Friese S. Qualitative Data Analysis with ATLAS.ti 3ed 2019

QUALITATIVE DATA ANALYSIS WITH ATLAS.TI

Susanne Friese provides both practitioners and researchers with a handy guide full of important information on making most effective use of ATLAS.ti. It’s a ‘must have’ in the field of qualitative data analysis. Maik Arnold, Fachhochschule Dresden An up-to-date, must-read guide full of useful information for students and early-career researchers considering using ATLAS.ti for data analysis. Read this book and learn about computer assisted NCT from the best. Jennifer Coe, University of Suffolk This book is an excellent tool supporting novice and experienced researchers as they develop the capability to utilize ATLAS.ti in qualitative or mixed methods research. Georgios Fessakis, University of the Aegean This book provides an informative guide for students, researchers and educators to uncover and systematically analyse complex phenomena hidden in unstructured data. It provides analytical and visualization tools designed to open new interpretative views on qualitative data. Nashwa Ismail, Open University This book gives practical insight and encouragement for everyone interested in computerassisted qualitative data analysis with ATLAS.ti. It is also an essential read for those commencing exploring the world of qualitative research methods or seeking to make sense of rich data in a meaningful way. Tobias Mettler, University of Lausanne Just like Atlas, the mythic Greek Titan, this book can hold the weight of your qualitative data analysis using ATLAS.ti software. Enlightening, extensive and detailed, the book is a precious guide for both experts and novices in the field of software-supported analysis of qualitative data. Stavroula Prantsoudi, University of the Aegean This is a wonderful, easy-to-read and pragmatic introduction to a method of data analysis for people who are still on their journey to acquire a deeper understanding of software-assisted qualitative research or social research in general. With every sentence, you feel the author has developed the book out of a long-standing practice of teaching to people all over the world – with a sense of humour and user needs. Peter Stegmaier, University of Twente Friese clearly articulates her approach to using ATLAS.ti for analysing qualitative data thematically and developing from the descriptive through to the conceptual with invaluable tips on how to then write this up in a report. Steven Wright, Lancaster University

QUALITATIVE DATA ANALYSIS WITH ATLAS.TI SUSANNE FRIESE THIRD EDITION

SAGE Publications Ltd 1 Oliver’s Yard 55 City Road London EC1Y 1SP SAGE Publications Inc. 2455 Teller Road Thousand Oaks, California 91320 SAGE Publications India Pvt Ltd B 1/I 1 Mohan Cooperative Industrial Area Mathura Road New Delhi 110 044 SAGE Publications Asia-Pacific Pte Ltd 3 Church Street #10-04 Samsung Hub Singapore 049483 Editor: Alysha Owen Editorial assistant: Charlotte Bush Production editor: Ian Antcliff Marketing manager: Susheel Gokarakonda Cover design: Lisa Harper-Wells Typeset by: C&M Digitals (P) Ltd, Chennai, India Printed in the UK © Susanne Friese 2019 Third edition published 2019 Second edition published 2014 First edition published 2011. Reprinted 2012. Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act, 1988, this publication may be reproduced, stored or transmitted in any form, or by any means, only with the prior permission in writing of the publishers, or in the case of reprographic reproduction, in accordance with the terms of licences issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers. Library of Congress Control Number: 2018954978 British Library Cataloguing in Publication data A catalog record for this book is available from the British Library ISBN 978-1-5264-4623-7 ISBN 978-1-5264-5892-6 (pbk) At SAGE we take sustainability seriously. Most of our products are printed in the UK using responsibly sourced papers and boards. When we print overseas we ensure sustainable papers are used as measured by the PREPS grading system. We undertake an annual audit to monitor our sustainability.

Brief contents Online resources xiii Preface to the third edition xiv ATLAS.ti key terms xvi Introduction xxv 1 Overview of the process of computer-assisted analysis 1 2 Getting to know ATLAS.ti 9 3 Embarking on the journey – data and project management 25 4 Technical aspects of coding 69 5 Creating a coding scheme 103 6 Querying the data and further steps in the analysis process 154 7 Recognizing and visualizing relationships – working with networks 192 8 Compiling the final report – the last phase of the writing process 228 9 Teamwork 238 Epilogue 297 References 298 Index 303

