Qualitative_data_analysis

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nature; photograph preferred but not essential. [Morag] SLIM 34 year old female, 5′ 8′ tall, reasonably nice looking, seeks tall handsome gent who likes eating out and socialising Must have good sense of humour and like children. [Fiona] WHAT IS QUALITATIVE ANALYSIS? 43 SCORPIO MALE tall, slim, handsome and fun loving, seeks good looking professional female for nights out, wild times, romance and fun. Photo please. [Alistair] Even in this small selection of ads there are some surprises. For example, Morag tells us she’s single; we can presume that not many of those advertising in the personal columns would tell us otherwise! On the other hand, Pat doesn’t tell us whether s/he is male or female. Someone may be in for a shock. Perhaps gender doesn’t matter to Pat, though most of the other advertisers seem to think it does! Most of the information supplied by these erstwhile suppliants is qualitative; some, such as age, is quantitative. Incidentally, this balance of information offered in the personal columns mirrors that available in most other areas of social life. The qualitative data gives us information about a whole range of ‘qualities’, such as whether the individual is ‘sincere’, ‘sexy’, ‘fun-loving’ and so on. Much of this information is straightforwardly descriptive: it allows us to form an idea of the individual’s character and interests. The personal ads are literally ‘unclassified’; but in order to choose a mate we can sort the data according to relevant characteristics, i.e. we can classify it. The first thing we might do is assign individuals to various categories, according to character, interests or the like; for example, this one is ‘lonely’, that one ‘likes eating out’; this one is ‘glamorous’, that one ‘likes nights out’. By sorting the information into different categories, we can make comparisons between cases much more effectively. If we want someone interested in sports, for example, we can identify all those who like sports, and then compare them. Or we may want to discount all those who fall within a particular category, for example, such as those who suggest a photo would be appreciated. We may be interested in all those who belong to a particular category or combination of categories, such as those who express interest in ‘possible romance’. There is no obvious limit to the number of categories, and no reason why they shouldn’t overlap. You can enjoy as many hobbies as you like, and if you like ‘fun nights out’ or ‘cosy nights in’ that certainly doesn’t preclude any other activities (only hinted at) of which politeness prohibits mention. Few advertisers frankly admit to an interest in sex! We can picture categorization as a process of funnelling the data into relevant categories for analysis (Figure 3.3). The data loses its original shape, but we gain by organizing it in ways which are more useful for our analysis.
44 QUALITATIVE DATA ANALYSIS I have chosen a straightforward example, but categories may have to be more sensitive to capture the subtleties of the data. For example, the style of the ad may be as revealing as the content. How can this be categorized? What do different styles convey? And are there oblique messages which the advertisers are trying to convey? Just asking these questions is sufficient to underline that categorization is a conceptual process. It involves ideas and it requires thought, sometimes a great deal of it. Once the data has been organized into categories, we can retrieve it in a variety of ways. We may want to find someone who has a number of characteristics we find desirable in a prospective mate. We may want to exclude anyone with a characteristic or combination of characteristics we dislike. We want to restrict our search to those individuals who share a particular set of characteristics. From a practical point of view, categorizing the data allows us to make comparisons more effectively and hence locate individuals who seem most likely to fit the bill. From a research point of Figure 3.3 Categorizing as a method of funnelling data
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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
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 56 and 57: CLASSIFICATION WHAT IS QUALITATIVE
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
Page 106 and 107: 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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