Qualitative_data_analysis

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FINDING A FOCUS 67 Sheila The solicitor. Victoria He was OK—but, as he says himself, he does a lot of conveyancing so that’ll be seventeen phone calls just to meet him for a cup of coffee. Sheila I was rather taken with Simon—the gynaecologist. Victoria No—too inhibiting. You can’t flirt with someone who can visualise your Fallopian tubes. Sheila Now Malcolm—what do you think he meant by ‘lively social life’? Victoria Drink. Sheila He wants a breezy, uninhibited companion. Victoria To drink with. Sheila And what do you think he meant by ‘life peppered with personal tragedy’? Victoria Hangovers. Source: Wood 1990:39 (abridged) What kind of humour is this? Well, it is not slapstick comedy, nor joke-telling. The humour is woven into an ordinary conversation between two women discussing men. The conversation is ‘ordinary’, but the images are ‘extraordinary’—the drive tarmacked eight times, the seventeen phone calls. The subject may be mundane, but the style is distinctive, with an obvious leaning towards hyperbole. There is also a deflationary element to this humour, with male pretensions punctured by Victoria’s cynical comments. This sketch has an obvious target. Even on a first and superficial impression, then, we can identify some lines for analysis. We could explore the use made of the contrast between the ordinary setting and the extraordinary images, or how the humour targets certain victims. Even at this stage, our general knowledge of the data can shape our thinking, suggest some main themes, and impose some parameters on our analysis. Why analyse this kind of humour? To answer that question, we have to ask another: why humour in the first place? Before we plunge headlong into the analysis, this is the point at which to reflect upon our reasons for undertaking the research. This is a useful exercise, even if means going over old ground. No matter how well thought out our initial research aims, in the interim, our ideas may have developed or changed. Why are we interested in humour? Are we simply interested in what makes people laugh? Are we interested in how the humorist achieves her effects? Are we interested in the substance of humour—the general social
68 QUALITATIVE DATA ANALYSIS expectations and assumptions it reveals, or the particular groups it targets? Do we want to look at humour from a philosophical, psychological, sociological, anthropological, semantic or literary perspective, or some combination of these? Addressing or re-addressing such basic questions can help to develop a focus for the analysis. Another way of finding a focus is through a review of the decisions we have made in selecting material for analysis. Why have we selected the particular sites, individuals or events we have included in our observations? If they are in some way ‘typical’ or ‘exceptional’, then how are they ‘typical’ or ‘exceptional’—and why is this important? Why choose Victoria Wood as an example? How, if at all, is Victoria Wood ‘representative’ of a wider population? Is it because she is successful? Does it matter that she is a woman? Is it important that she is a contemporary comedienne? Is it significant that she works in television and radio as well as in print? How we answer these questions may help to focus our analysis. For example, we might concentrate on issues of gender, taking Victoria Wood as a significant figure amongst contemporary female humorists. Thinking through the reasons we have selected our data can give us clues about the direction our analysis should take. We also have to think about our potential audience: who is going to read the results of the research, and what would be interesting or useful to report? We can ask ourselves bluntly: who cares? If we are conducting a study of gender and humour for a television network, for example, we might identify a rather different set of analytic interests and concerns, than if we are hoping to produce a Ph.D. in media studies. We also have to ask ourselves another blunt question: so what? Trying to anticipate what our audience may value as a worthwhile analysis can help to shape our thinking even at a preliminary stage. In some cases, such as policy-oriented research, it may be possible or indeed necessary to discuss research aims and analytic objectives with others with a stake in the project’s outcomes. Questions to help find a focus • What kind of data are we analysing? • How can we characterize this data? • What are our analytic objectives? • Why have we selected this data? • How is the data representative/exceptional ? • Who wants to know? What do they want to know? Rather than start the analysis from scratch, we also need to consider what resources we can call upon. In trying to clarify our interests and aims, we may want
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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
Page 32 and 33: classified. Take the example in Ill
Page 34 and 35: Figure 2.1 Describing a bit of data
Page 36 and 37: WHAT IS QUALITATIVE DATA? 21 home;
Page 38 and 39: Figure 2.4 Nominal variable with mu
Page 40 and 41: Figure 2.6 Interval variable with f
Page 42 and 43: ILLUSTRATION 2.2 EXAMPLE OF A GRADI
Page 44 and 45: WHAT IS QUALITATIVE DATA? 29 what a
Page 46 and 47: Chapter 3 What is qualitative analy
Page 48 and 49: Figure 3.2 Three aspects of descrip
Page 50 and 51: WHAT IS QUALITATIVE ANALYSIS? 35 co
Page 52 and 53: WHAT IS QUALITATIVE ANALYSIS? 37 en
Page 54 and 55: WHAT IS QUALITATIVE ANALYSIS? 39 Th
Page 56 and 57: CLASSIFICATION WHAT IS QUALITATIVE
Page 58 and 59: nature; photograph preferred but no
Page 60 and 61: WHAT IS QUALITATIVE ANALYSIS? 45 vi
Page 62 and 63: WHAT IS QUALITATIVE ANALYSIS? 47 Fi
Page 64 and 65: Figure 3.6 Formal and substantive c
Page 66 and 67: WHAT IS QUALITATIVE ANALYSIS? 51 ne
Page 68 and 69: WHAT IS QUALITATIVE ANALYSIS? 53 If
Page 70 and 71: Figure 3.10 Qualitative analysis as
Page 72 and 73: Chapter 4 Introducing computers ‘
Page 74 and 75: ecording of an index of information
Page 76 and 77: 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 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 128 and 129: Figure 8.2 Weighing up the degree o
Page 130 and 131: 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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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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