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Analyzing Qualitative Data
Systematic Approaches
Second Edition
- H. Russell Bernard - University of Florida, USA
- Amber Wutich - Arizona State University, USA
- Gery W. Ryan - Kaiser Permanente Bernard J. Tyson School of Medicine, USA
September 2016 | 576 pages | SAGE Publications, Inc
The fully updated Second Edition presents systematic methods for analyzing qualitative data with clear and easy-to-understand steps. The first half is an overview of the basics, from choosing a topic to collecting data, and coding to finding themes, while the second half covers different methods of analysis, including grounded theory, content analysis, analytic induction, semantic network analysis, ethnographic decision modeling, and more. Real examples drawn from social science and health literature along with carefully crafted, hands-on exercises at the end of each chapter allow readers to master key techniques and apply them to their own disciplines.
Chapter 1: Introduction to Text: Qualitative Data Analysis
Introduction: What Is Qualitative Data Analysis?
What Are Data and What Makes Them Qualitative?
About Numbers and Words
Research Goals
Kinds of Qualitative Data
Key Concepts in This Chapter
Summary
Further Reading
Chapter 2: Choosing a Topic and Searching the Literature
Introduction
Exploratory and Confirmatory Research
Four Questions to Ask About Research Questions
The Role of Theory in Social Research
Choosing a Research Question
The Literature Search
Databases for Searching the Literature
Key Concepts in This Chapter
Summary
Exercises
Further Reading
Chapter 3: Research Design I: Sampling
Introduction
Two Kinds of Samples
Kinds of Nonprobability Samples
Key Concepts in This Chapter
Summary
Exercises
Further Reading
Chapter 4: Research Design II: Collecting Data
Introduction
Data Collection Methods
Indirect Observation
Direct Observation
Elicitation Methods
Accuracy
Eliciting Cultural Domains
Mixed Methods
Choosing a Data Collection Strategy
Key Concepts in This Chapter
Summary
Exercises
Further Reading
Chapter 5: Finding Themes
Introduction
What’s a Theme?
Where Do Themes Come From?
Eight Observational Techniques: Things to Look for
Four Manipulative Techniques: Ways to Process Texts
Selecting Among Techniques
And Finally...
Key Concepts in This Chapter
Summary
Exercises
Further Reading
Chapter 6: Codebooks and Coding
Introduction
Three Kinds of Codes
Building Codebooks
Using Existing Codes
Codebooks Continue to Develop
Hierarchical Organization of Codebooks
Applying Theme Codes to Text
The Mechanics of Marking Text
Multiple Coders
The Content of Codebooks
Describing Themes: Bloom’s Study of AIDS
Finding Typical Segments of Text
Key Concepts in This Chapter
Summary
Exercises
Further Reading
Chapter 7: Introduction to Data Analysis
Introduction: What Is analysis?
Database Management
Data Matrices
Proximity Matrices
Key Concepts in This Chapter
Summary
Exercises
Further Reading
Chapter 8: Conceptual Models
Introduction
Statistical Models and Text Analysis
Building Models
Step 1: Identifying Key Concepts
Step 2: Linking Key Constructs
Step 3: Testing the Model
Key Concepts in This Chapter
Summary
Exercises
Further Reading
Chapter 9: Comparing Attributes of Variables
Introduction
Fundamental Features of Comparisons
Levels of Measurement
Converting Text to Variable Data
Levels of Aggregation
Many Types of Comparisons
Comparing the Columns
And Finally...
Key Concepts in This Chapter
Summary
Exercises
Further Reading
Chapter 10: Grounded Theory
Introduction: On Induction and Deduction
Overview of Grounded Theory
A GT Project: Schlau’s Study of Adjustment to Becoming Deaf as an Adult
Visualizing Grounded Theories
Key Concepts in This Chapter
Summary
Exercises
Further Reading
Chapter 11: Content Analysis
Introduction
History of Content Analysis
Doing Content Analysis
Intercoder Reliability
A Real Example of Using Kappa: Carey et al.’s Study
Cross-Cultural Content Analysis: HRAF
Automated Content Analysis: Content Dictionaries
Key Concepts in This Chapter
Summary
Exercises
Further Reading
Chapter 12: Schema Analysis
Introduction
History of Schema Analysis
Mental Models
Kinds of Schemas
Methods for Studying Schemas
Folk Theories: Kempton’s Study of Home Thermostats
Key Concepts in This Chapter
Summary
Exercises
Further Reading
Chapter 13: Narrative Analysis
Introduction
Sociolinguistics
Hermeneutics
Phenomenology
Steps in a Phenomenological Study
Key Concepts in This Chapter
Summary
Exercises
Further Reading
Chapter 14: Discourse Analysis II: Conversation and Performance
Introduction
Grammar Beyond the Sentence
Conversation Analysis
Transcriptions
Taking Turns in a Jury
Performance Analysis: Ethnopoetics
Language in Use
Critical Discourse Analysis: Language and Power
Key Concepts in This Chapter
Summary
Exercises
Further Reading
Chapter 15: Analytic Induction and Qualitative Comparative Analysis
Introduction
Induction and Deduction—Again
Analytic Induction
Qualitative Comparative Analysis—QCA
And Finally...
Key Concepts in This Chapter
Summary
Exercises
Further Reading
Chapter 16: Ethnographic Decision Models
Introduction
How to Build EDMs
Key Concepts in This Chapter
Summary
Exercises
Further Reading
Chapter 17: KWIC Analysis and Word Counts
Introduction
KWIC—Key Word in Context
An Example of KWIC
Word Counts
Words and Matrices
Personal Ads
Describing Children
Word Counts Are Only a Start
Key Concepts in This Chapter
Summary
Exercises
Further Reading
Chapter 18: Cultural Domain Analysis
Introduction
What Are Cultural Domains?
Free Lists
Plotting Free Lists
Analyzing Free List Data
Pile Sorts
Analyzing Pile Sort Data: MDS
Folk Taxonomies
How to Make a Taxonomy: Lists and Frames
And Finally...
Key Concepts in This Chapter
Summary
Exercises
Further Reading
Chapter 19: Semantic Network Analysis
Introduction
Converting Texts Into Similarity Matrices
Jang and Barnett’s Study of CEO Letters
Nolan and Ryan’s Study of Horror Films
Some Cautions About All This
Semantic Network Analysis of Themes
And Finally...
Key Concepts in This Chapter
Summary
Exercises
Further Reading
Appendix
Supplements
Student Resource Site
The authors created engaging and helpful digital content to develop a rich learning environment for instructors and to support students' personalized learning.
The authors' website includes the following resources.
The authors created engaging and helpful digital content to develop a rich learning environment for instructors and to support students' personalized learning.
The authors' website includes the following resources.
- Video tutorials on working with MAXQDA
- Presentation slides
- MAXQDA keyboard shortcuts
- Datasets
- Stop list
- Recommended readings
Provides detailed explanations and examples on qualitative data analysis which are quite valuable to students
Department of Communication Sciences, Hacetteppe University
February 18, 2022