Qualitative_data_analysis

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connections; description qualitative data management: use of computer for see under computers qualitative research: basic orientations 1–3; constraints and restrictions on 115–10, 243; focus and objectives of 46, 52, 65, 67, 68, 77; internal replication of 229–2, 258; joint 120, 230; report see account, producing; relationship of researcher with subject 116; role of researcher in producing qualitative data 14–15; techniques and methods 4, 14–16, 38, 273; types of xiv–1; see also social research quality 11; of data 75–5, 231–5, 263 quantitative data distinguished from qualitative 3, 9–11, 12–14, 273 quantitative measurement: conceptual basis of 18, 24–5 quantitative methods and analysis compared with qualitative 3–4, 5, 14–15, 20, 22, 23, 24–8, 43, 268, 273; computer and 64 quasi-statistics, quasi-statistical approach 5, 29; see also statistics questionnaire surveys 14, 15, 80 random samples 28 randomizing cases 127, 186, 235 ratio scale 24 reading 86–8, 105, 247, 272; checklist 87–6; free association 90; Hypersoft and 280; shifting focus and sequence 90–8; transposition and comparison 90, 199 recategorizing data 147–51 recoding data 208–208 INDEX 293 recontextualization of data see categories, splitting and splicing regularities see patterns in data reliability 14, 229–2, 258–3, 268 representativeness 268–3 research see qualitative research; social research reviewing the literature 68–9 Richards, L. 5, 49, 61 Richards, T. 5, 49, 61 Riley, J. 92 Sabatier, P.A. 87 Sayer, A. 3, 8, 11, 12, 40, 50, 52, 53, 54, 86, 99, 160, 177, 188, 254 scanners 57 science, scientific theory 3, 7, 11, 12, 27, 31, 35, 64, 228, 229 scientific method 24 Seidel, J. 63, 275 Shimahara, N. 258, 268 singularities, singularity 18–19, 28, 49, 230, 263, 273; and linking data 174, 191; and matrices 202, 205 social criticism, evaluation 54, 244, 245, 253 social research: ethical code 229; measurement in 18–29 social science 3, 12, 54–4 social structure 51 social theory see theory Sperber, D. 33 spontaneity 56, 94–1, 254–8 statistics, statistical analysis 3, 4, 28–8, 49, 186, 239, 263 story-telling techniques used to enhance account 245–47 Strauss, A. 5, 7, 39, 60, 87, 90, 92, 109 structured interviews see interview(s) subcategorization 110, 115, 134, 139–9, 157; and diagrams 208–1, 210, 214, 215, 216; and linking 198; mechanical aspects 145, 147
294 INDEX subclassification 144–7; see also subcategorization subconscious 36–7 subject: and account of analysis 245, 246; credibility of research to 243; and definition of data 14–15; intentions and interpretations of 31, 36– 7, 104, 144, 172; relationship with researcher 116 subjectivity 14, 236, 261, 268 substantive connections 46, 160 summarizing data 249–5, 263, 267; irrelevant 85, 259; see also diagrams surveys, survey data 6, 14, 28, 38, 127; questionnaire 14, 15, 80 symbolism and communication 33–4 tables see diagrams terminology see language Tesch, R. xiv, 1–3, 8, 58, 136, 280 text 12, 200–3, 244, 272 Theiler, Max 228 theory 51, 52–2, 103, 241, 267–1 theory-building approach to qualitative research 1, 53, 111, 268 Toffler, A. 48 Trow, M.J. 226 validity 14, 236, 243, 258, 260–61 variables 22; associations between 28, 48–9; case 184–9, 208; cross-tabulating see cross-tabulations; face-sheet 82, 83, 186; interval 24, 25; maps of complex relationship between 212; nominal 22–2, 45, 46, 152; ordinal 23, 24, 45, 152 variations see patterns in data Varley, H. 13 video material, computer and 57, 77 Warren, W.P. 99 Whitehead, A.N. 99 Wilson, D. 33 Wilson, John Dover 92 Wood, Victoria: humour used as example for analysis see individual processes word-processing 60, 83, 281
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Qualitative data analysis Learning
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First published 1993 by Routledge 1
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Glossary 283 References 285 Index 2
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9.1 Categorizing data—1 120 9.2 C
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3.1 Personal ads 42 5.1 ‘The libr
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Preface A new book on qualitative d
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analyse humour from any number of p
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Q. What colour is snow? A. White. C
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INTRODUCTION 3 approaches, interest
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INTRODUCTION 5 qualitative methods.
