Qualitative_data_analysis

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Galtung, J. 14 Geer, B. 7, 8, 29, 53, 110, 232 Geerz, C. 31 generalization 28, 268–3 Giarelli, J.M. 7, 18, 226, 241 Gibbon, Edward 273 graphic information, computer storage of 57, 77 graphics 46, 51, 257; representation of levels of classification 114; see also diagrams grounded theory 5, 109–4, 273, 274 group discussion, conversations 80 group interviews 14, 75 Hawking, S.W. 24, 35, 212, 261, 262 Highfield, R. 25, 48 Huberman, M. 5, 7, 51, 145, 186, 200, 230 humour; text of ‘If the Impressionists had been Dentists’ (Woody Allen) 276–72; used as example for analysis see individual processes hyperlinks see computers, linking Hypersoft 280–4 hypotheses 51, 70–9, 270 ideas see concepts; theory image 12–12; see also graphic information indexing: of account 257; of data 57–7, 62, 79–7, 85, 127, 181, 206–8, 280 induction 268 inference 168, 169, 172, 177, 179, 179, 183, 195, 197, 198, 208, 225, 270 interpretation(s) 5, 27, 30, 40–41, 54, 86, 99, 236; creating alternative 236–34, 245; misinterpreting 230–9; subject and 31, 36–7, 104, 144, 172 interpretive approach to qualitative research 1, 5 interval variables 24, 25 interview(s) 14, 15, 18, 38, 104, 105, 116; data 12, 77, 79, 80–8, 82, 184; group 14, 75 jargon see language joint research 120, 230 Jones, Steve 211 Jones, Sue 5, 110 keywords 60–9, 91–8, 125, 127, 280 INDEX 291 labels, labelling data 102, 127; see also categories; categorization language 8, 11, 92; use of, in final account 252–6, 254 laptop computers 57 Lee, R.M. 6, 8, 280 linking 160–67, 177, 239, 247, 259, 262, 263, 272, 273, 275; computer and see under computers; linking memos and data 94–1, 280; and mapping 218–12 literature relevant to social science: reviewing, as help in finding focus for analysis 68–9; and validity of concepts in new research 267–1 mapping 201, 211–18, 239, 251, 259, 259, 272; and annotating 96–3; bar charts 216; colour in 220; feedback loops 221; Hypersoft and 281; interpretive labels 212; lines 218–14; overlaps 216; range of symbols 213–6; scale 222; size of shapes related to scope 214–9, 225 Marsh, C. 6 Massie, Allan 244, 245 mathematics 3, 24, 29; see also statistics
292 INDEX matrices 201–211, 239, 259, 272; see also cross-tabulations Maxwell, A.E. 27–7 meaning: as basis for qualitative data 11–12; bits of data as units of 121–17, 125, 127; of a category 108; communication of see communication; and number 3, 12, 18, 25–5, 29, 49, 273; and science 27 measurement 11, 18–29 memos 92–98, 105, 127, 127–2, 210, 239, 247, 262, 272; on computer 94, 280; on paper 93 Merrill, L. 69–8, 71, 73, 91 methodology of qualitative data analysis 4– 8, 273–7; for individual processes see process Miles, M. 5, 7, 51, 145, 186, 200, 230 natural sciences see science networks, network approach 5, 274 neuron networks 274 Noguchi, Hideyo 228 nominal variables 22–2, 45, 46, 152 number(s): as basis for quantitative data 11, 29; and meaning 3, 12, 18, 25–5, 29, 49, 273; use of, in qualitative analysis 27–8, 49, 188–80, 207, 231, 268, 273 objectivity 35, 228, 236, 268 observation, observational research 14–15, 104, 105, 116, 184, 232, 233; participant 4, 14, 38, 103 O’Hanlon, Redmond 26 ordinal variables 23, 24, 45, 152 participant observation 4, 14, 38, 103 pattern coding 5 patterns in data 5, 28, 48, 50, 202, 203, 205, 208, 210, 263, 273; see also connections Patton, M.Q. 5, 7, 14, 249, 268, 270 Penrose, R. 274 Peter, L. 86, 99 Pfaffenberger, B. 274, 275 policy analysis, evaluation xiv, 68, 87, 103, 110, 244, 248, 257, 258 positivist social science 36 Praverand, P. 64 pre-structured data 14 prediction and qualitative analysis 30 prejudice see bias probabilities 239; see also inference psychoanalysis 36; and fairy stories 33–4 qualitative data: collection see qualitative research, techniques and methods; diversity of 12–12; gaps in, revealed by use of diagrams 200, 202; irrelevant 85, 259; nature of 9–29; patterns in see patterns; quality of 75–5, 231–5, 263; reduction and summarizing of 249–5, 263, 267 see also diagrams; reflecting on, to find focus for analysis 65–6; relationship to quantitative see quantitative data; unstructured nature of 14–16 qualitative data analysis: acquisition of skills 6; finding a focus for 64–75, 105, 247; independent assessment and advice 242– 5, 244, 258; methodology and processes see methodology; nature of 30–1, 54–4, 271–5 paradoxes of 273; relationship to quantitative see quantitative data; traditional 4, 93, 200, 275; see also classification;
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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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Page 260 and 261: Chapter 15 Producing an account ‘
Page 262 and 263: PRODUCING AN ACCOUNT 247 An analysi
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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
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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 308 and 309: connections; description qualitativ
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