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Qualitative Data Analysis: Practical Strategies 2nd Revised edition [Pehme köide]

3.96/5 (27 hinnangut Goodreads-ist)
(Western Sydney University, Australia)
  • Formaat: Paperback / softback, 584 pages, kõrgus x laius: 232x186 mm, kaal: 1040 g
  • Ilmumisaeg: 26-Oct-2020
  • Kirjastus: Sage Publications Ltd
  • ISBN-10: 1526404761
  • ISBN-13: 9781526404763
Teised raamatud teemal:
Qualitative Data Analysis: Practical Strategies 2nd Revised editionSuurem pilt
  • Formaat: Paperback / softback, 584 pages, kõrgus x laius: 232x186 mm, kaal: 1040 g
  • Ilmumisaeg: 26-Oct-2020
  • Kirjastus: Sage Publications Ltd
  • ISBN-10: 1526404761
  • ISBN-13: 9781526404763
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781526404763.html
Märksõnad:
Balancing theoretical foundations with practical strategies, this book helps you develop an approach to your qualitative analysis that is both systematic and insightful.

It demonstrates the importance of tying analysis into every aspect of research, from design, through data collection and management, to writing up, and provides step-by-step guidance on how to embed analysis from start to finish. Grounded in the reality of doing research, this second edition:

  • Presents visual and text-based methods for analysis, using manual and digital tools 
  • Inspires confidence as you code, connect and interrogate observational, text and visual data
  • Showcases best practice and helps you navigate real-life dilemmas using case studies of research from across the social sciences.

Together with rich online resources including videos, datasets and journal articles, this is an important new edition for all students undertaking qualitative research, with a focus on analysis and design.


Balancing theoretical foundations with practical strategies, this book helps you develop an approach to your qualitative data analysis that is both systematic and insightful.

