Qualitative_data_analysis

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Techniques for interactive reading • The interrogative quintet • The substantive checklist • Transposing data • Making comparisons • Free association • Shifting focus • Shifting sequence ANNOTATING DATA READING AND ANNOTATING 93 Elizabethan gallants or inns-of-court men carried ‘table-books’ about with them, to note down interesting anecdotes, witty jests, memorable remarks and the like (Wilson 1936:xli). The qualitative analyst likewise needs a version of the Elizabethan table-book, or perhaps of its modern equivalent, the Filofax, to help him or her to annotate their data. Annotating goes hand in hand with reading the data. We need to record our observations and ideas about the data in order to prepare the ground for further analysis. And we need to record them now, while we have them; and not even five minutes later, when that flash of insight has literally flashed out of existence! Using field work techniques and even interviews, much of our data may take the form of notes. Annotating data involves making notes about the notes. To distinguish the two, we can call our notes about notes ‘memos’. Memos may relate to any aspect of the data. They may record pedantic points of information—or brilliant leaps of imagination. They can encompass a panoramic sweep of the data, or pinpoint minutiae. It doesn’t matter. They are simply a useful way of enriching our analysis. It may be helpful to think of memos as essentially private, like diary entries. We don’t have to justify them to anyone. We may record our first impressions of the data: ‘I laughed here’; ‘This isn’t funny’. We may put down a jumble of confused ideas. We may ask naïve questions. Memoing should be a creative activity, relatively unencumbered by the rigours of logic and the requirements of corroborating evidence. Memos should be suggestive; they needn’t be conclusive. There are some tasks for which memos are particularly well suited. One is to comment closely and critically on the quality of the data. Suppose I didn’t find a sketch funny, but the TV audience laughed uproariously. Was it a live audience? These are points worth noting. If the data is uneven, where are the strong and weak points? Where do we have most confidence in the data? Where is there evidence of
94 QUALITATIVE DATA ANALYSIS bias? Memoing is another road to ‘thorough’ description: What is the context? What is meant? What happens? Can we clarify or elaborate on the data? Answering these questions provides a basis for well grounded interpretation. Memoing can also be more analytic. What is this sketch about? Is this dialogue typical? Why is that a ‘key’ episode? Here we can bring into play the reading techniques outlined above, with the aim of stimulating ideas and developing new lines of inquiry (Illustration 7.2). ILLUSTRATION 7.2 USING MEMOS TO OPEN UP LINES OF ENQUIRY Data Memos Beattie What’s in your sandwiches? ‘Drugs’—we are not told what drugs, but I imagine valium etc. Connie Soap powder. I think it’s these drugs I’m on. Quite nice though. What’s yours? rather than cocaine. The implicit reference is surely to drug dependency. The humour is in the effects—not just eating soap powder sandwiches, but finding them ‘quite nice’! This is an example of incongruity—we don’t expect people to eat and enjoy soap powder sandwiches What kind of incongruity? Not exaggeration. Absurd, unbelievable. Why soap powder—a hint of domesticity here? How should we record memos? The back of an envelope will do! But there are more reliable methods. Suppose as we are reading through some data we want to add a memo. On paper, we can either write the memo on the same sheet as the data —perhaps in the margin—or we can write it on a separate sheet. Neither method may seem very satisfactory. If we write on the same sheet, we make a clear connection between the data and our memo, but we may feel unduly intrusive and in danger of disrupting the flow of information. And suppose we change our minds and decide our comment is no longer appropriate, perhaps even misleading? On the other hand, if we write on a separate sheet, we leave the data undisturbed, but then the connection between our comment and the data is less apparent. We may overlook the comment, or lose it. Perhaps the paper clip provides a partial solution:
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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
Page 58 and 59: nature; photograph preferred but no
Page 60 and 61: WHAT IS QUALITATIVE ANALYSIS? 45 vi
Page 62 and 63: WHAT IS QUALITATIVE ANALYSIS? 47 Fi
Page 64 and 65: Figure 3.6 Formal and substantive c
Page 66 and 67: WHAT IS QUALITATIVE ANALYSIS? 51 ne
Page 68 and 69: WHAT IS QUALITATIVE ANALYSIS? 53 If
Page 70 and 71: Figure 3.10 Qualitative analysis as
Page 72 and 73: Chapter 4 Introducing computers ‘
Page 74 and 75: ecording of an index of information
Page 76 and 77: Figure 4.1 A link between text held
Page 78 and 79: Computer transformations • Search
Page 80 and 81: Chapter 5 Finding a focus A Zen sto
Page 82 and 83: FINDING A FOCUS 67 Sheila The solic
Page 84 and 85: FINDING A FOCUS 69 to reflect upon
Page 86 and 87: For example, here are some question
Page 88 and 89: Transposition: Doctor: ‘I’m afr
Page 90 and 91: Figure 5.2 Main themes for analysin
Page 92 and 93: Chapter 6 Managing data Piles of pa
Page 94 and 95: MANAGING DATA 79 setting, by source
Page 96 and 97: MANAGING DATA 81 Interviewer Maybe
Page 98 and 99: Figure 6.1 Case documents kept in a
Page 100 and 101: MANAGING DATA 85 ‘Show the full r
Page 102 and 103: Chapter 7 Reading and annotating Ac
Page 104 and 105: • Official/bureaucratic commitmen
Page 106 and 107: READING AND ANNOTATING 91 women wer
Page 110 and 111: 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
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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
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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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