Qualitative_data_analysis

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OF MAPS AND MATRICES 225 undercurrents. We may conclude from this example that, while cathartic humour may ‘fan the flames’, it is not essential to ignite the spark of humour arising from disrupted expectations. But these are only two examples. Our mapping may be suggestive of a relationship between the two categories, but in order to examine and elucidate that relationship we have to conduct a more thorough exploration of the data. Fortunately the computer through its efficient search and retrieval facilities can make this task an easy one, mechanically if not conceptually. Indeed, within an interactive environment, the computer may provide automatic access to the data which is represented by the graphic images on our map. Another relationship we may want to map out is how categories connect over time. We may want to know how an author organizes a succession of images to achieve a comic effect; how interaction unfolds over time between different actors or agencies; or how expectations evolve and attitudes alter in the light of accumulating experiences or new information. For questions such as these, it may be useful to map out the data from a chronological perspective. For example, suppose we are interested in the effects of social interaction with other ‘dentists’ on Vincent’s feelings. This is a ‘sub-plot’ which figures less forcefully than the dentist-patient interactions we have already discussed, but which nevertheless contributes to the overall humour of the letters. Vincent has several dealings with Gauguin and we may decide to map out the Vincent-Gauguin interaction as it evolves over time. We tried something of the sort in presenting the Claire Memling story. However, we can map out the succession of events more effectively if in addition to lines and boxes, we utilize more explicitly an additional element in our diagrams, namely space. We can use space to represent time (amongst other things) and by doing so map out ‘events’ (psychological as well as social) in a more illuminating way. Figure 13.22 shows the history of Vincent’s interaction with Gauguin, arranged by time and divided between the social events and Vincent’s feelings at the time. This chronological sequence could be further refined and if sufficient information is available we could even differentiate between units of time. By mapping it out in this way, we can display more clearly the succession of events which makes up the Vincent-Gauguin story. We can also see more clearly the way Vincent’s feelings unfold as various incidents follow his decision to share offices with Gauguin. To do so we have presented a series of databits in chronological sequence—and not necessarily, incidentally, in the sequence in which they appear in the text. We could use the same procedure with categories as with databits, to set out chronologically the relationship between different elements in our analysis just as we have set out the different elements in the Vincent-Gauguin story.
226 QUALITATIVE DATA ANALYSIS Figure 13.22 Using space to represent time The computer can aid mapping by providing a set of graphic tools ideally suited for the task. We have also used the computer to map to scale the scope of the categories we have used in our analysis. There are other ways in which the computer may also be able to assist us in mapping. I have emphasized the role of mapping in making inferences from data, but mapping can also lead us back to the data as we draw out (literally) relationships which we have not yet observed. Mapping contributes to conceptual clarification which can encourage us to think about the data in new ways. One important function of the computer is to facilitate a close interaction between concepts and data.
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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
Page 190 and 191: Figure 11.11 Conditional and causal
Page 192 and 193: Chapter 12 Making connections Imagi
Page 194 and 195: Figure 12.1 The difference between
Page 196 and 197: Table 12.1 Concurrence between cate
Page 198 and 199: Table 12.4 Boolean operators for ca
Page 200 and 201: From Table 12.6 we can tell that mo
Page 202 and 203: Notice how this cross-tabulation co
Page 204 and 205: substantive connections between cat
Page 206 and 207: Figure 12.5 Following a trail of di
Page 208 and 209: Figure 12.7 Retrieving chronologica
Page 210 and 211: This approach is very exploratory,
Page 212 and 213: MAKING CONNECTIONS 197 Table 12.10
Page 214 and 215: MAKING CONNECTIONS 199 a consequenc
Page 216 and 217: Chapter 13 Of maps and matrices A n
Page 218 and 219: Table 13.1 Comparing information ac
Page 220 and 221: OF MAPS AND MATRICES 205 [In a calm
Page 222 and 223: Table 13.5 Data indices by case and
Page 224 and 225: Table 13.7 Recoding data to express
Page 226 and 227: Table 13.11 Cross-tabulating ‘occ
Page 228 and 229: Figure. 13.4 The history of the uni
Page 230 and 231: Figure 13.7 Incorporating detail by
Page 232 and 233: Figure 13.10 Comparing differences
Page 234 and 235: Figure 13.13 Adjusting scope of mos
Page 236 and 237: Figure 13.17 Comparing strength of
Page 238 and 239: Figure 13.20 Identifying positive a
Page 242 and 243: Chapter 14 Corroborating evidence
Page 244 and 245: CORROBORATING EVIDENCE 229 There is
Page 246 and 247: CORROBORATING EVIDENCE 231 describe
Page 248 and 249: CORROBORATING EVIDENCE 233 of these
Page 250 and 251: Figure 14.1 Concurrence between cat
Page 252 and 253: CORROBORATING EVIDENCE 237 possibil
Page 254 and 255: CORROBORATING EVIDENCE 239 that the
Page 256 and 257: 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
Page 266 and 267: Figure 15.3 Tree diagrams represent
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
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CONCLUSION 275 empirical analysis o
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Appendix 1 ‘If the Impressionists
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APPENDIX 1 279 though it had been o
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Appendix 2 Software The reader look
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Glossary Associating categories the
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References Allen, Woody (1978) ‘I
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REFERENCES 287 Sperber, Dan and Wil
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Capra, F. 29, 64, 99 case studies 1
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Galtung, J. 14 Geer, B. 7, 8, 29, 5
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connections; description qualitativ
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