Qualitative_data_analysis

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MANAGING DATA 79 setting, by source or by topic? Whatever basis we choose for filing data, it is likely to facilitate analysis in some directions and inhibit it in others. In filing the data, we already have to address issues which are fundamental to our analysis. What is the focus of our research? If we are conducting a case study, then what constitutes the case? If we intend to make comparisons across a number of cases, what is the ‘population’ we are studying? Our answers to these questions will determine what we can say about our data at the end of the day. A decision need not be difficult just because it is fundamental. We may have no problem in deciding what constitutes a case, for example, where our data results from a set of unstructured interviews, and our research aim is to analyse the perceptions and attitudes of our respondents. Each respondent can be regarded as a ‘case’ and each interview filed under a reference to the appropriate case. Matters become more complicated where our data derives from a variety of sources, and our analysis has multiple foci, such as groups and agencies as well as individuals. How should we record the material for our analysis of humour? How we answer this question depends on what we want to draw conclusions about at the end of the analysis. Do we want to discuss and compare each of the TV programmes, for example? Or do we want to focus our inquiry on each of the several sketches which taken together constitute a programme? If we split the data up into sketches, it may be more difficult to relate the data to the overall programmes. We may lose important information about how one sketch leads into another, how sketches of different types are put together, and so on. On the other hand, if our main interest is in how the humour works within individual sketches, there is no point in filing the material by programme. This will make it more difficult to make comparisons between the different sketches. Fortunately, these are not either/or decisions. One virtue of the computer is that it may provide facilities for reformatting data. For example, we may decide to file by programme, but still be able to reformat the data so that we can take the sketches as our basic cases—or vice versa. We can amalgamate files to make new ones, bringing all the relevant sketches together to make a programme file; or we can disaggregate files, splitting programme files up to make files for their constituent sketches. If one reformatting procedure is simpler than another, this may influence our initial decision about how to file the data. As well as considering the central focus of the analysis, we also have to consider the ease and efficiency with which we can file and reformat the data. We may opt for sketches as our basic cases because they are convenient in terms of length and content; by comparison, programmes may be too complex and unwieldy. Decisions in terms of convenience can be justified if they coincide with or at least don’t contravene our analytic interests. In formatting data, we must ensure that the data is fully referenced. This may mean no more than specifying a reference for each case included in our study:
80 QUALITATIVE DATA ANALYSIS ‘Sketch01’ or ‘Programme01’ for example. However, we may want to reference the data more fully, by including details of who recorded the data, how, when and where it was recorded, and so on. This information may provide important contextual material and may also be useful if we want to make comparisons between cases in terms of how the data was obtained. For example, we may want to compare sketches we have seen ‘live’ in the theatre with those seen on video or those which we have only read, but not seen performed. Nevertheless, unless this reference information may illuminate the data in some way, or promises to provide a useful basis of comparison, there is no point in recording it. As well as reference information about cases, we may also want to record reference information about the data in each case. We might reference the ‘Turkish Bath’ sketch as ‘Case 001’, and record some further information about when, where and how this was observed. But in addition to information about the case, we also have information about the data itself. We know who is speaking at any one point in the data, and can therefore record the source of the data as it varies through the case material. Most interview data will contain at least two sources—the respondent and the interviewer. Other forms of data—group discussions, meetings, informal conversations—may include several sources. Referencing the data by source is useful, but only if it sheds light on the data, or provides a basis for future comparison. If we have no interest in ‘Pat’ or ‘Thelm’ as individuals, we may dispense with the information about sources and concentrate entirely on the dialogue itself. The computer has a capacity to locate and retrieve information which is remarkable by human standards. For example, we can ask it to collate all the contributions which Pat (or Thelm) makes to the dialogue. The computer can hunt through all the cases for contributions which Pat has made, and record these in a separate file. The computer can also improve our efficiency in managing data. The trick is to file information only once, and then obtain access to it as required. If we file information about different speakers (e.g. ‘S1 is Pat, S2 is Thelm’) then we can reference the data more economically and retrieve the full reference whenever required. Anyone who has obtained qualitative data through a standardized questionnaire will immediately see the value of this facility. The questions can be filed once, and then it is sufficient to record a brief reference (e.g. Q1) for the data. The full question can be displayed on screen as required. Take Illustration 6.2 as an example. ILLUSTRATION 6.2 RECORDING DATA FULLY BUT INEFFICIENTLY
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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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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 92 and 93: Chapter 6 Managing data Piles of pa
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
Page 142 and 143: 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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