Qualitative Data Analysis 2/e

Part I: Laying the foundations for analysis Chapter 1: Thinking and working qualitatively Chapter 2: Foundations for thinking and working qualitatively Chapter 3: Design that supports analysis Chapter 4: Managing and preparing data for analysis Part II: Fundamentals of analysis: working with data Chapter 5: Read, reflect, and connect: initial explorations of data Chapter 6: Coding as an analytic strategy Chapter 7: Tools to manage the coding process Chapter 8: Codes, themes, and descriptive writing Chapter 9: Working with cases to build understanding and explanation Chapter 10: Learning from stories, accounts, and conversations Chapter 11: Analysing visual data Chapter 12: Comparative analyses using coded data Part III: Extending analysis beyond codes and themes Chapter 13: Exploring, seeing, and investigating connections in data Chapter 14: Elaborating concepts, developing theory Chapter 15: Realising coherent understanding Chapter 16: Defending and extending: issues of quality and significance

Pat Bazeley's book is suitable for both novice and experienced researchers who are serious about undertaking rigorous qualitative analysis. It succeeds in balancing academic considerations with the practicalities of doing qualitative data analysis. The entire text is prescribed reading for all my senior students who are tackling qualitative research. -- Jacques de Wet If you are new to qualitative research and interested in developing theory at doctoral level, this book is a useful resource for demystifying theory development. As a Phenomenologist, I found the phenomenology sections are written in accessible language. A valuable guide covering the basics, including naming codes and understanding thick descriptions. -- Efe Imiren For many students and emerging scholars new to qualitative research, the analysis of qualitative research data remains a mysterious process. This book is an indispensable guide in opening up this 'black box' of qualitative research. Pat Bazeley shows the qualitative data analysis process in action: not a technical and highly procedural undertaking, but an interpretative act that requires as much systematicity as it requires creativity, imagination and thinking. -- Mathias Decuypere