Qualitative_data_analysis

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Table 12.4 Boolean operators for category retrievals this means BOTH categories have been assigned and it excludes those where only one OR the other has been applied. In our other retrieval we asked the computer to collect all examples where one category BUT NOT the other has been assigned to the data. Using ‘X’ and ‘Y’ for our categories, we can see that our retrievals are based on the following operations: retrieve all ‘X’ AND ‘Y’ retrieve all ‘X’ NOT ‘Y’ These are often called ‘boolean’ operators after the nineteenth century logician, George Boole, who first distinguished them. There is another operator we may find useful as a basis for retrievals: retrieve all ‘X’ OR ‘Y’ MAKING CONNECTIONS 183 This would allow us to retrieve all the data which has been assigned to either of two different categories. For example, we could retrieve all the databits assigned to ‘dentist suffering’ and ‘patient suffering’. Notice that in ordinary language, we tend to use ‘and’ rather than ‘or’ when we refer to retrieving all the databits for each of the categories—meaning ‘all X and all Y’. Confusion arises if we think about the results of the retrieval—which will include all ‘X’ and all ‘Y’—rather than the decisions on which it is based. To avoid confusion, we need to consider how the retrieval ‘operates’ on each databit. It is this decision which we must be clear about: i.e. do we include this databit or not? The boolean operators provide a logical basis for deciding on inclusion or exclusion in terms of how our categories have been assigned (Table 12.4) Cross-tabulations based on such retrievals involve looking for connections where categories do or do not concur. The evidence these retrievals produce is embedded within each databit. The databits within each cell have no relationship to each other, other than that they have been assigned to the same category or categories.
184 QUALITATIVE DATA ANALYSIS Table 12.5 Retrieval based on categories assigned to proximate bits of data This has limitations, for what if there are relationships between databits which have been assigned to non-concurring categories? We may want to look for evidence where categories may not concur, but are nevertheless close to each other in the data. For example, we could look for categories which have been assigned to consecutive databits, or categories which have been assigned to databits which fall within a certain distance of each other in the data. This distance could be defined in terms of a number of characters, a paragraph, section or even a whole case. Using either sequence or proximity as a condition for our retrieval, we can produce a crosstabulation of all the databits which have been assigned to one or other categories and do or do not fulfil this condition. For example, in Table 12.5 we have a crosstabulation of categories where they have been assigned, not to the same databit, but to one falling within a specified distance. Of course, a condition of proximity includes all the databits where categories are concurrent, overlapping or consecutive within the data. We could even impose the requirement that categories should have been assigned to the data in a certain order. So far we have made no comment on the numerical aspects of our crosstabulation. As well as assessing the databits we have retrieved, we may also take account of the number of databits accumulated in each cell (Table 12.6). For example, if virtually all the databits are concentrated in the first cell, and display the suspected association between categories, then we will doubtless feel more confident in inferring a connection between the categories than if the converse holds true, and only a small minority of databits are located in the first cell. For each category, we can consider the proportion of databits which is associated with the other category. Table 12.6 Retrieval based on categories ‘temperament’ and ‘suffering’ assigned to proximate bits of data
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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
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Figure 8.2 Weighing up the degree o
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CREATING CATEGORIES 115 Although co
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CREATING CATEGORIES 117 for an adeq
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CREATING CATEGORIES 119 well as con
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Figure 9.2 Categorizing data—2 Fi
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are entitled to expect some consist
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ASSIGNING CATEGORIES 125 we ought t
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ASSIGNING CATEGORIES 127 the data.
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have annotated this data. Suppose w
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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
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 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
Page 228 and 229: Figure. 13.4 The history of the uni
Page 230 and 231: Figure 13.7 Incorporating detail by
Page 232 and 233: Figure 13.10 Comparing differences
Page 234 and 235: Figure 13.13 Adjusting scope of mos
Page 236 and 237: Figure 13.17 Comparing strength of
Page 238 and 239: Figure 13.20 Identifying positive a
Page 240 and 241: OF MAPS AND MATRICES 225 undercurre
Page 242 and 243: Chapter 14 Corroborating evidence
Page 244 and 245: CORROBORATING EVIDENCE 229 There is
Page 246 and 247: 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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