Qualitative data analysis Learning how to analyse qualitative data by computer can be fun. That is one assumption underpinning this new introduction to qualitative analysis, which takes full account of how computing techniques have enhanced and transformed the field. The book provides a practical and unpretentious discussion of the main procedures for analysing qualitative data by computer, with most of its examples taken from humour or everyday life. It examines ways in which computers can contribute to greater rigour and creativity, as well as greater efficiency in analysis. The author discusses some of the pitfalls and paradoxes as well as the practicalities of computer-based qualitative analysis. The perspective of Qualitative Data Analysis is pragmatic rather than prescriptive, introducing different possibilities without advocating one particular approach. The result is a stimulating, accessible and largely discipline- neutral text, which should appeal to a wide audience, most especially to arts and social science students and first-time qualitative analysts. Ian Dey is a Senior Lecturer in the Department of Social Policy and Social Work at the University of Edinburgh, where he regularly teaches research methods to undergraduates. He has extensive experience of computer-based qualitative analysis and is a developer of Hypersoft, a software package for analysing qualitative data. Qualitative data analysis A user-friendly guide for social scientists Ian Dey LONDON AND NEW YORK First published 1993 by Routledge 11 New Fetter Lane, London EC4P 4EE Simultaneously published in the USA and Canada by Routledge 29 West 35th Street, New York, NY 10001 Routledge is an imprint of the Taylor & Francis Group This edition published in the Taylor & Francis e-Library, 2005. “To purchase your own copy of this or any of Taylor & Francis or Routledge’s collection of thousands of eBooks please go to www.eBookstore.tandf.co.uk.” © 1993 Ian Dey All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging in Publication Data A catalog record for this book is available from the Library of Congress ISBN 0-203-41249-4 Master e-book ISBN ISBN 0-203-72073-3 (Adobe eReader Format) ISBN 0-415-05851-1 (hbk) ISBN 0-415-05852-X (pbk) Contents List of figures, illustrations and tables vi Preface xi Acknowledgements xiv 1 Introduction 1 2 What is qualitative data? 10 3 What is qualitative analysis? 31 4 Introducing computers 57 5 Finding a focus 65 6 Managing data 77 7 Reading and annotating 87 8 Creating categories 100 9 Assigning categories 120 10 Splitting and splicing 137 11 Linking data 161 12 Making connections 177 13 Of maps and matrices 201 14 Corroborating evidence 227 15 Producing an account 245 16 Conclusion 272 Appendix 1: ‘If the Impressionists had been Dentists’ 277 Appendix 2: Software 281 v Glossary 283 References 285 Index 288 Figures, illustrations and tables FIGURES 1.1 The steps involved in data analysis—chapter by chapter 8 2.1 Describing a bit of data as a ripple in the flow of experience 19 2.2 Category relating two similar observations 20 2.3 Categorizing using inclusive categories 22 2.4 Nominal variable with mutually exclusive and exhaustive values 23 2.5 Ordinal variable indicating order between observations 24 2.6 Interval variable with fixed distance between values 25 2.7 Quantitative and qualitative data in dynamic balance 30 3.1 Qualitative analysis as a circular process 32 3.2 Three aspects of description in qualitative analysis 33 3.3 Categorizing as a method of funnelling data 44 3.4 Derivation of nominal variables with exclusive and exhaustive values 47 3.5 Formal connections between concepts 47 3.6 Formal and substantive connections between building blocks 49 3.7 Connections between chronological or narrative sequences 52 3.8 Causal connections between concepts 52 3.9 Qualitative analysis as a single sequential process 54 3.10 Qualitative analysis as an iterative spiral 55 4.1 A link between text held in separate locations 61 5.1 Deriving hypotheses about humour from the literature 72 5.2 Main themes for analysing humour 75 5.3 Integrating themes around issues of style and substance 75 6.1 Case documents kept in a hierarchical file system 83 6.2 Data stored in fields on a card-based filing system 84 7.1 Relating data to key themes 97 7.2 Mapping ideas to data within and across cases 98 7.3 Relating two ideas 98 8.1 Alternative category lists for analysing female stereotypes 108 8.2 Weighing up the degree of refinement in initial category set 113 8.3 Developing a more refined category list 114 vii 9.1 Categorizing