Qualitative_data_analysis

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FINDING A FOCUS 69 to reflect upon our own experience of humour. For example, one factor stimulating my own interest in the humour of Victoria Wood was the laughter one of her sketches provoked when we watched it during a discussion of interviewing in a methods class. Humour is part of everyday life, and we probably all have our favourite humorous programmes, books, anecdotes and incidents. Reflection on personal experience can be a rich mine of insights and ideas which may prove useful in directing our analysis. The general culture may provide another rich source of ideas for analysis. There is a ‘non-academic’ literature on humour, to be found in newspaper reviews, comedian biographies and the like. There may also be non-literary sources, such as radio and TV interviews. I write in a city which is fortunate to have an annual book festival devoted to literary issues, such as the social functions of humour. Academics thankfully have no monopoly over the dissection of humour. Indeed I suspect discussion of what was ‘funny’ or ‘not funny’ on last night’s TV may run the weather a close second as a topic of everyday conversation. As we have seen, accumulated knowledge need not refer only to the results of previous research and scholarship. But undoubtedly the academic literature may prove one of the most useful sources of analytic strategy. This is because previous research or scholarship may have examined issues in a thorough and systematic way. Some of the concepts and relationships observed or hypothesized in previous work may suggest questions which require further exploration. Of course, replication is seldom on the agenda in social science research, since novelty has a strong career value; and it is even less likely to commend itself to qualitative researchers. On the other hand, plagiarism is rife. In a research context, it makes sense to ‘borrow’ freely from other authors, providing we acknowledge our sources and subject their ideas to a critical assessment through examining evidence. In this case, we could review the existing literature on humour. Fortunately, computer-based bibliographies now allow us to track down relevant literature through a few interactive exchanges at the computer terminal. We can locate immediately all the texts catalogued under humour, or related subjects such as wit or comedy. We can explore other parts of the bibliography, using search facilities to find texts which include relevant words in their title, such as ‘laughter’. Our search may take us across different disciplines, for example, to texts on the psychology or anthropology of humour. It may take us through the nooks and crannies of the bibliography to unsuspected texts. We may begin with an eclectic approach to the literature, but we must quickly sort out what texts seem promising and which seem irrelevant. Here we cross our first Rubicon, for the literature tends to be organized along disciplinary lines. Do we adopt a literary approach, and explore issues of style, idiom and the use of irony? Do we take a linguistic view, and investigate the communication of humorous intent
70 QUALITATIVE DATA ANALYSIS through language? Do we want to take a psychological, anthropological or sociological perspective? Do we want to confine ourselves to a particular perspective, or attempt a more inter-disciplinary analysis? No doubt our background, training and inclinations will influence this decision. But we should try to articulate, as clearly as possible (and on paper), our reasons for deciding one way or another. In these reasons, we may find some clues to how we may begin to analyse the data. As an example of the value of literature in sign-posting different directions for the analysis, consider the comments in Illustration 5.2 on women’s comedy, taken from an article by Merrill in a recent collection exploring perspectives on women and comedy (Merrill 1988). In the space of one short article, we have a rich range of potential topics for analysis. These statements and questions may spark off any number of ideas about how to proceed. We can identify some questions or even some hypotheses which can be explored through our analysis. ILLUSTRATION 5.2 COMMENTS ON FEMINIST HUMOUR Because humor depends upon a perception of events or behaviour as unexpected or incongruous, the individual who publicly points up such inconsistencies risks making a statement about the status quo. Consequently, satire, irony and comedy pointedly directed can wield enormous social and political power. 272 Because of conventional sex roles, women have had to be practical; pragmatists rather than idealists. The mundane day-to-day business of life has been women’s domain… Women’s comedy is infused with these realistic associations. 274 …the point-of-view represented in feminist comedy is one that affirms women’s experience, rather than denigrating it… Oppressive contexts and restrictive values would be ridiculed, rather than the characters who are struggling against such restrictions. 275 Comedy depends on perspective. A certain aesthetic distancing or tension between empathy and judgement is needed for one to view the irrational or incongruous as comic. 276 Tomlin’s characters display insight and integrity which allows them to be selfcritical without being self deprecating. 277 Comedy is both an aggressive and intellectual response to human nature and experience…(feminist humor) addresses itself to women and to the multiplicity of experiences and values women may embody. 278 Humor addressed to women; comedy that recognizes the value of female experience may be an important step in developing a culture that allows women to self-critically question the stereotypes that have governed our lives. 279 Source. Merrill 1988
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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
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 82 and 83: FINDING A FOCUS 67 Sheila The solic
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
Page 132 and 133: 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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