Contents Online resources xiii Preface to the third edition xiv ATLAS.ti key terms xvi Introduction xxv For whom did I write the book? xxvii Chapter overview xxviii Sample projects xxx Further reading xxxii 1 Overview of the process of computer-assisted analysis 1 Phase 1: description of the data – creation of a code system 2 Phase 2: querying data – finding answers – identifying relationships 3 The analytic process 4 Does my methodological approach fit a computer-assisted analysis? 6 Further reading 7 2 Getting to know ATLAS.ti 9 Skills training 2.1: starting the program and importing the sample project 10 Skills training 2.2: getting to know the user interface 11 The ATLAS.ti ribbon 12 The navigation panel 14 Description of entities that you are going to work with in ATLAS.ti 15 Skills training 2.3: working with the entity managers 16 Skills training 2.4: working with docked and floated windows 17 Skills training 2.5: loading documents 19 Skills training 2.6: creating tab groups 20 Skills training 2.7: simple data retrieval 21 Skills training 2.8: looking at a network 21 Skills training 2.9: previewing the Query Tool 22 Review questions 23

CONTENTS vii 3 Embarking on the journey – data and project management 25 Data preparation 27 Text documents 28 PDFs 29 Audio and video files 29 Multimedia transcripts 30 Image files 30 Excel files (survey import) 30 File size and document length 31 Language support 31 Collecting data with the ATLAS.ti mobile app 31 Transcription guidelines 31 Guidelines for interview transcripts 31 Guidelines for focus group transcripts 32 About data file names 33 Project management in ATLAS.ti 34 Skills training 3.1: setting up a project 36 Creating a new project 36 Adding documents 37 Commenting your data and keeping track of analytic thoughts 37 Saving the project 38 Skills training 3.2: organizing project documents 38 Creating groups in a manager 39 Creating groups in the Group Manager 41 Deleting a group 42 Renaming a group 43 Skills training 3.3: managing your project 43 Removing documents from a project 43 Renumbering documents 43 Deleting a project 44 Skills training 3.4: exporting projects for project transfer or backup 44 Skills training 3.5: creating project snapshots 45 Skills training 3.6: changing the default location for ATLAS.ti project data 46 Skills training 3.7: preparing and importing survey data 48 Preparing survey data 49 Importing survey data 50 Inspecting the imported data 50 Working with survey data 51 Skills training 3.8: importing reference manager data for a literature review 52 Skills training 3.9: writing memos in the early stages of analysis 54

viii CONTENTS Creating a memo 56 When to use comments and memos in ATLAS.ti 57 Skills training 3.10 exploring your data – creating word clouds 59 Skills training 3.11: keyword in context search 65 Review questions 67 Further reading 67 4 Technical aspects of coding 69 Skills training 4.1: coding with a new code 71 Quotation reference 73 Code reference 74 Skills training 4.2: coding with two or more codes 75 Skills training 4.3: list coding 75 Skills training 4.4: coding via drag and drop 76 Skills training 4.5: replacing a code 77 Skills training 4.6: resizing the length of a quotation 77 Skills training 4.7: unlinking and removing codes 78 Skills training 4.8: writing quotation comments 79 Skills training 4.9: coding with in-vivo codes 80 Skills training 4.10: further coding-related options 81 Creating new codes 81 Renaming codes 82 Adding a color attribute to codes 82 Deleting codes (and other entities) 82 Writing code definitions 83 Merging codes 84 Skills training 4.11: importing a list of existing codes 85 Skills training 4.12: exporting the code list for reuse in another project 86 Skills training 4.13: focus group coding 86 Coding focus group data 88 Handling other media types 90 Skills training 4.14: coding a PDF document 90 Skills training 4.15: working with audio and video files 90 Adding audio/video files to a project 91 Display of video documents 91 Zooming the timeline 92 Creating an audio or video quotation 93 Display of video quotations 94 First steps in analyzing video data 95 Making use of quotation names and comments 95 Adding codes 96 Skills training 4.16: coding an image 97

CONTENTS ix Skills training 4.17: working with geo data 98 Adding a geo document 98 Creating a geo quotation 99 Coding geo quotations 100 Review questions 101 Further reading 101 5 Creating a coding scheme 103 Let’s do a puzzle so you remember how good you are at categorizing 105 Skills training 5.1: noticing and collecting – coding data for content 108 Discussion of the coding exercise 110 How to add more structure to your exploration 111 More on code word labels, quotations and numbers 116 The ‘right’ length for quotations 116 What to do with repeated occurrences 116 The ‘right’ number of codes 117 More on categories and subcategories 118 Do I need to code everything? 120 Building an efficient code system 120 Skills training 5.2: retrieving all quotations of a code 122 Skills training 5.3: developing subcategories 124 Skills training 5.4: building categories from descriptive labels 129 Skills training 5.5: defining categories on the ‘right’ level 135 Skills training 5.6: comparing thematic to interpretive coding 139 Advantages of a well-sorted and structured code list 143 Using syntax to distinguish between distinct levels and types of codes 145 Moving on 147 Skills training 5.7: writing research-question memos 147 Recommendations for organizing research-question memos 151 Review questions 151 Further reading 152 6 Querying the data and further steps in the analysis process 154 Skills training 6.1: getting to know the operators 155 Boolean operators 155 Proximity operators 157 Semantic operators 161 Exploring the data terrain further – the journey continues 163 Skills training 6.2: creating and working with smart codes 163 Creating smart codes 164 Editing smart-code queries 165

