Qualitative_data_analysis

More documents

Recommendations

Info

Table 15.3 Distribution of responses by case PRODUCING AN ACCOUNT 267 How strong is that pattern? If we look at the frequency and distribution of these examples, we find a reasonable spread across the data, with no concentration in particular cases (Table 15.3). ‘Active’ and ‘passive’ responses are woven through the letters, with only the final letter making no reference to patient response at all. While this suggests a gender-stereotyping pattern is woven through the letters, if we look at the data in terms of patients rather than responses, we find the picture is not quite so clear. For example, three of the six examples of ‘passive’ responses amongst women relate to one patient, Claire Memling. We also find that in the case of two patients, Claire Memling and Mrs Schwimmer, initial passivity eventually turns into an ‘active’ response. That leaves only two examples, Mrs Zardis and Mrs Fetelman, where female patients (and not just their response) are unambiguously ‘passive’. Nevertheless, the balance of evidence does suggest a gender-stereotyping pattern of responses. The most clear-cut and dramatic examples in the data all tend to confirm this pattern, while as we have seen the negative evidence is less clear-cut and dramatic. Overall, we may reasonably conclude that Woody Allen has portrayed a gender-stereotyped view in his depiction of patient responses. Although this example refers to very little data, the principles involved can easily be applied to more extensive data, without unduly extending the length of the analysis. Obviously the more data is involved, however, the more we may have to select and summarize the data, while still providing sufficient information to validate our interpretations. Unfortunately, the technological limitations of traditional forms of publication impose constraints which mean we cannot reproduce the interactive environment of a computer-based account. Ideally, we should be able to link summaries to databits, and databits to context, so that at any time readers can check for themselves the validity of our interpretations by accessing the original data in context. If accounts were stored on disk, the reader could have
268 QUALITATIVE DATA ANALYSIS full access to all the data on which our analysis is based. By electronically linking our summaries and interpretations to the relevant data, our audience could then check for themselves any doubtful (or especially interesting) points in our analysis. So long as producing an account depends only on traditional forms of publication, we have to accept limitations which in principle may be overcome following the advent of desktop computing. However, this vision of a future in which the research community exchanges disks as well as papers, and accounts can be validated in a fully interactive medium, cannot be realized without the development and standardization of the relevant software. Meantime let us return to our present problems of validation, and consider the issues posed by ‘construct’ validity. These refer to the fit (or lack of fit) between the concepts used in our account and those already established in the relevant field. If we have used concepts which are congruent with those employed successfully in other analyses, our audience may have greater confidence in the validity of our account. If we have spurned the conceptual tools currently available in favour of inventing our own, we can expect a more sceptical response. Even the scientific community likes to keep originality on a lead—unless its problems have become so pressing that a complete shift in paradigm is required. Before we dedicate ourselves to revolutionizing current paradigms, however, we ought to recognize the circumstances in which such changes can occur. Einstein’s relativity theory explained empirical discrepancies which were inexplicable within the framework of Newtonian physics, and made some fresh predictions which could be tested against evidence. Theories in social science do not have such explanatory and predictive power. Often in place of explanation and prediction, we have to make do with insight and speculation. To our audience, these qualities, valuable though they may be, will rarely constitute an overwhelming case for changing the way they think. There is much to be said, therefore, for working with established rather than original concepts. The task of testing and honing these concepts through empirical enquiry is no less valuable than that of creating new conceptual tools. In practice, qualitative analysis may well involve a mix of these two tasks, depending on the fit between our data and the concepts we employ at the outset. To validate new concepts, we can still consider their congruence with established thinking. If our concepts are inconsistent with established thinking, we have to accept a sterner test of their validity, if not in terms of their explanatory and predictive power, then at least in terms of the significant insights and understanding they afford. Much the same point applies to ‘criterion’ validity. If our observations are inconsistent with the results produced through other measures, then we have to be particularly careful to ensure that our confidence in them is not misplaced. Qualitative analysis is often castigated as being too subjective, and as Patton comments:
