Qualitative_data_analysis

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are entitled to expect some consistency in the divisions which are drawn within the data. To illustrate this process, let us look at a letter ostensibly written by Vincent Van Gogh to his brother Theo (Illustration 9.1), but actually one of ten written by Woody Allen (1978) in his article ‘If the Impressionists had been Dentists’ (see Appendix 1). ILLUSTRATION 9.1 TWO WAYS OF IDENTIFYING ‘BITS’ OF DATA. Dear Theo Dear Theo [Will life never treat me decently? I am wracked by despair! My head is pounding.] [Mrs Sol Schwimmer is suing me because I made her bridge as I felt it and not to fit her ridiculous mouth.] [That’s right! I can’t work to order like a common tradesman.] [I decided her bridge should be enormous and billowing with wild, explosive teeth flaring up in every direction like fire!] [Now she is upset because it won’t fit in her mouth!] [She is so bourgeois and stupid, I want to smash her.] [I tried forcing the false plate in but it sticks out like a star burst chandelier.] [Still, I find it beautiful. She claims she can’t chew! What do I care whether she can chew or not!] [Theo, I can’t go on like this much longer!] [I asked Cézanne if he would share an office with me but he is old and infirm and unable to hold the instruments and they must be tied to his wrists but then he lacks accuracy and once inside a mouth, he knocks out more teeth than he saves.] [What to do?] Vincent Vincent Source: Woody Allen 1978:188 ASSIGNING CATEGORIES 123 [Will life never treat me decently? I am wracked by despair! My head is pounding. Mrs Sol Schwimmer is suing me because! made her bridge as I felt it and not to fit her ridiculous mouth. That’s right! I can’t work to order like a common tradesman. I decided her bridge should be enormous and billowing with wild, explosive teeth flaring up in every direction like fire! Now she is upset because it won’t fit in her mouth! She is so bourgeois and stupid, I want to smash her. I tried forcing the false plate in but it sticks out like a star burst chandelier. Still, I find it beautiful. She claims she can’t chew! What do I care whether she can chew or not!] [Theo, I can’t go on like this much longer! I asked Cézanne if he would share an office with me but he is old and infirm and unable to hold the instruments and they must be tied to his wrists but he lacks accuracy and once inside a mouth, he knocks out more teeth than he saves. What to do?] No doubt we could argue over the fine detail of these decisions; the break between one bit of data and another is a matter of judgement, and alternatives might be equally plausible. However, there is some consistency in how the data is
124 QUALITATIVE DATA ANALYSIS broken down within each example, and a sharp contrast between the two. In the first example, bits are narrowly defined in terms of relatively small ‘units of meaning’ with the break between one bit and another often—but not always—demarcated by the end of a sentence. Each bit conveys a distinct element within the narration. In the second example, bits are defined more broadly in terms of larger ‘units of meaning’ which embrace distinct episodes within the text, the first concerning the lamentable business of Mrs Schwimmer’s bridge and the second the frailties of Cézanne. Although I have presented a clear division between different bits of data, it is possible to overlap the different bits, so that one bit starts within another. In Illustration 9.2, for example, the second bit of data, demarcated by the double brackets, overlaps with the first. ILLUSTRATION 9.2 OVERLAPPING BITS OF DATA [Will life never treat me decently? I am wracked by despair! [[My head is pounding.] Mrs Sol Schwimmer is suing me because I made her bridge as I felt it and not to fit her ridiculous mouth.]] Whenever we divide data into bits in this way, meaning is lost because the data is abstracted from its context. This is true, regardless of the size of the bits of data we identify. For example, the import of the episode with Cézanne can only be understood fully in the context of the preceding account of the incident with Mrs Sol Schwimmer. Even if we had taken the whole letter as our bit of data, this can only be fully understood within the context of the series of letters which Vincent writes to his brother Theo. This loss of meaning is inescapable. On the other hand, the bits of data also acquire a new meaning, in relation to other bits of data with which we implicitly or explicitly compare them. Our choice between alternative methods of breaking up the data may be dictated by how fine-grained we want our analysis to be, and how narrowly or broadly focused our categories are. We may be concerned not to include too much information within a single bit of data, leading to a proliferation of assigned categories for each databit. On the other hand, we may be reluctant to adopt too narrow an approach, if this means we lose some sense of the overall meaning of the data. When we break the date up too finely we may lose important contextual information about the data. This problem is less severe where the computer allows us to retrieve instantly the context of any bit of data, but we will still want to respect the ‘integrity’ of the data. However we choose, once having chosen a particular path
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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
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 108 and 109: Techniques for interactive reading
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 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 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
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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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