Qualitative_data_analysis

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CLASSIFICATION WHAT IS QUALITATIVE ANALYSIS? 41 Interpretation and explanation are the responsibility of the analyst, and it is his or her task to develop a meaningful and adequate account; the data merely provide a basis for the analysis, they do not dictate it (Burgess 1982). This requires the development of a conceptual framework through which the actions or events we are researching can be rendered intelligible. To interpret is to make action meaningful to others, not just or even necessarily within the terms used by the actors themselves. To explain is to account for action, not just or necessarily through reference to the actors’ intentions. It requires the development of conceptual tools through which to apprehend the significance of social action and how actions interrelate. We can grasp the nature of this task more readily if we imagine it is like the completion of a jigsaw puzzle. (A more apt analogy might be a three-dimensional puzzle, such as one of those wooden blocks which come apart with deceptive ease and fit together with frustrating difficulty, since we can represent time and process through the third dimension.) The only point of taking the puzzle apart, of course, is to find a way of putting it together again. The finished puzzle represents the results of our research, and through it we can identify different facets of social action and their mutual connection. Qualitative analysis involves more than fitting the bits together, however. Our data start as a seamless sequence, from which we ourselves must first of all cut out all the bits of the puzzle. We must cut them out in ways which correspond to the separate facets of the social reality we are investigating, but which also allow us to put them together again to produce an overall picture. How do we do jigsaws? There are some pieces of puzzle which are so unique that we can see straight away their place in the picture. These exceptions apart, classification is the key to the process. Before I can fit a piece into the puzzle, I have to assess its characteristics and assign it to some category or another. This bit is a corner, that’s an edge, this blue bit is sky, that brown bit is earth, and so on. The categories we use are organizing tools which allow us to sort out the heap of bits according to relevant characteristics. Gradually, all the blue bits together may make the sky, the brown bits the earth, the green bits a forest, and so on until we have built up a complete picture. The categories through which I initially organize the bits—flat-edged, blue, brown and green—lead on towards a new classification— sky, earth, forest—in terms of which I can finally describe the picture. Without classifying the data, we have no way of knowing what it is that we are analysing. Nor can we make meaningful comparisons between different bits of data. It would be wrong to say that before we can analyse data, we must classify it, for classifying the data is an integral part of the analysis: it lays the conceptual foundations upon which interpretation and explanation are based. This process is not unfamiliar, for classification is part and parcel of the processes of practical
42 QUALITATIVE DATA ANALYSIS Table 3.1 Implicit classifications in everyday life reasoning in everyday life, though (hopefully) social scientists may bring a more rigorous and systematic approach to it. For example, when I take the bus into town to buy slippers in a shoe shop, my action is based on a whole series of (implicit) classifications (Table 3.1). These (implicit) classifications allow us to make sense of our experience and communicate intelligibly about it. Since I suggested classification can be seen as a form of practical reasoning, let us take a practical problem as an example. Suppose we want to find a prospective partner, and decide to use the ‘Lonely Hearts’ column in our local newspaper. In Illustration 3.1, I have taken some examples from a current edition of my local newspaper, replacing the original box numbers with fictional names. How can we choose a likely prospect?. We might rely on impression and intuition, and make a selection on this basis. But being social scientists, let us opt for a more systematic approach. Illustration 3.1 Personal ads GOOD-LOOKING professional male, 44. into most sports, theatre, eating out and flying, wishes to meet sincere, caring, mature lady, 30–50, for friendship, possible romance. [Alex] GORGEOUS GEMINI seeks sexy Sagittarian, for fun nights out; no expense spared for £2. 00; who could resist; hope I’m still 26 before you answer; photo appreciated. [Pat] GORGEOUS (immodest or deluded?) single professional female, 22, seeks single, professional, handsome hunk to restore my faith in human PUSSYCAT seeks tomcat for exchanging pedigrees. Must be well groomed, tame and handsome, aged between 18– 27. Would like nights out, friendship and cosy nights in No scrooges please! [Alice] LONELY TALL dark male, 24, seeks fun loving, nonsmoking female of similar age who enjoys cinema, music, sports, nights out and in, for possible romance. Photo appreciated. [Neil] LIFE BEGINS at 40. Are you fit? Glamorous granny seeks grandad to go out and show them how to jive or stay in and babysit and find romance. [Leslie]
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Qualitative data analysis Learning
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First published 1993 by Routledge 1
Page 6 and 7: Glossary 283 References 285 Index 2
Page 8 and 9: 9.1 Categorizing data—1 120 9.2 C
Page 10 and 11: 3.1 Personal ads 42 5.1 ‘The libr
Page 12 and 13: Preface A new book on qualitative d
Page 14 and 15: analyse humour from any number of p
Page 16 and 17: Q. What colour is snow? A. White. C
Page 18 and 19: INTRODUCTION 3 approaches, interest
Page 20 and 21: INTRODUCTION 5 qualitative methods.
Page 22 and 23: INTRODUCTION 7 First of all, I take
Page 24 and 25: INTRODUCTION 9 the purposes of anal
Page 26 and 27: WHAT IS QUALITATIVE DATA? 11 distan
Page 28 and 29: WHAT IS QUALITATIVE DATA? 13 contem
Page 30 and 31: 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 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 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
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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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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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