Qualitative_data_analysis

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ILLUSTRATION 2.2 EXAMPLE OF A GRADING AND MARKING SCHEME Grade Mark A 75–100 B 65–74 C 55–64 D 50–54 E 45–49 F 35–44 G 0–34 The different bands refer to qualitative differences in performance. For example, grade A means ‘excellent’. But what do the marks mean? Note that the grades refer to bands of marks of unequal size (Illustration 2.3). Although we have a numerical scale from one to one hundred, we do not have standard units. Compare two essays marked ten and thirty with another two essays marked fifty and seventy. The former are in the same grade—that is, have the same meaning, ‘bad fail’—the latter are separated by two grades, and therefore have different meanings. As marks in some grades are given more weight than marks in others, it would be misleading to average marks to determine overall performance. Yet once performance is measured in terms of marks, the temptation to do just that may be overwhelming, even if the interpretation of such an average must be obscure! ILLUSTRATION 2.3 GRADES WITH DIFFERENT MARK BANDS Grade Mark band G 35 A 25 B & C & F 10 D & E 5 WHAT IS QUALITATIVE DATA? 27 To interpret data in social research, it may be more important to use meaningful categories than to obtain precise measures. As a category ‘forty years old’ is not especially meaningful (except perhaps to forty-year-olds!) and age data organized
28 QUALITATIVE DATA ANALYSIS into different categories by ‘years’ may be very hard to interpret, Therefore we may prefer to reclassify ‘years’ data in terms of age groups e.g. ‘under twenty-fives’ etc. which are more meaningful, even though we cannot then measure just how old those in each age group are. Problems of interpretation are pervasive in any science, whether we are thinking of ‘strange attractors’ in physics, ‘black holes’ in astronomy or the ‘nuclear family’ in social science. Numbers are never enough: they have to refer to concepts established through qualitative analysis. While quantities are powerful precisely because of the complex mathematical operations they permit, they mean nothing in themselves unless they are based on meaningful conceptualizations. In other words, social science (and science for that matter) without qualitative data would not connect up with the world in which we live. If it is folly to disregard the problems of meaning in science, it is also folly to discount the contribution of numbers in analysing qualitative data. When A.E.Maxwell, the Senior Lecturer in Statistics at the Institute of Psychiatry in the University of London, wrote about ‘Analysing Qualitative Data’ three decades ago, he explained to his readers that his book could have been called ‘Chi-Square tests’ (Maxwell 1961). At that time, it was taken for granted that qualitative analysis meant the statistical analysis of variables which were not amenable to more quantitative measurement. Now, we take for granted precisely the opposite: that no book on qualitative data analysis will be concerned with the statistical analysis. This shift in paradigm has had some virtue, for it has placed more emphasis on the meaning and interpretation of data through the processes of description and classification. However, this emphasis also exaggerates distinctions between alternative methods which ought more properly to be viewed as partners than as competitors. It may indeed be difficult if not irrelevant to count examples, if our data is entirely descriptive and we are analysing singularities rather than looking for patterns within our data. However, as I suggested earlier, even ‘singularities’ are likely to be embedded in a language full of more or less implicit comparisons and classifications. My daughter cannot tell me what happened at school today without them. Classification is not just a product of structured interview schedules; it is the stuff of everyday thinking. One of the main aims of analysis may be to recognize and make explicit the classifications used by subjects. Another may be the development of the analyst’s own classification of the data. But once data has been classified, it can be counted. Enumeration is implicit in the idea of measurement as recognition of a limit or boundary. Once we recognize the boundaries to some phenomenon, we can recognize and therefore enumerate examples of that phenomenon. Once we know
Page 2 and 3: Qualitative data analysis Learning
Page 4 and 5: 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 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 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
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Chapter 6 Managing data Piles of pa
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MANAGING DATA 79 setting, by source
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MANAGING DATA 81 Interviewer Maybe
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Figure 6.1 Case documents kept in a
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MANAGING DATA 85 ‘Show the full r
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Chapter 7 Reading and annotating Ac
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• 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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