x CONTENTS Skills training 6.3: getting to know the Code Co-occurrence Table 166 For anyone interested in the mathematics behind the c-coefficient 168 Skills training 6.4: getting to know the Code-Document Table 169 Skills training 6.5: creating queries in the Query Tool 173 Starting simple: building a query using set operators 173 Creating a report 174 Building a query using proximity operators 176 Building more complex queries 176 Skills training 6.6: learning about code queries in combination with document attributes 178 Skills training 6.7: creating smart groups 181 Skills training 6.8: working with global filters 182 Global filters in the Code Co-occurrence Table 184 Global filters in the Code-Document Table 186 Reflections on the use of code groups 187 Reflections on the use of numbers and how perfect does the code system need to be? 188 Review questions 189 Solutions 190 Skills training 6.1: understanding Boolean operators 190 Skills training 6.5: step-by-step instruction to answer RQ6 190 Further reading 191 7 Recognizing and visualizing relationships – working with networks 192 Skills training 7.1: learning terminology 194 Skills training 7.2: using networks for conceptual-level analysis 196 Exploring code co-occurrences in networks 196 Case-based analysis in networks 200 Using networks to develop the storyline for your research report 202 Using networks to discuss findings with your adviser or colleague(s) 203 Using networks to present findings 203 Illustrating results from the Schwarzenegger project 204 Illustrating results from a media analysis of the financial crisis 204 Using networks in publications 205 Skills training 7.3: creating and working with networks 206 Learning how to link 207 Relation properties 207 Exploring the links 209 Linking multiple nodes simultaneously 209 Adding nodes 210 Removing nodes 211

CONTENTS xi Moving nodes 211 Accessing data behind nodes 211 Layout options 212 Routing 214 Network view options 214 Skills training 7.4: working with the Relations Editor 215 Opening the Relations Editor 215 Modifying an existing relation 216 Creating a new relation 216 On the use of networks for structural purposes 216 Dealing with case-based networks 219 Hyperlinks in ATLAS.ti 220 Examples of using hyperlinks 220 Skills training 7.5: working with hyperlinks 221 Linking quotations 222 Linking across tab groups 223 Browsing hyperlinks 224 Visualizing hyperlinks 225 Overview of all code–code links and hyperlinks 226 Review questions 226 Further reading 227 8 Compiling the final report – the last phase of the writing process 228 Contents for the method chapter 229 Contents for the result chapter 231 Skills training 8.1: exporting memos for reports 232 Skills training 8.2: how to quote data segments in reports 233 Skills training 8.3: exporting networks for reports 234 Contents for the appendix 234 Skills training 8.4: creating a code book 235 Skills training 8.5: exporting the document with codes in the margin 236 Review questions 237 Further reading 237 9 Teamwork 238 Decide who is going to be the project manager 239 Checking user accounts 239 Common tasks of project managers 241 Skills training 9.1: merging projects 242 Housekeeping 246

xii CONTENTS Common tasks of team members 247 Importing project bundle files 247 Overview of team project tasks 249 Scenario 1 – analyzing a common set of documents 250 Scenario 2 – analyzing different sets of documents 252 Scenario 3 – joint development of a coding frame 253 Scenario 4 – team projects for the classroom 257 Overview of the tasks for classroom projects 257 Scenario 4a – the teacher-guided classroom project 260 Scenario 4b – the teacher-guided project with more student autonomy 263 Scenario 4c – for a two-semester qualitative method course 264 Scenario 5: inter-coder agreement 264 To the critics 265 Why reliability matters 266 Reliability and validity 267 Requirements for coding 268 Development of semantic domains 268 Multi-valued coding 270 Common mistakes 271 Measuring inter-coder agreement 272 Krippendorff’s family of alpha coefficients – from the general to the specific 275 Other methods for analyzing inter-coder agreement 276 Decision rules 278 Skills training 9.2: analyzing inter-coder agreement 280 Project set-up 280 Performing inter-coder agreement analysis 283 Interpreting results 287 Relevance 288 Agreement on the presence or absence of semantic domains 289 Domain identification 290 Agreement in coding within a semantic domain 291 Violation of mutually exclusive coding 292 Qualitative comparison of the codings of different coders 294 Epilogue 297 References 298 Index 303

