Analyzing Qualitative Data
Systematic Approaches
Second Edition
Amber Wutich
- Arizona State University, USA
If you’re in North America, please visit our Sage College Publishing website to purchase or sample this book:
Go to College Publishing WebsiteDescription
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.
Contents
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
Appendix
Description
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.
Contents
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
Appendix
Analyzing Qualitative Data
Systematic Approaches
July 2016 | 576 pages | Sage US
| Format | Published Date | ISBN | Price |
|---|
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.
Table Of Contents:
- 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