Descripciones mejoradas de Syndetics:

An introduction to key approaches in qualitative analysis, demonstrating in each case how to carry them out using NVivo. NVivo is a powerful computer package from QSR, the developers of NUD.IST. It provides the researcher with an extensive range of tools and the book shows clearly how each can be used to support standard qualitative analysis techniques such as coding, theory building, theory testing, cross-sectional analysis, modelling and writing. The book demonstrates how different styles of analysis, such as grounded theory and narrative, rhetorical and structured approaches, can be undertaken using NViro. In most cases, the analysis is illustrated using documents from a single data set. There are many figures, tables, guides and hints for good practice. The result is a valuable text for undergraduates, postgraduates and researchers needing to learn both qualitative analysis techniques and the use of software such as NVivo.

Tabla de contenidos provista por Syndetics

• List of tables(p. ix)
• List of figures(p. xi)
• Step-by-step guides(p. xv)
• Series editor's foreword(p. xviii)
• Acknowledgements(p. xx)
• Introduction(p. xxi)
• 1 What is qualitative analysis?(p. 1)
• What are qualitative data?(p. 1)
• The two-paradigms approach(p. 4)
• Contrasting logic(p. 7)
• Strategies of qualitative research(p. 9)
• Computer-assisted qualitative data analysis(p. 10)
• The quality of qualitative research(p. 12)
• Conclusion(p. 14)
• Further reading(p. 14)
• 2 Getting started with NVivo(p. 16)
• Documents and nodes(p. 16)
• New project(p. 17)
• Backing up(p. 19)
• Job Search project(p. 21)
• The Document Explorer(p. 24)
• The Document Browser(p. 25)
• The parts of the document(p. 27)
• Browse the document, edit and change style(p. 28)
• Document properties(p. 29)
• Make a report on a document(p. 30)
• Nodes(p. 31)
• Node Explorer(p. 31)
• Other ways of creating nodes(p. 34)
• Node report(p. 40)
• Searching(p. 41)
• Refining the coding at a node(p. 44)
• Conclusion(p. 46)
• Further reading(p. 47)
• 3 Data preparation(p. 48)
• Styles(p. 50)
• Parts of the document(p. 51)
• Conclusion(p. 56)
• Further reading(p. 56)
• 4 Coding(p. 57)
• Nodes and coding(p. 57)
• Data-driven or concept-driven?(p. 59)
• The node definition(p. 60)
• What can nodes be about?(p. 62)
• Thinking about the text(p. 65)
• Selecting the text(p. 65)
• Coding at already created nodes(p. 67)
• Seeing what is coded at a node(p. 68)
• Hierarchy of nodes(p. 70)
• Functions of the node tree(p. 72)
• Types of node in NVivo(p. 73)
• Organizing tree nodes(p. 74)
• Coding away from the computer(p. 77)
• Refining the coding(p. 79)
• Conclusion(p. 81)
• Further reading(p. 81)
• 5 Memos and attributes(p. 83)
• Memos(p. 84)
• Linking documents and memos(p. 85)
• DataBites and annotations(p. 91)
• How to use document, node and DataBite linking(p. 94)
• Attributes(p. 95)
• Conclusion(p. 104)
• Further reading(p. 104)
• 6 Searching for text(p. 105)
• What to search the text for(p. 105)
• Simple searching(p. 107)
• The NVivo Search Tool(p. 107)
• An example using the Job Search project(p. 115)
• Metaphors and accounts(p. 121)
• Sets(p. 123)
• Conclusion(p. 126)
• Further reading(p. 127)
• 7 Developing an analytic scheme(p. 128)
• Ways of coding at nodes(p. 128)
• Creating and manipulating a node tree(p. 131)
• Pattern searching and checking hunches(p. 141)
• Node and attribute searching in NVivo(p. 142)
• Examples of questions addressed by searching with nodes and attributes(p. 144)
• Conclusion(p. 154)
• Further reading(p. 155)
• 8 Three analytic styles(p. 156)
• Structured analysis(p. 156)
• Grounded theory(p. 165)
• Narrative, life history and biography(p. 174)
• Conclusion(p. 185)
• Further reading(p. 185)
• 9 Visualizing the data(p. 187)
• Matrices and tables(p. 187)
• Matrix searching in NVivo(p. 193)
• Charts and diagrams(p. 200)
• The NVivo Model Explorer(p. 209)
• Conclusion(p. 218)
• Further reading(p. 218)
• 10 Communicating(p. 220)
• The need to write(p. 221)
• Organization(p. 222)
• Quality(p. 230)
• Teams(p. 233)
• Conclusion(p. 237)
• Further reading(p. 238)
• Glossary(p. 239)
• References(p. 247)
• Index(p. 251)

Notas de autor provistas por Syndetics

Graham R. Gibbs is Reader in Social Science Computing and Head of the Department of Behavioural Sciences at the University of Huddersfield. He has taught both quantitative and qualitative methods to undergraduate and postgraduate students for over twenty years. He currently teaches research design and qualitative analysis to masters' students. He is also interested in computer assisted learning and is director of the coMentor virtual learning environment project