Qualitative_data_analysis

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Figure 15.7 Procedures for assigning categories in algorithmic form PRODUCING AN ACCOUNT 261 instead (perhaps unwisely, given our discussion of fabricating evidence!) on the audience’s trust in the integrity of the analyst. This makes even the most cursory scrutiny of the reliability of their procedures impossible. But the time spent on laying out our decision-making processes is not wasted if it gives our audience an insight into our analytic procedures and enhances their confidence in our results. Turning now to validity, I defined a valid account as one ‘which can be defended as sound because it is well-grounded conceptually and empirically’. There is a paradox here, of course, for how do we know that an analysis is well-grounded empirically, if our only access to that data is through the analysis? We have no ‘independent’ access to data other than through the concepts used in our analysis.
262 QUALITATIVE DATA ANALYSIS The observations we have made have been informed throughout by our conceptualizations. No more than in any other form of research can qualitative analysis provide some kind of ‘privileged’ access to ‘how things really are’ independently of our ways of thinking about them. Before we despair, however, or abandon ourselves to an unalloyed subjectivity (which for a social scientist is much the same thing), let us take heart from our example of time. We can accept without qualms the idea that my watch is ‘valid’ if it tells the right time. And how do we tell the ‘right’ time? To check the validity of my watch, I rely on other measurements of time. In my case, I watch the six o’clock news, or I phone the speaking clock. If the time on my watch is consistent with the time as measured by other ‘instruments’ like the speaking clock, I am happy to accept it as a valid measure. This is so, incidentally, even though I have no idea of what time ‘really’ is. The common sense view of time is that an interval between two events would be the same for all observers. This does not fit with my psychological experience of time (I find the hour in which I give a lecture tends to pass very quickly, but the hour in which I listen to a lecture passes very slowly!). Nor does it fit with the modern physicist’s view, where time is not separate from space, each observer has his own measure of time, and identical clocks carried by different observers won’t necessarily agree (Hawking 1988:18–22). This process of checking the validity of my watch is more complex than it might at first appear. Let us consider it more closely. Suppose I have a digital watch with a range of functions. To check the time, I look at my watch and read off a set of numbers. What have these numbers got to do with what time it is? The answer may be—nothing. I could be looking at the date, or the time that I have set the alarm to wake me up tomorrow morning. The first thing I have to be sure of is that the numbers I’m looking at are about what time it is, and not about something completely different. If my watch only has one function, then of course I can be pretty sure. But even so, this assumes that I have at some point set the watch so that it will tell the time. If my niece has in the meantime got hold of my watch, and treated it as an interesting toy rather than a measuring instrument, I may be misled. If the reading I make doesn’t ‘fit’ with the reading I’d expect if my watch was telling the time, I may begin to doubt its validity as a measure of time. This fit (or lack of it) between our observations (the numbers) and our concept (‘telling the time’) is aptly called the ‘face’ validity of a measure. We have to decide whether ‘on the face of it’ the observation is consistent with the concept. In the case of the watch, this may be so straightforward that we take it for granted. But in qualitative analysis, the fit between observations and concepts may be far less obvious. Once I’m satisfied that my watch is telling the time, I want to know if it’s the ‘right’ time, and I check it against the time given by the speaking clock. In making this comparison, I am implicitly assuming that telling the time with my watch is
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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
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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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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
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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
Page 272 and 273: Figure 15.5 Different writing strat
Page 274 and 275: PRODUCING AN ACCOUNT 259 the vibrat
Page 278 and 279: PRODUCING AN ACCOUNT 263 consistent
Page 280 and 281: Table 15.1 Databits assigned to cat
Page 282 and 283: Table 15.3 Distribution of response
Page 284 and 285: PRODUCING AN ACCOUNT 269 To be subj
Page 286 and 287: PRODUCING AN ACCOUNT 271 analysts t
Page 288 and 289: Figure 16.3 Analysis as an iterativ
Page 290 and 291: CONCLUSION 275 empirical analysis o
Page 292 and 293: Appendix 1 ‘If the Impressionists
Page 294 and 295: APPENDIX 1 279 though it had been o
Page 296 and 297: Appendix 2 Software The reader look
Page 298 and 299: Glossary Associating categories the
Page 300 and 301: References Allen, Woody (1978) ‘I
Page 302 and 303: REFERENCES 287 Sperber, Dan and Wil
Page 304 and 305: Capra, F. 29, 64, 99 case studies 1
Page 306 and 307: Galtung, J. 14 Geer, B. 7, 8, 29, 5
Page 308 and 309: connections; description qualitativ
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