
84(2)
4.4.5 The Dedoose Training Center
86(1)
4.5 Team Conduct Rules
87(2)
4.6 Case Study: Large-Scale, Multilanguage, Cross-Cultural Analysis With Dedoose
89(12)
4.6.1 Setting Up the Project
91(3)
4.6.2 Data Sources
94(1)
4.6.3 Data Management
94(2)
4.6.4 Analysis Processes
96(1)
4.6.5 Reporting the Project
97(3)
4.6.6 Looking Back
100(1)
4.6.7 Information About the Case Study Authors
101(1)
4.7 Conclusion
101(2)
Chapter 5 Qualitative Analysis
103(30)
5.1 Qualitative Analysis: Looking for Quality
103(9)
5.1.1 Theory Bits
105(2)
5.1.2 Technological Advances in Visualizing Meanings
107(1)
5.1.3 Improving the Reporting of Multivariate Findings
108(1)
5.1.4 Using Great Quotes in Dedoose
109(2)
5.1.5 Recommendations
111(1)
5.2 Working With Codes
112(10)
5.2.1 Creating Connections
113(2)
5.2.2 Codes
115(2)
5.2.3 Code/Tag Tree Structure Modification
117(3)
5.2.4 Code Weights/Ratings
120(2)
5.3 Case Study: Using Dedoose for a Multisite Study
122(10)
5.3.1 Setting Up the Project
123(1)
5.3.2 Data Sources
124(1)
5.3.3 Data Management
125(1)
5.3.4 Analysis Processes
126(4)
5.3.5 Reporting the Project
130(1)
5.3.6 Looking Back
131(1)
5.3.7 Case Study Conclusion
132(1)
5.3.8 Information About the Case Study Authors
132(1)
5.4 Conclusion
132(1)
Chapter 6 Designing Mixed Methods Analysis
133(28)
6.1 Identifying Analysis Strategies
133(4)
6.2 Using Descriptors
137(4)
6.2.1 Multiple Descriptors
139(1)
6.2.2 Dynamic Descriptors
140(1)
6.3 Topic Modeling
141(6)
6.3.1 Background
141(2)
6.3.2 Modes of Inquiry
143(1)
6.3.3 Recent Developments
144(1)
6.3.4 Training the Model
145(2)
6.4 Case Study: Integrating Mixed Data in a Longitudinal Study
147(13)
6.4.1 Setting Up the Project
148(1)
6.4.2 Data Sources
149(4)
6.4.3 Data Management
153(4)
6.4.4 Analysis Processes
157(2)
6.4.5 Looking Back
159(1)
6.4.6 Information About the Case Study Authors
160(1)
6.5 Conclusion
160(1)
Chapter 7 Managing Complex Mixed Methods Analysis
161(34)
7.1 Recognizing and Managing Complexity in Analysis
161(3)
7.2 Data Complexity in Your Project
164(6)
7.2.1 Setting Up Complex Data in Dedoose
168(1)
7.2.2 Moving From Qualitative to Quantitative
169(1)
7.2.3 Moving From Quantitative to Qualitative
169(1)
7.3 Using Visualization Tools for Analysis
170(5)
7.3.1 Descriptor Ratios Pie Chart
171(1)
7.3.2 Code Co-Occurrence Table
172(2)
7.3.3 Descriptor x Descriptor x Code Chart
174(1)
7.4 Moving Through and Filtering Your Data
175(11)
7.4.1 Filtering via Chart Selection Reviewer
177(2)
7.4.2 Filtering via the Excerpts Workspace: Increasing Complexity
179(4)
7.4.3 Filtering via the Data Set Workspace
183(3)
7.5 Case Study: Complex Yet Manageable---the Organizational Genius of Dedoose
186(8)
7.5.1 Setting Up the Project
187(1)
7.5.2 Data Sources
188(1)
7.5.3 Data Management
188(3)
7.5.4 Analysis Processes
191(1)
7.5.5 Reporting the Project
192(1)
7.5.6 Looking Back
193(1)
7.5.7 Information About the Case Study Author
193(1)
7.6 Conclusion
194(1)
Chapter 8 Working With Numbers in Dedoose: Statistics, Tabling, and Charting for Numbers, Weights, and Option List Field Data
195(16)
8.1 Background/Introduction
195(3)
8.1.1 Descriptors
196(1)
8.1.2 Types of Numeric and Categorical Data
197(1)
8.2 Charts, Tables, and Plots for Individual Fields or Code Weights
198(5)
8.2.1 Individual Number Fields or Code Weights/Ratings
198(2)
8.2.2 Individual Option List/Categorical Field Data
200(3)
8.3 Charts, Tables, Plots, and Analyses for Pairs of Fields/Code Weights
203(7)
8.3.1 Pairs of Option List Fields: Data Interaction
203(2)
8.3.2 Tests With Continuous Numbers
205(5)
8.4 Summary
210(1)
PART III REPORTING CREDIBLE RESULTS AND SHARING FINDINGS
211(27)
Chapter 9 Reporting Your Findings
213(12)
9.1 Reaching Your Audience
213(1)
9.2 Mixed Methods Procedural Checklist
214(1)
9.3 Case Study: Reporting to Multiple Audiences
215(10)
9.3.1 Setting Up the Project
216(2)
9.3.2 Data Sources
218(1)
9.3.3 Data Management
218(2)
9.3.4 Analysis Processes
220(2)
9.3.5 Reporting the Project
222(1)
9.3.6 Looking Back
223(1)
9.3.7 Information About the Case Study Authors
224(1)
Chapter 10 Sharing Data With a Larger Audience
225(13)
10.1 Reaching a Larger Audience
225(1)
10.2 Case Study: Sharing Qualitative Social Science Data
226(9)
10.2.1 Managing Your Dedoose Project for Sharing
228(4)
10.2.2 Finding a Data Repository
232(2)
10.2.3 Promoting Your Data
234(1)
10.2.4 Information About the Case Study Authors
235(1)
10.3 Changing Reporting Practices: Open Access
235(2)
10.4 Final Word
237(1)
Closing Remarks 238(4)
Thomas S. Weisner
References 242(9)
Index 251
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.



Dr. Eli Lieber is an interdisciplinary social scientist, methodologist, and data analyst.  He has spent over 20 years at the University of California, Los Angeles focused on advancing our thinking about and strategically implementing qualitative and mixed methods approaches in social science research.  Initially trained as a quantitative psychologist, he soon began working with colleagues from other social science disciplines and came to appreciate the deep importance and role in qualitative perspectives.  From a practical view, he believes that the informed integration of methods, study design, and the data generated produce more comprehensive and robust research findings than those from more method-centric approaches.

Dr. Liebers recent work has focused on the continued development of mixed methods strategies and technologies.  Eli is particularly interested in what we do with all the data we gather: How can data be integrated and what evolving technologies can make our research and evaluation work and findings more efficient, effective, and sustainable? Eli looks forward to his work with Institute for Mixed Methods Research (IMMR) colleaguesa truly global and diverse group of individuals.  The IMMR mission will be an ongoing service to building methodological capacity, relationship building, engagement and communication regarding evolving mixed methods work, and bringing the deep experience of IMMR associates into the service of others employing these practices.  Dr. Lieber is optimistic about how mixed methods research and IMMR can advance social science through engaging with colleagues directly, through connections within larger institutions organizations, and through the forging of strategic partnerships.

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.