Qualitative_data_analysis

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Table 13.5 Data indices by case and category Table 13.6 The number of assignations of each category by case OF MAPS AND MATRICES 207 numbers. For example, Table 13.5 could then be reduced to the information in Table 13.6. The inferences which we drew from Table 13.4 about the use of different categories could equally be made on the basis of Table 13.6. Indeed because the detail has been eliminated and the data reduced to numbers, the points we noted earlier emerge with greater clarity. It may seem strange, in a qualitative analysis, to introduce numbers as a means of data reduction. However, whenever we make a qualitative judgement about the data by assigning a category to the data, we also create numerical data. For we can enumerate the qualitative decisions we make and relate them to the cases we are analysing. We can count the number of times we have assigned any category to the
208 QUALITATIVE DATA ANALYSIS data. Only prejudice would prevent us from using this information if it can help us to identify patterns within the data. On the other hand, we are faced with a matrix whose meaning has been reduced to whether or not a category has been assigned to the data. The interpretation of this information depends entirely upon the decisions we have made in creating categories and assigning them to the data. We may be wary, therefore, of drawing conclusions on the basis of such limited evidence. Any inferences we can draw about regularities and variation in the data must reflect the confidence we can place in our initial conceptualizations. The matrix can be useful, but we may need to return to the original data as often as possible, for confirmation of patterns apparent within the data or to reexamine and modify our earlier judgements. With its facilities for searching and retrieving data, the computer should make it possible to do so with a minimum of fuss. If our variables describe properties of cases, we may be able to ‘recode’ these values to make a more meaningful classification. Suppose, for example, that it made sense to think of the degree to which these letters rely upon stereotypes of artistic temperament. Then we might ‘recode’ our data to discriminate between those where Woody Allen relies heavily on artistic stereotypes and those where he doesn’t use them much if at all. We might distinguish two or more values of the variable ‘temperament’, such as ‘high’ and ‘low’. To recode the data in terms of these values, we would need to identify a cut-off point above which the existing values would be recoded as ‘high’ and below which they would be recoded as ‘low’. Of course we also have to decide whether to record values which are at the cut-off point itself as ‘high’ or ‘low’. Deciding where to make a cut-off point is a conceptual and empirical problem. Empirically, we may be influenced largely by the pattern of existing values. Is there a natural break in the data? Does some subdivision of the range of values (e.g. midway) give a useful distinction between ‘high’ and ‘low’ values? Conceptually, we have to be sure that it is meaningful to describe some values as ‘high’ and others as ‘low’. These are of course relative terms, and we have to consider the context in which they are made. If stereotypes of artistic temperament are used five times in the space of a short letter, can this reasonably described as ‘heavy’ usage? By reducing the number of values, we not only summarize the data but also render it more intelligible. We translate numbers into meaningful values. Our measurement may be less exact, but our classification is more useful. The matrix in Table 13.7 shows one possible recoding of the values in Table 13.6. We can see at a glance the patterns of usage for some of the main categories in our analysis. On the other hand, these patterns are a product of a series of decisions we have made in interpreting the data through categorizing and recoding it. We must therefore avoid any presumption that in some sense they really exist in the
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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
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• What generally constitutes a
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Databits Categories 5. Now she is u
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Chapter 10 Splitting and splicing T
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Using Hypertext linking, whenever n
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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
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 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 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
Page 248 and 249: CORROBORATING EVIDENCE 233 of these
Page 250 and 251: Figure 14.1 Concurrence between cat
Page 252 and 253: CORROBORATING EVIDENCE 237 possibil
Page 254 and 255: CORROBORATING EVIDENCE 239 that the
Page 256 and 257: CORROBORATING EVIDENCE 241 seductiv
Page 258 and 259: CORROBORATING EVIDENCE 243 research
Page 260 and 261: Chapter 15 Producing an account ‘
Page 262 and 263: PRODUCING AN ACCOUNT 247 An analysi
Page 264 and 265: Figure 15.2 The whole is greater th
Page 266 and 267: Figure 15.3 Tree diagrams represent
Page 268 and 269: PRODUCING AN ACCOUNT 253 associatio
Page 270 and 271: 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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