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Analyzing Qualitative Data
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Analyzing Qualitative Data
Systematic Approaches

Second Edition
July 2016 | 576 pages | SAGE Publications, Inc
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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.
  • 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

Professor Emre Toros
Department of Communication Sciences, Hacetteppe University
February 18, 2022
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Key features
NEW TO THIS EDITION:

  • An all-new chapter on doing a literature review focuses on choosing research topics that rely at least partly on qualitative data.
  • Updated coverage in the sampling chapter reflects the fast-growing literature on nonprobability sampling in social research generally, and in qualitative research in particular.
  • New information in Chapters 5 (Themes) and 6 (Coding) includes up-to-date coverage of automated coding and other forms of theme identification, deductive and inductive approaches to code development, and how to manage codebooks in large, collaborative projects.
  • A new chapter on semantic network analysis covers network analysis, how to count similarity, Jaccard’s coefficient of similarity, and semantic network analyses of themes and codes.
  • New and expanded coverage is offered on content dictionaries and the history of automated content analysis (Chapter 11), word clouds (Chapter 17), and data management, similarity matrices, multidimensional scaling, and hierarchical clustering (Chapter 18).
  • Updated software guidance reflects new software developments and macros.
KEY FEATURES:

  • Chapter-ending key concepts, summaries, and hands-on exercises help readers master key content.
  • Suggestions for further readings serve as pointers to the literature.
  • A helpful appendix (at the back of the book and online) contains information on appropriate software. 

Sample Materials & Chapters

Chapter 1

Chapter 5

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