data—1 120 9.2 Categorizing data—2 121 9.3 Categorizing data—3 121 10.1 Levels of subclassification of the subcategory ‘suffering’ 145 10.2 Initial relationships between categories 149 10.3 Incorporating categories, and distinguishing more and less important 150 lines of analysis 10.4 Reassessing relationships between categories—1 150 10.5 Reassessing relationships between categories—2 153 10.6 Reassessing position of categories in analysis 153 10.7 Revising analysis with minimum disturbance 156 10.8 Comparing subcategories of ‘substance’ 157 10.9 Shifting the analytic emphasis 159 11.1 Single hyperlink between two bits of data stored separately 162 11.2 Multiple hyperlinks between bits of data stored separately 163 11.3 Linking dentists and patients 164 11.4 Observing the link ‘debunked by’ between databits 166 11.5 Linking and categorizing complement each other 167 11.6 Linking two databits 167 11.7 An explanatory link between two databits 169 11.8 Linking and categorizing two databits 169 11.9 Inferring an explanatory link between two databits 170 11.10 Explaining Mrs Sol Schwimmer’s litigation 172 11.11 Conditional and causal links in the tale of Kaufman and Tonnato 175 11.12 Connecting incongruous and cathartic humour 176 11.13 Linking data and connecting categories 176 12.1 The difference between associating and linking events 179 12.2 Association and linking as mutually related means of establishing 180 connections 12.3 Following a trail of links through the data 190 12.4 Two trails of links through the data 190 12.5 Following a trail of different links through the data 191 12.6 A ‘chain’ of causal links in the data 192 12.7 Retrieving chronological links in the Claire Memling story 193 12.8 Vincent’s explanations linked to chronology of events in the Claire 194 Memling story 13.1 Textual and diagrammatic displays of information 202 13.2 Map of relationship between two concepts 212 13.3 Map of complex relationships between four variables 212 13.4 The history of the universe through time 213 viii 13.5 A small selection of symbols based on computer graphics 214 13.6 Differentiating concepts through different shapes and patterns 214 13.7 Incorporating detail by including subcategories 215 13.8 Adjusting for the empirical scope of categories 215 13.9 Mapping relationships for all cases 216 13.10 Comparing differences in scope through a bar chart 217 13.11 Using overlaps to indicate scale 217 13.12 Adjusting for scope in presenting classification scheme21 8 13.13 Adjusting scope of most refined categories 219 13.14 Distinguishing exclusive and inclusive relationships 219 13.15 Making relationships between categories more explicit 220 13.16 Representing strength of different causal relationships 220 13.17 Comparing strength of relationships between categories 221 13.18 Integrating connections between categories 222 13.19 Representing reciprocal connections between categories 222 13.20 Identifying positive and negative categories 223 13.21 Representing concurrence between categories 224 13.22 Using space to represent time 226 14.1 Concurrence between categories 235 14.2 Two routes through the data, arriving at different results 240 15.1 The whole is greater than the sum of the parts—1248 15.2 The whole is greater than the sum of the parts—2 249 15.3 Tree diagrams representing different analytic emphases 251 15.4 Tree diagrams indicating different analytic emphases 252 15.5 Different writing strategies—sequential and dialectical 257 15.6 Decision-making laid out in algorithmic form 260 15.7 Procedures for assigning categories in algorithmic form 261 15.8 The two aspects of generalization 270 16.1 Linear representation of analysis 272 16.2 Loop representation of analysis 272 16.3 Analysis as an iterative process 273 ILLUSTRATIONS 1.1 Different approaches to qualitative research 2 2.1 Structured and unstructured responses to the question ‘What are the 17 main advantages and disadvantages of closed questions in an interview?’ 2.2 Example of a grading and marking system 27 2.3 Grades with different mark bands 27 ix 3.1 Personal ads 42 5.1 ‘The library’ 66 5.2 Comments on feminist humour 70 6.1 ‘Two attendants at a Turkish Bath’ 78 6.2 Recording data fully but inefficiently 80 6.3 Filing reference information—questions and sources 81 6.4 Data filed efficiently 82 7.1 ‘In the Office’ 89 7.2 Using memos to open up lines of enquiry 94 7.3 Linking memos and data 95 8.1 Preliminary definitions of categories 109 8.2 Developing a more extensive category list 113 9.1 Two ways of identifying ‘bits’ of data 123 9.2 Overlapping