Qualitative_data_analysis

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ILLUSTRATION 10.5 SUBDIVIDING DATABITS BETWEEN SUBCATEGORIES Discomfort Disfigurement I tried forcing the false plate in but it sticks out like a star burst chandelier. If we had initially used broader distinctions within the data, for example treating the whole episode with Mrs Sol Schwimmer as a single databit, then the scope for making further distinctions within the data at this stage would obviously be that much greater. The rationale for making further divisions between bits of data depends upon much the same factors as those we considered in relation to categorizing the data in the first place. If our databit is too extensive we may end up assigning too many subcategories to the databit, and the relation between the databit and the subcategory may be obscured by the presence of irrelevant data. It is certainly convenient if there is an immediately transparent relation between subcategory and databit and this may only be possible through subdividing the databit. On the other hand, we may be reluctant to subdivide data too far lest we lose important contextual information. Fortunately, this problem is reduced for subcategorized as for categorized data by the ability of the computer to locate the data immediately in the context from which it has been taken. Subdividing databits does not require the assignation of subcategories, since we can subdivide databits using existing categories. In other words, we can split databits without splitting categories. We can think of this process as recategorizing rather than subcategorizing the data. How far we recategorize databits may depend on just how broad brush our initial analysis has been. If we have used very general, common sense categories in our initial analysis and assigned correspondingly large bits of data, we may want to recategorize in terms of more specific categories and more narrowly defined bits of data. This may or may not go hand in hand with splitting our initial categories into subcategories. SPLICING CATEGORIES SPLITTING AND SPLICING 147 When we splice ropes, we join them by interweaving different strands. When we splice categories, we join them by interweaving the different strands in our analysis. We split categories in a search for greater resolution and detail and splice them in a search for greater integration and scope. The fewer and more powerful our categories, the more intelligible and coherent our analysis.
148 QUALITATIVE DATA ANALYSIS The most straightforward example of splicing categories is simply the reverse of splitting them. Suppose we had begun with a fine-grained analysis involving the categories ‘disability’ ‘discomfort’ and ‘disfigurement’. Then we could splice these categories together by integrating them under the over-arching category ‘suffering’. Like splitting categories, splicing is likely to be an increasingly focused activity. We do not want to include every strand in our analysis. We want to concentrate our efforts on the central categories emerging from our preliminary analysis. How do we decide what is central? As always, we have to pay heed to both the conceptual and empirical relevance of the categories we have employed so far. Conceptual relevance can be established in terms of our main interests and objectives as these emerge from our preliminary analysis of the data. By this stage our ideas may be taking shape and we may be able to identify the main directions in which we want the analysis to go. Ideas which seemed interesting at first may no longer seem so; while other issues, apparently marginal at first, may now assume centre stage. The data provides the anvil upon which we can shape and sharpen our ideas. Some categories may apply to the data much more effectively than others. This is likely to be evident in the amount of data which is encompassed by our categories. Those which become central are likely to encompass most of the data, while those which become marginal may be weakly represented in the data. Likely—but not necessarily. We have to avoid a mechanical approach and allow empirical relevance pride of place over empirical instantiation. We must judge whether the extent to which a category is represented in the data indicates its relevance to our understanding of the data. A small point may mean a great deal. For example, imagine we have a scene of two people meeting, and one person is holding something and pointing with outstretched arm towards the other. What the person is holding may matter a great deal in our interpretation of the scene. Is it coke—or cocaine? Some points, apparently small details in themselves, may be pivotal to our comprehension of the rest of our data. Nevertheless, it would be a strange analysis which failed to encompass the bulk of the data upon which it is based. The small point can only be pivotal if we have also grasped the rest of the scene. Then and only then can we understand its implications. So although not all our central categories need to be richly represented in the data, some must. Overall, we can expect the categories which are central to encompass the bulk of the data. Once we have selected the main strands of our analysis, we can begin to interweave them. Here we shift from making comparisons within categories to making comparisons between them. How can such comparisons be made? At a conceptual level, when we first create our categories we may already have identified, implicitly or explicitly, some possible relationships between them. Recall our discussion of humour, and the logical relationships which we identified between
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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
Page 112 and 113: Figure 7.1 Relating data to key the
Page 114 and 115: READING AND ANNOTATING 99 may retur
Page 116 and 117: CREATING CATEGORIES 101 types of ph
Page 118 and 119: CREATING CATEGORIES 103 conceptual
Page 120 and 121: CREATING CATEGORIES 105 Categorizat
Page 122 and 123: Table 8.1 Alternative category list
Page 124 and 125: This is only a starting point. Our
Page 126 and 127: CREATING CATEGORIES 111 undertaking
Page 128 and 129: Figure 8.2 Weighing up the degree o
Page 130 and 131: CREATING CATEGORIES 115 Although co
Page 132 and 133: CREATING CATEGORIES 117 for an adeq
Page 134 and 135: CREATING CATEGORIES 119 well as con
Page 136 and 137: Figure 9.2 Categorizing data—2 Fi
Page 138 and 139: are entitled to expect some consist
Page 140 and 141: ASSIGNING CATEGORIES 125 we ought t
Page 142 and 143: ASSIGNING CATEGORIES 127 the data.
Page 144 and 145: have annotated this data. Suppose w
Page 146 and 147: ASSIGNING CATEGORIES 131 suddenly t
Page 148 and 149: • What generally constitutes a
Page 150 and 151: Databits Categories 5. Now she is u
Page 152 and 153: Chapter 10 Splitting and splicing T
Page 154 and 155: Using Hypertext linking, whenever n
Page 156 and 157: SPLITTING AND SPLICING 141 databits
Page 158 and 159: Category Subcategories Suffering Di
Page 160 and 161: Figure 10.1 Levels of subclassifica
Page 164 and 165: Figure 10.2 Initial relationships b
Page 166 and 167: If we want to make this change, it
Page 168 and 169: Figure 10.5 Reassessing relationshi
Page 170 and 171: SPLITTING AND SPLICING 155 Although
Page 172 and 173: Figure 10.8 Comparing subcategories
Page 174 and 175: Figure 10.9 Shifting the analytic e
Page 176 and 177: Chapter 11 Linking data Categorizin
Page 178 and 179: Figure 11.2 Multiple hyperlinks bet
Page 180 and 181: example, we may ask what it is abou
Page 182 and 183: Figure 11.5 Linking and categorizin
Page 184 and 185: Figure 11.7 An explanatory link bet
Page 186 and 187: Table 11.2 Multiple links between d
Page 188 and 189: 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,
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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
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CORROBORATING EVIDENCE 243 research
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Chapter 15 Producing an account ‘
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PRODUCING AN ACCOUNT 247 An analysi
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Figure 15.2 The whole is greater th
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Figure 15.3 Tree diagrams represent
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PRODUCING AN ACCOUNT 253 associatio
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worrying unduly about the connectio
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Figure 15.5 Different writing strat
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PRODUCING AN ACCOUNT 259 the vibrat
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Figure 15.7 Procedures for assignin
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PRODUCING AN ACCOUNT 263 consistent
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Table 15.1 Databits assigned to cat
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Table 15.3 Distribution of response
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PRODUCING AN ACCOUNT 269 To be subj
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PRODUCING AN ACCOUNT 271 analysts t
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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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