Qualitative_data_analysis

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Chapter 6 Managing data Piles of papers spilling from the desk across the floor and strewn over every available surface: such is the archetypal image of the qualitative analyst at work. Of course, this shrewd paragon of academic virtue knows exactly where everything is and can always find a particular paper within moments: the supposed chaos is more apparent than real. This image is comforting, but hardly credible. In the real world, a chaotic mass of papers spread across the room is a recipe for confusion, error and frustration; finding that particular paper may take not moments but hours or even days. In practice, therefore, the analyst depends on storing and filing data in an organized and systematic way. In any case, the image of the paper-strewn room has been superseded by that of the desktop computer. The data which once occupied a room now occupies a small disk no larger than a thick notepad. The scramble through endless papers is replaced by the search through innumerable files held on the computer. Good analysis requires efficient management of one’s data. The opportunities for error are, in Gibbon’s words, ‘various and infinite.’ Data must be recorded fully and accurately, and that may be easier said than done, as anyone who has tried transcribing audio tapes can verify. For example, try to decipher a group interview with a dozen very animated respondents, all speaking rapidly in broad dialect and sometimes several speaking at once! Apart from ‘technical’ difficulties, there are problems in ensuring data is of consistently high quality where observations have been directed at more subtle aspects of social interaction. Take the sketch in Illustration 6.1 for example. The dialogue can be recorded with relative ease, but we may also want to record other details, such as what Thelm and Pat are wearing (bikini ‘overalls’ over their everyday clothes), what they are doing (chain-smoking while supervising the baths) and other aspects of their interaction apart from the dialogue. The more complex the data, the more risk that some information will be overlooked or not recorded accurately and that as a result the quality of the data will be uneven. Our first concern, therefore, is to check the data for accuracy and to look for possible gaps and inconsistencies. Where the data is of uneven quality, it may be possible to ‘repair the damage’ by further
78 QUALITATIVE DATA ANALYSIS research. Where this is not possible, our anxieties and reservations about the quality of the data should be recorded for future reference. ILLUSTRATION 6.1 ‘TWO ATTENDANTS AT A TURKISH BATH’ Thelm My God, if her bum was a bungalow she’d never get a mortgage on it. Pat She’s let it drop. Thelm I’ll say. Never mind knickers, she needs a safety net. Pat She wants to do that Jane Fonda. Thelm That what? Pat That exercise thing—nemobics. Thelm What’s that? Pat Our next-door does it. We can hear her through the grate. You have to clench those buttocks. Thelm Do you? She’ll never get hers clenched—take two big lads and a wheelbarrow… Source: Victoria Wood 1985:107 Since some of this data is held in video rather than text format, we need to find ways of recording such data for analysis. Unfortunately computer storage of significant amounts of video data is not yet possible, though the computer can cope with static images (e.g. pictures and diagrams) providing substantial memory resources are available. However, most of the software available at present for analysing data is oriented to analysing text, and while some can accommodate graphics, analysis is still based on textual description of the material. This translation of pictures into text is also likely to pose problems for achieving a consistent and complete recording of relevant data. Data should be recorded in a format which facilitates analysis. Decisions made at this stage can have repercussions later. Suppose we have conducted some interviews and we have to record responses to a large number of open-ended questions. Do we file the data as a complete interview for each case, or as a set of responses for each question? The former allows the interview to be recorded and read as a whole, but inhibits ready comparison between related responses; the latter facilitates comparison between responses, but makes it difficult to see the interview as a whole. Fieldwork notes pose similar problems. Do we file the data chronologically, by
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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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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 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 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
Page 134 and 135: CREATING CATEGORIES 119 well as con
Page 136 and 137: Figure 9.2 Categorizing data—2 Fi
Page 138 and 139: are entitled to expect some consist
Page 140 and 141: 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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