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 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
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
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.
Page 144 and 145:
have annotated this data. Suppose w
Page 146 and 147:
ASSIGNING CATEGORIES 131 suddenly t
Page 148 and 149:
• What generally constitutes a
Page 150 and 151:
Databits Categories 5. Now she is u
Page 152 and 153:
Chapter 10 Splitting and splicing T
Page 154 and 155:
Using Hypertext linking, whenever n
Page 156 and 157:
SPLITTING AND SPLICING 141 databits
Page 158 and 159:
Category Subcategories Suffering Di
Page 160 and 161:
Figure 10.1 Levels of subclassifica
Page 162 and 163:
ILLUSTRATION 10.5 SUBDIVIDING DATAB
Page 164 and 165:
Figure 10.2 Initial relationships b
Page 166 and 167:
If we want to make this change, it
Page 168 and 169:
Figure 10.5 Reassessing relationshi
Page 170 and 171:
SPLITTING AND SPLICING 155 Although
Page 172 and 173:
Figure 10.8 Comparing subcategories
Page 174 and 175:
Figure 10.9 Shifting the analytic e
Page 176 and 177:
Chapter 11 Linking data Categorizin
Page 178 and 179:
Figure 11.2 Multiple hyperlinks bet
Page 180 and 181:
example, we may ask what it is abou
Page 182 and 183:
Figure 11.5 Linking and categorizin
Page 184 and 185:
Figure 11.7 An explanatory link bet
Page 186 and 187:
Table 11.2 Multiple links between d
Page 188 and 189:
our reasons for making decisions an
Page 190 and 191:
Figure 11.11 Conditional and causal
Page 192 and 193:
Chapter 12 Making connections Imagi
Page 194 and 195:
Figure 12.1 The difference between
Page 196 and 197:
Table 12.1 Concurrence between cate
Page 198 and 199:
Table 12.4 Boolean operators for ca
Page 200 and 201:
From Table 12.6 we can tell that mo
Page 202 and 203:
Notice how this cross-tabulation co
Page 204 and 205:
substantive connections between cat
Page 206 and 207:
Figure 12.5 Following a trail of di
Page 208 and 209:
Figure 12.7 Retrieving chronologica
Page 210 and 211:
This approach is very exploratory,
Page 212 and 213:
MAKING CONNECTIONS 197 Table 12.10
Page 214 and 215:
MAKING CONNECTIONS 199 a consequenc
Page 216 and 217:
Chapter 13 Of maps and matrices A n
Page 218 and 219:
Table 13.1 Comparing information ac
Page 220 and 221:
OF MAPS AND MATRICES 205 [In a calm
Page 222 and 223:
Table 13.5 Data indices by case and
Page 224 and 225:
Table 13.7 Recoding data to express
Page 226 and 227:
Table 13.11 Cross-tabulating ‘occ
Page 228 and 229:
Figure. 13.4 The history of the uni
Page 230 and 231:
Figure 13.7 Incorporating detail by
Page 232 and 233: Figure 13.10 Comparing differences
Page 234 and 235: Figure 13.13 Adjusting scope of mos
Page 236 and 237: Figure 13.17 Comparing strength of
Page 238 and 239: Figure 13.20 Identifying positive a
Page 240 and 241: OF MAPS AND MATRICES 225 undercurre
Page 242 and 243: Chapter 14 Corroborating evidence
Page 244 and 245: CORROBORATING EVIDENCE 229 There is
Page 246 and 247: CORROBORATING EVIDENCE 231 describe
Page 248 and 249: CORROBORATING EVIDENCE 233 of these
Page 250 and 251: Figure 14.1 Concurrence between cat
Page 252 and 253: CORROBORATING EVIDENCE 237 possibil
Page 254 and 255: CORROBORATING EVIDENCE 239 that the
Page 256 and 257: CORROBORATING EVIDENCE 241 seductiv
Page 258 and 259: CORROBORATING EVIDENCE 243 research
Page 260 and 261: Chapter 15 Producing an account ‘
Page 262 and 263: PRODUCING AN ACCOUNT 247 An analysi
Page 264 and 265: Figure 15.2 The whole is greater th
Page 266 and 267: Figure 15.3 Tree diagrams represent
Page 268 and 269: PRODUCING AN ACCOUNT 253 associatio
Page 270 and 271: worrying unduly about the connectio
Page 272 and 273: Figure 15.5 Different writing strat
Page 274 and 275: PRODUCING AN ACCOUNT 259 the vibrat
Page 276 and 277: Figure 15.7 Procedures for assignin
Page 278 and 279: PRODUCING AN ACCOUNT 263 consistent
Page 280 and 281: Table 15.1 Databits assigned to cat
Page 284 and 285: PRODUCING AN ACCOUNT 269 To be subj
Page 286 and 287: PRODUCING AN ACCOUNT 271 analysts t
Page 288 and 289: Figure 16.3 Analysis as an iterativ
Page 290 and 291: CONCLUSION 275 empirical analysis o
Page 292 and 293: Appendix 1 ‘If the Impressionists
Page 294 and 295: APPENDIX 1 279 though it had been o
Page 296 and 297: Appendix 2 Software The reader look
Page 298 and 299: Glossary Associating categories the
Page 300 and 301: References Allen, Woody (1978) ‘I
Page 302 and 303: REFERENCES 287 Sperber, Dan and Wil
Page 304 and 305: Capra, F. 29, 64, 99 case studies 1
Page 306 and 307: Galtung, J. 14 Geer, B. 7, 8, 29, 5
Page 308 and 309: connections; description qualitativ
show all

Qualitative_data_analysis

Create successful ePaper yourself

Delete template?

Save as template?