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INTRODUCTION 7 First of all, I take
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INTRODUCTION 9 the purposes of anal
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WHAT IS QUALITATIVE DATA? 11 distan
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WHAT IS QUALITATIVE DATA? 13 contem
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WHAT IS QUALITATIVE DATA? 15 ‘ric
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classified. Take the example in Ill
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Figure 2.1 Describing a bit of data
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WHAT IS QUALITATIVE DATA? 21 home;
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Figure 2.4 Nominal variable with mu
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Figure 2.6 Interval variable with f
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ILLUSTRATION 2.2 EXAMPLE OF A GRADI
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WHAT IS QUALITATIVE DATA? 29 what a
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Chapter 3 What is qualitative analy
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Figure 3.2 Three aspects of descrip
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WHAT IS QUALITATIVE ANALYSIS? 35 co
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WHAT IS QUALITATIVE ANALYSIS? 37 en
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WHAT IS QUALITATIVE ANALYSIS? 39 Th
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CLASSIFICATION WHAT IS QUALITATIVE
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nature; photograph preferred but no
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WHAT IS QUALITATIVE ANALYSIS? 45 vi
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WHAT IS QUALITATIVE ANALYSIS? 47 Fi
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Figure 3.6 Formal and substantive c
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WHAT IS QUALITATIVE ANALYSIS? 51 ne
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WHAT IS QUALITATIVE ANALYSIS? 53 If
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Figure 3.10 Qualitative analysis as
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Chapter 4 Introducing computers ‘
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ecording of an index of information
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Figure 4.1 A link between text held
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Computer transformations • Search
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Chapter 5 Finding a focus A Zen sto
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FINDING A FOCUS 67 Sheila The solic
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FINDING A FOCUS 69 to reflect upon
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For example, here are some question
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Transposition: Doctor: ‘I’m afr
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Figure 5.2 Main themes for analysin
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Chapter 6 Managing data Piles of pa
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MANAGING DATA 79 setting, by source
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MANAGING DATA 81 Interviewer Maybe
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Figure 6.1 Case documents kept in a
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MANAGING DATA 85 ‘Show the full r
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Chapter 7 Reading and annotating Ac
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• Official/bureaucratic commitmen
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READING AND ANNOTATING 91 women wer
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Techniques for interactive reading
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we can write our comment on a separ
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Figure 7.1 Relating data to key the
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READING AND ANNOTATING 99 may retur
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CREATING CATEGORIES 101 types of ph
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CREATING CATEGORIES 103 conceptual
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CREATING CATEGORIES 105 Categorizat
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Table 8.1 Alternative category list
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This is only a starting point. Our
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CREATING CATEGORIES 111 undertaking
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Figure 8.2 Weighing up the degree o
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CREATING CATEGORIES 115 Although co
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CREATING CATEGORIES 117 for an adeq
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CREATING CATEGORIES 119 well as con
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Figure 9.2 Categorizing data—2 Fi
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are entitled to expect some consist
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ASSIGNING CATEGORIES 125 we ought t
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ASSIGNING CATEGORIES 127 the data.