Arvustused

Pat Bazeleys 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

List of figures
xiii
List of tables
xvii
About the author xix
Preface to the first edition xxi
Preface to the second edition xxiii
Online resources xxvi
PART I LAYING THE FOUNDATIONS FOR ANALYSIS
1(122)
1 Thinking and working qualitatively
5(22)
Chapter objectives
6(1)
1.1 Thinking qualitatively
6(4)
1.2 Qualitative methodologies (and methods)
10(4)
1.3 Working qualitatively: pathways through analysis
14(6)
1.4 Working qualitatively: using software
20(2)
1.5 Working qualitatively: implications for analysis
22(3)
1.6 Writing about working qualitatively
25(1)
Further reading
25(2)
2 Foundations for thinking and working qualitatively
27(28)
Chapter objectives
28(1)
2.1 Making the implicit explicit
28(1)
2.2 Ontological and epistemological perspectives
29(2)
2.3 Inquiry paradigms used by qualitative researchers
31(11)
2.4 Epistemologically informed methods?
42(2)
2.5 Logic and argument: pathways to drawing inferences
44(5)
2.6 Applying deterministic, probabilistic, and mechanistic logic in inferring causation
49(2)
2.7 Concluding comments
51(1)
2.8 Writing about foundations
52(1)
Further reading
52(3)
3 Design that supports analysis
55(36)
Chapter objectives
56(1)
3.1 Design: giving form to ideas
56(3)
3.2 Focusing the study: what do you want to achieve?
59(14)
3.3 Developing research questions
73(1)
3.4 Design for data that can be analysed
74(6)
3.5 Relationships with participants
80(3)
3.6 Design for quality and credibility of conclusions
83(2)
3.7 Concluding comments: a cohesive design?
85(2)
3.8 Writing about design
87(1)
Further reading
88(3)
4 Managing and preparing data for analysis
91(32)
Chapter objectives
92(1)
4.1 Before data collection
92(1)
4.2 Keeping organised, accessible, and useful data records
93(3)
4.3 Recording and preparing data for analysis
96(15)
4.4 The importance of context
111(3)
4.5 Recording sample details for analysis
114(6)
4.6 Checks and balances for data sources
120(1)
4.7 Managing relationships in the research team
121(1)
4.8 Writing about data management
122(1)
Further reading
122(1)
PART II FUNDAMENTALS OF ANALYSIS: WORKING WITH DATA
123(268)
How I use the terms code, category, and concept
124(1)
Code or connect?
125(2)
5 Read, reflect, and connect: initial explorations of data
127(28)
Chapter objectives
128(1)
5.1 Read, and read again
128(1)
5.2 Write as you read or review
129(5)
5.3 Purposeful play: preliminary explorations of each data source
134(15)
5.4 Exploring context
149(2)
5.5 Involving participants in early analysis
151(1)
5.6 Refocus, ready for the next phase
151(2)
5.7 Writing about preliminary analysis
153(1)
Further reading
154(1)
6 Coding as an analytic strategy
155(36)
Chapter objectives
156(1)
6.1 Using codes to work with data
156(6)
6.2 Approaches to coding
162(3)
6.3 Naming codes
165(3)
6.4 Coding to capture substance and meaning
168(15)
6.5 Issues of validity and reliability in coding
183(6)
6.6 Concluding comments
189(1)
6.7 Writing about coding processes
189(1)
Further reading
190(1)
7 Tools to manage the coding process
191(40)
Chapter objectives
192(1)
7.1 Preparing for the task of coding
192(3)
7.2 Pencil-and-paper strategies for coding
195(4)
7.3 Interactive coding with qualitative software
199(7)
7.4 Developing a coding system
206(8)
7.5 Creating a codebook
214(2)
7.6 Machine coding of text content - promise or poison?
216(10)
7.7 Managing the process of coding
226(2)
7.8 Concluding comments
228(1)
7.9 Writing about codes
229(1)
Further reading
229(2)
8 Codes, themes, and descriptive writing
231(30)
Chapter objectives
232(1)
8.1 Describing coded categories and concepts as an analytic step
232(5)
8.2 Combining, connecting, and focusing codes
237(8)
8.3 Theming participant experience: lessons from phenomenology
245(3)
8.4 Writing descriptively to build on codes and themes
248(11)
8.5 Concluding comments
259(1)
8.6 Writing about your use of code or theme-based/descriptive methods
259(1)
Further reading
260(1)
9 Working with cases to build understanding and explanation
261(36)
Chapter objectives
262(1)
9.1 Strategies for learning from cases
262(11)
9.2 Comparing and relating cases
273(11)
9.3 Building explanatory theory - one case at a time
284(10)
9.4 Concluding comments
294(1)
9.5 Writing about your use of case-based methods
294(1)
Further reading
295(2)
10 Learning from stories, accounts, and conversations
297(34)
Chapter objectives
298(1)
10.1 Learning from stories and accounts
298(5)
10.2 Components of stories and accounts
303(7)
10.3 Participant narratives - making meaning of experience
310(5)
10.4 Deconstructing the storyline
315(1)
10.5 Tracing causation through narrative
316(3)
10.6 Learning from discourse: the intersubjective space
319(6)
10.7 Interaction in focus group data
325(3)
10.8 Concluding comments
328(1)
10.9 Writing about your use of narrative and discourse methods
328(1)
Further reading
329(2)
11 Analysing visual data
331(38)
Chapter objectives
332(1)
11.1 The nature of visual data
332(1)
11.2 Using visual data sources in qualitative research
333(5)
11.3 Approaches to the analysis of visual data
338(1)
11.4 Documenting production and `biography'
338(1)
11.5 Describing the visual image
339(9)
11.6 Interpreting image content
348(4)
11.7 Contributions from linguistic methodologies
352(5)
11.8 Consideringaudience
357(2)
11.9 Analysing video and film
359(5)
11.10 Computer technology and visual analysis
364(1)
11.11 Concluding comments
365(1)
11.12 Writing about visual analysis
366(1)
Further reading
366(3)
12 Comparative analyses using coded data
369(22)
Chapter objectives
370(1)
12.1 Why compare?
370(3)
12.2 Tools for comparative analyses
373(7)
12.3 Exploring when, how, or for whom experience varies
380(8)
12.4 Final reflection: comparison as a means rather than an end
388(1)
12.5 Writing about comparisons
389(1)
Further reading
389(2)
PART III RECONSTRUCTION, CONNECTION, AND COHERENCE
391(126)
13 Exploring, seeing, and investigating connections in data
393(40)
Chapter objectives
394(1)
13.1 Build on foundational work
394(2)
13.2 Write your way to deeper awareness
396(3)
13.3 Draw to spark ideas about relationships
399(11)
13.4 Interrogate your coding system
410(15)
13.5 Build explanatory theory
425(5)
13.6 Concluding comments
430(1)
13.7 Writing about exploring and testing relationships in data
431(1)
Further reading
431(2)
14 Elaborating concepts, developing theory
433(26)
Chapter objectives
434(1)
14.1 Elaborating and theorising concepts
434(12)
14.2 From concepts to theory
446(9)
14.3 Concluding comments
455(1)
14.4 Writing about concepts and theory development
455(1)
Further reading
456(3)
15 Realising coherent understanding
459(32)
Chapter objectives
460(1)
15.1 Framing coherence
460(3)
15.2 `Aha!' - or slowly dawning realisation?
463(4)
15.3 Shapes of coherence: what might yours look like?
467(17)
15.4 Making warranted assertions
484(3)
15.5 In conclusion: tips and tricks for bringing it together
487(2)
15.6 Writing about results and conclusions
489(1)
Further reading
490(1)
16 Defending and extending: issues of quality and significance
491(26)
Chapter objectives
492(1)
16.1 Quality in qualitative research
492(9)
16.2 Generalisation and transferability
501(2)
16.3 Theoretical extension
503(5)
16.4 Audiences and representation
508(6)
16.5 A final reflection
514(1)
16.6 Writing about quality and significance
514(1)
Further reading
515(2)
Appendix: Data samples 517(10)
References 527(20)
Index 547
Pat Bazeley is Director of Research Support P/L and Adjunct Professor in the Translational Research and Social Innovation Centre at Western Sydney University. Since graduating in psychology, she has worked in community development, as an evaluation researcher, and in academic research development. For almost 30 years Pat has been providing research training and project consulting to academics, graduate students and practitioners representing a wide range of disciplines across Australia and internationally. Her particular expertise is in helping researchers to make sense of qualitative, survey, and mixed methods data, and to use computer programs for management and analysis of data. Pats research has focused on qualitative and mixed methods data analysis, the development and performance of researchers, and the wellbeing of older women. She has published books, chapters, and articles on mixed methods and qualitative data analysis. She serves on the Editorial Board of the Journal of Mixed Methods Research and was 20152016 President of the Mixed Methods International Research Association.