Qualitative_data_analysis

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Figure 3.6 Formal and substantive connections between building blocks WHAT IS QUALITATIVE ANALYSIS? 49 fun-lovers are looking for romance and the home-lovers are looking for partners. By looking for such patterns, we can find fresh perspectives on the data. Where we find regularities, we can also find variations and exceptions. Perhaps some of our sports-lovers just want company, but others are looking for something more serious. We can compare them, and perhaps identify some common factors which could ‘explain’ this variation. For example, perhaps all the sports-lovers are men, and that may explain why they are reluctant to suggest (or admit) they want a more serious relationship? By examining the association between different variables, we can begin to identify connections between them. Perhaps only one of our sports-lovers wants a partner. We can study this exception to the rule. We have already considered the search for singularities, in our hunt for that paragon of all virtues who fulfils our wildest dreams of a potential mate. In social research as in life, there are key moments, individuals, and episodes, whether because they epitomize a theme or stand out as exceptions to it. We may have noticed these at first glance, but once the data is classified, we can examine more precisely the connections between ‘key’ episodes and the patterns they exemplify or contradict. What exactly are the characteristics of our ideal mate? And how many of these characteristics does s/he share with other individuals in our sample? Just how typical or exceptional is our singularity? Gradually our data acquire a new complexion, as we build up a clearer picture of the main characteristics, good and bad, of the advertisers in our sample, and the associations between them. We can enhance our identification of patterns in the data by analysing the frequencies with which characteristics occur, and even by cross-tabulating different characteristics. Once data have been categorized they can be counted, and data which can be enumerated can be analysed statistically, if only at a simple level. This provides one means of identifying or confirming regularities and variations within the data. We can infer connections between concepts by examining how categories combine. Are fun-lovers also art-lovers? Do more men than women like sport? Can we differentiate between different types, in terms of clusters of characteristics? And do these different types attract similar types, or are they looking for opposites? Through this kind of analysis, we can ‘interrogate’ our data (Richards and Richards 1991) and explore the connections between our categories.
50 QUALITATIVE DATA ANALYSIS This approach to connecting concepts is powerful enough in its way, but it examines relationships as though they were external and contingent. We may or may not find that art-lovers are also fun-lovers. And if they are, we are no nearer to understanding why this connection apparently exists between them. I say ‘apparently’ because, of course, we have to allow for the possibility of spurious correlations and intervening variables. The association of one variable with another is not sufficient ground for inferring a causal or any other connection between them. In effect, we have to interpret numbers in terms of meanings. Only if we can identify an intelligible meaning does the numerical correlation between different variables acquire significance. In qualitative analysis, we can at least return to the data to see whether such a connection can reasonably be inferred. For example, suppose we observe that most male advertisers receive replies from women, while most women advertisers receive replies from men. In other words, there is a high correlation between the gender of the advertiser and the gender of the respondent. Now, following the approach outlined above, we could check whether there is any evidence in our data explaining why these events (the advert and the reply) should be correlated in this way. And of course, we are likely to find such evidence in the expressed sexual preferences of advertisers and respondents. But in any case, we would have no difficulty in accepting this correlation as evidence of a significant connection between the events of advertising and receiving a reply. The reason for our lack of difficulty or doubt is that we can readily supply a very plausible explanation of the nature of this connection. This explanation refers to the power of the sex drive and the force of attraction between the opposite sexes. We could throw in some cultural factors too, such as the repression of homosexuality or the powerful influence of prevailing social expectations encouraging heterosexual relationships. These factors can account for the pattern in our data, and give us confidence that we are inferring a genuine connection between events. What kind of explanations are these? If they also depend on recognizing some regular conjunction of events, then we face an infinite regress, where correlations between variables must be perpetually re-analysed in terms of correlations between other variables. For example, we could cite the regularity with which men and women live together as evidence of sexual attraction—but then this regularity in turn would require explanation. However, our explanation is couched in different terms. In citing the force of sexual attraction, we are identifying a force familiar to us all by virtue of our nature as human beings. The sexual drive is an integral part of the human make-up—even when it is repressed. As such, it is a power (or a susceptibility) which acts as a causal mechanism in producing particular and identifiable effects (such as pregnancy). From this perspective, cause is related to capabilities and liabilities; instead of being external and contingent, it is internal and
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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
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 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 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
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
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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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