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have annotated this data. Suppose w
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ASSIGNING CATEGORIES 131 suddenly t
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• What generally constitutes a
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Databits Categories 5. Now she is u
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Chapter 10 Splitting and splicing T
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Using Hypertext linking, whenever n
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SPLITTING AND SPLICING 141 databits
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Category Subcategories Suffering Di
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Figure 10.1 Levels of subclassifica
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ILLUSTRATION 10.5 SUBDIVIDING DATAB
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Figure 10.2 Initial relationships b
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If we want to make this change, it
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Figure 10.5 Reassessing relationshi
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SPLITTING AND SPLICING 155 Although
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Figure 10.8 Comparing subcategories
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Figure 10.9 Shifting the analytic e
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Chapter 11 Linking data Categorizin
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Figure 11.2 Multiple hyperlinks bet
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example, we may ask what it is abou
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Figure 11.5 Linking and categorizin
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Figure 11.7 An explanatory link bet
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Table 11.2 Multiple links between d
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our reasons for making decisions an
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Figure 11.11 Conditional and causal
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Chapter 12 Making connections Imagi
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Figure 12.1 The difference between
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Table 12.1 Concurrence between cate
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Table 12.4 Boolean operators for ca
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From Table 12.6 we can tell that mo
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Notice how this cross-tabulation co
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substantive connections between cat
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Figure 12.5 Following a trail of di
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Figure 12.7 Retrieving chronologica
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This approach is very exploratory,
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MAKING CONNECTIONS 197 Table 12.10
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MAKING CONNECTIONS 199 a consequenc
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Chapter 13 Of maps and matrices A n
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Table 13.1 Comparing information ac
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OF MAPS AND MATRICES 205 [In a calm
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Table 13.5 Data indices by case and
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Table 13.7 Recoding data to express
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Table 13.11 Cross-tabulating ‘occ
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Figure. 13.4 The history of the uni
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Figure 13.7 Incorporating detail by
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Figure 13.10 Comparing differences
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Figure 13.13 Adjusting scope of mos
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Figure 13.17 Comparing strength of
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Figure 13.20 Identifying positive a
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OF MAPS AND MATRICES 225 undercurre
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Chapter 14 Corroborating evidence
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CORROBORATING EVIDENCE 229 There is
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CORROBORATING EVIDENCE 231 describe
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CORROBORATING EVIDENCE 233 of these
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Figure 14.1 Concurrence between cat
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CORROBORATING EVIDENCE 237 possibil
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CORROBORATING EVIDENCE 239 that the
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CORROBORATING EVIDENCE 241 seductiv
Page 258 and 259: CORROBORATING EVIDENCE 243 research
Page 260 and 261: Chapter 15 Producing an account ‘
Page 262 and 263: PRODUCING AN ACCOUNT 247 An analysi
Page 264 and 265: Figure 15.2 The whole is greater th
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Page 268 and 269: PRODUCING AN ACCOUNT 253 associatio
Page 270 and 271: worrying unduly about the connectio
Page 272 and 273: Figure 15.5 Different writing strat
Page 274 and 275: PRODUCING AN ACCOUNT 259 the vibrat
Page 276 and 277: Figure 15.7 Procedures for assignin
Page 278 and 279: PRODUCING AN ACCOUNT 263 consistent
Page 280 and 281: Table 15.1 Databits assigned to cat
Page 282 and 283: Table 15.3 Distribution of response
Page 284 and 285: PRODUCING AN ACCOUNT 269 To be subj
Page 286 and 287: PRODUCING AN ACCOUNT 271 analysts t
Page 288 and 289: Figure 16.3 Analysis as an iterativ
Page 290 and 291: CONCLUSION 275 empirical analysis o
Page 292 and 293: Appendix 1 ‘If the Impressionists
Page 294 and 295: APPENDIX 1 279 though it had been o
Page 296 and 297: Appendix 2 Software The reader look
Page 298 and 299: Glossary Associating categories the
Page 300 and 301: References Allen, Woody (1978) ‘I
Page 302 and 303: REFERENCES 287 Sperber, Dan and Wil
Page 304 and 305: Capra, F. 29, 64, 99 case studies 1
Page 306 and 307: Galtung, J. 14 Geer, B. 7, 8, 29, 5
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