Qualitative_data_analysis

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Figure 8.2 Weighing up the degree of refinement in initial category set volume of data which would be categorized under such a general category. At a minimum we may want to differentiate between different kinds of stereotypes, and we may also start to refine other categories to produce a much more extensive category list (Illustration 8.2). At this point, we can turn to our third question and consider relationships between the categories we use. This raises two basic issues. First, are categories inclusive or exclusive? And second, how many levels of classification do we want to use? ILLUSTRATION 8.2 DEVELOPING A MORE EXTENSIVE CATEGORY LIST Catharsis-sex Catharsis-suffering Catharsis-other Incongruity-exaggerating Incongruity-transposing Incongruity-other Values-confirming Values-subverting Victims-ridiculing Victims-empathizing Stereotype-appearance Stereotype-health Stereotype-character CREATING CATEGORIES 113 Categories can be either inclusive or exclusive. If two categories are inclusive, then we can assign them both to the same bit of data without being inconsistent. If two categories are exclusive, then we can only assign one or other to the bit of data. Categories which are exclusive are always related in some way to an underlying
114 QUALITATIVE DATA ANALYSIS Figure 8.3 Developing a more refined category list concept or overarching category. In Figure 8.3 I have differentiated between inclusive and exclusive categories by using different ways of depicting the way categories interrelate. I have used a long bar with short arms to bracket those categories which are inclusive, and a short bar with longer arms to indicate those which are exclusive. Thus the category ‘victims’ includes two exclusive categories ‘ridiculing’ and ‘empathizing’, while the category ‘stereotypes’ embraces three categories which are inclusive. Because the categories ‘victims’ and ‘stereotypes’ are inclusive rather than exclusive, we could assign ‘ridiculing’ and ‘appearance’ to the same bit of data. It doesn’t matter how you choose to depict these different relations between categories, so long as they are consistently noted in some way. Second, we have to consider levels of classification. Figure 8.3 already involves four levels of classification, with some categories ‘nested’ within others. But in terms of our category set, some of these levels can be deemed redundant when it comes to categorizing the data. There is simply no point in using the category ‘humour’—nor of distinguishing between ‘style’ and ‘substance’. These are distinctions which may be useful conceptually, but have little analytic power when it comes to organizing the data. They do not discriminate sufficiently between different aspects of the data. At most we might opt for a couple of levels, using the subcategories of style and substance, and their own subcategories. It is important to keep track of these different levels of classification, and the easiest way to do this is graphic representation. Some computer packages provide facilities to support this aspect of analysis.
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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
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 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 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
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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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