Qualitative_data_analysis

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Figure 11.2 Multiple hyperlinks between bits of data stored separately LINKING DATA 163 pathways within the data, with each pathway incorporating a chain of links between different bits of data (Figure 11.2). In conceptual terms, the resulting complex of pathways may be more perplexing than illuminating, if we fail to distinguish between the different strings attaching different bits of data. Retaining the visual image, we have to colour code the strings if we are going to disentangle the different paths and not become confused by twists and turns and overlaps amongst the different strings. It is not enough just to make an electronic link: we also have to describe it. Like categorizing, linking therefore has a conceptual as well as a mechanical aspect. We make hyperlinks between two bits of data only if we think they are linked conceptually in some way. For example, suppose we want to note the substantive relations between dentists and patients discussed earlier. We could do so using the links ‘treat’ and ‘pay’ as in Figure 11.3. In this respect, linking is akin to categorizing. For the sake of clarity and consistency we need to create a list of links which we can assign to the links we observe between bits of data. Like our category set, our link list ought to be conceptually and empirically ‘grounded’—conceptually in the ideas and objectives which inform our research, empirically in the observations we make of interrelationships within the data. We can devise links in much the same way as we devise categories—by deriving them from our initial questions and interests, and/or by inferring them from the data. Again we may be influenced by theoretical concerns, substantive interests, practical policy problems, or rely mainly on generating ideas
164 QUALITATIVE DATA ANALYSIS Figure 11.3 Linking dentists and patients through our interaction with the data. Like our category set, we may make a list of links in advance and modify it as we go along, or we may prefer to derive it directly from the data. However we proceed, the links we devise ought to meet both our conceptual and empirical requirements. There is no point in devising a link which is fine in theory but has no practical application; nor is there any point in making links which do not relate to the overall analysis. Like our category set, we also have to decide how long our links list should be. This again will depend upon the volume and complexity of our data and the conceptual aims of our analysis. Unlike our category set, though, our links list can be very short, confined to only a few items or perhaps even (dispensing with a list altogether) to a single item. We could not base a categorical analysis upon a single category, but we could confine our analysis of relationships to a single link, such as causality. On the other hand, if we identify too many links, we may be overwhelmed by the complexity of relationships which we can observe within the data, and lose consistency and coherence in our analysis. Potentially, there are as many links as there are transitive verbs in the English language. A transitive verb is an archetypal link which connects a subject and an object. When we parody, satirize, lampoon, mock, or ridicule something, we establish a relation between ourselves and the (unfortunate) object of our attentions. In practice, only a subset of possible relationships is likely to be of interest to the analyst. Our link list will reflect our preoccupations—if we are mainly interested in how meaning is communicated, for example, we will focus on different links than if we want to account for social action. Amongst social scientists, one common interest is in causal relationships, where X causes Y (or Y is caused by X). But causality is only one, if the most obvious, of many possible links (Illustration 11.1). We may be interested in the intelligibility of social action, for example, and devise a range of links such as explanation, exculpation, rationalization and so on. We may look for consistency or contradictions within the data, looking for areas of mutual support or opposition. Or we may on theoretical grounds anticipate more specific connections, and observe through linking how far these are evident in the data. For
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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
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 162 and 163: ILLUSTRATION 10.5 SUBDIVIDING DATAB
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 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,
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
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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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