Qualitative_data_analysis

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classified. Take the example in Illustration 2.1 of structured and unstructured responses to a question about the use of closed questions in an interview. ILLUSTRATION 2.1 STRUCTURED AND UNSTRUCTURED RESPONSES TO THE QUESTION ‘WHAT ARE THE MAIN ADVANTAGES AND DISADVANTAGES OF CLOSED QUESTIONS IN AN INTERVIEW?’ Structured response Unstructured response • Closed questions expedite the interview for both interviewer and respondent • Closed questions expedite later processing of data • Closed questions improve reliability • Closed questions convey more exact meaning by defining the range of appropriate responses • Closed questions improve reliability WHAT IS QUALITATIVE DATA? 17 Well, it can put people off, not being able to answer in their own words. But the important thing is that people may not be able to answer as they’d like. Answers to open questions are more likely to reflect a person’s own thinking —to be more valid. It’s much better to analyse the data afterwards, even if it’s more time-consuming. Of course time is of the essence, especially when you’ve had the kind of medical problems I’ve had over the last year. I had that operation in January, etc. etc. The structured response has been classified, for the data is divided into separate statements denoting distinctive advantages of closed questions, relating to the conduct of the interview, the ease of data processing and the communication of meaning. By contrast, the unstructured response is descriptive but unclassified: the response covers a range of points—not all of them relevant—which are not organized and presented as distinctive elements. Lack of structure is evident in the characteristic volume and complexity of much research data: in those apparently endless pages upon pages of fieldnotes; in the varied mass of documentary materials; in those lengthy and lavish interview transcripts. Such data may often lack structure, but this can be a problem as much as a virtue. The idea that qualitative data is mainly ‘unstructured’ is useful, if this is taken not as a definition but rather as an imperative for analysis. Although unstructured data may not be classified, it can be classified and indeed one of the main aims of qualitative analysis is often to do just that. While a lot of qualitative data may be unstructured, it is misleading to define qualitative data as ‘unstructured’ data. Is a response less ‘qualitative’ because I classify my observations? Suppose I am asked to describe the colour of my hair. Is my response less
18 QUALITATIVE DATA ANALYSIS ‘qualitative’ if I (sadly but honestly) select ‘grey’ from a list of alternatives, than if I write ‘grey’ in the space provided? Ironically, in defining qualitative data in terms of unstructured data or a particular family of research methods, qualitative analysts underestimate the significance of qualitative data across the whole research spectrum. They also underestimate the concern amongst other research traditions with problems of meaning and conceptualization (Fielding and Fielding 1986, Bryman 1988). Rather than counter-posing qualitative and quantitative data in this way, it makes more sense to consider how these can complement each other in social research (Giarelli 1988). To do so, let us look in more detail at different levels of measurement in social research. Here I am taking measurement in its broadest sense, as the recognition of a limit or boundary. As Bohm (1983:118) has argued, this is also its most ancient sense, as in the idea of a ‘measured’ action or response which acknowledges the proper limits to behaviour. Measurement referred to insight into the proper nature of the phenomenon; if behaviour went beyond its proper measure or limit, the result would be ill-health—or tragedy. Such limits can be recognized through qualitative assessment as well as specified more precisely through quantitative measures. Indeed, the specification of precise proportion was initially a subsidiary element of measurement, of secondary significance, though it has since supplanted the more general notion of recognizing the proper limit or boundary of some phenomenon. When we look at different levels of measurement, we find that numbers and meanings are related at all levels. A concept is an idea which embraces a number of observations which have characteristics in common. When we bring observations together as having some significance in common, we count them as belonging to the concept. The word count derives from the Latin ‘computare’, with the roots ‘com’, meaning together, and ‘putare’ meaning to calculate or reckon. (The term computer derives from the Latin ‘computare’). Counting therefore has a double meaning. We use it to refer to significance, as in the expression ‘that observation doesn’t count’; and we use it to refer to enumeration, as in the expression ‘count the observations’. So conceptualization even at the most elementary level is informed by number. And even at the most elementary level of enumeration, counting depends on the meaning of what we ‘reckon together’. The first step in recognizing a limit or boundary is to give a description of something. When my daughter describes ‘what happened at school this afternoon’, she is telling a story about a unique sequence of events. Much of the qualitative data produced through fieldwork methods or open-ended interview questions may be of the same narrative form. We describe by focusing on the characteristics of something —perhaps a person, object, event or process. No explicit comparison need be
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 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 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
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FINDING A FOCUS 67 Sheila The solic
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FINDING A FOCUS 69 to reflect upon
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For example, here are some question
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Transposition: Doctor: ‘I’m afr
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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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