Qualitative Data Analysis – online resources Qualitative Data Analysis with ATLAS.ti, third edition, is supported by a wealth of online resources for students and researchers, which are available at https://study.sagepub.com/friese3e FOR STUDENTS Step-by-step Mac instructions empower Mac users to get to grips with ATLAS.ti and learn the necessary skills for successful data analysis. Links to video tutorials offer easy-tofollow guidance to help you master the software at your own pace. ATLAS.ti support links provide trusted advice straight from the experts about doing research with the software. Student workbooks help you revise and reflect on your learning with a series of carefully curated further-reading resources, review questions and more. Sample projects give you hands-on experience of working with data in ATLAS. ti and allow you to practice the full range of software techniques like coding, linking and building networks. Glossary flashcards help you feel confident defining specialist software terms and understanding key concepts in qualitative analysis. FOR INSTRUCTORS PowerPoint slides with key topics, themes and visuals from the book are available for you to customize and use in your own teaching.

Preface to the third edition Computers, like every technology, are a vehicle for the transformation of tradition. Winograd and Flores (1987) In the epilogue of the second edition of my book I wrote the following: CAQDAS is transforming our ways of collecting, handling and analyzing qualitative data. Trying to apply traditional manual techniques within a software environment is similar to mounting a dialing plate on a smartphone. I am advocating that qualitative data analysis traditions need to be transformed – not because technology forces us, but because it enhances the research process and allows us to gain insights that otherwise could not have been achieved. Another transformation you are likely to see has to do with the types of data that will be collected, for example induced by mobile devices and apps like ATLAS.ti mobile. Computers will become more and more a hybrid of traditional desktop and Web-based applications with constant access to the Internet and the World Wide Web that you can use for communication and data collection. It is already common practice to send text, image, audio and video messages. If CAQDAS goes mobile, the collection of multimedia data for qualitative analysis can be expected to follow suit. Future versions of the software are likely to become available as both desktop and Web-based applications. The latter will facilitate real-time cooperative work across the globe. Since June 2018 ATLAS.ti Cloud has been available. When this book is published, realtime collaboration could already have been implemented. As a Web-based application, the ATLAS.ti Cloud opens up new possibilities for data analysis. The cloud version is not covered in this issue of the book because (1) it is still a beta version and its functionality is limited, and (2) I have to work with a new version of the software before I can write about how best to deal with it analytically. It takes some time to find a new workflow when working with new software. That’s also why I did not write the third issue of this book right after version 8 was released. In 2010, I wrote the first edition of this book. That is now eight years ago, and I have since continued to work on my methodological interest in how to implement qualitative data analysis computer-aided. For example, I have written several articles and book chapters on how to implement different methodological approaches with ATLAS.ti. So far, these have been ‘thematic analysis’ and ‘grounded theory’. At the most basic level, they can be thought of as a translation process. On one side is the methodology and on the other the software and its functionality. Filling the void is not always easy, especially if you have to learn both. This book contains new exercises that will help you to see which features you can use for which methodological tasks. I start this edition with a general description of the process of computer-aided analysis. If ATLAS.ti is completely new to you, you will learn about the user interface and general operation in the second chapter. The third chapter has been expanded to include not only data and project management topics but also explanations of how to begin the data

PREFACE TO THE THIRD EDITION xv analysis process, including the initial data exploration and writing of memos. The topics in Chapters 4 and 5 have been expanded – they concern the technical and methodological aspects of coding. Chapter 4 introduces a new section on working with focus group data, and Chapter 5 introduces a new exercise on interpretive analysis and coding. Chapter 6 deals exclusively with the various analysis tools. Unlike in the previous edition, writing memos for the advanced analysis process is discussed in Chapter 5. Several reviewers expressed the wish that I write a chapter on the preparation of a research report: in this issue, you will find a chapter entitled ‘Compiling the final report – the last phase of the writing process’. This chapter is not about writing per se, because writing is part of the analysis and is ongoing throughout the analysis process. It’s about which parts of your ATLAS.ti project can be used in a thesis, research report or paper. The last chapter is about teamwork, including the analysis of inter-coder agreements. It was put at the end of the book because it is not relevant to all readers. It does, however, refer to much of what you have learned before, so it only works in conjunction with all the other chapters. If you are a Mac user, you will find all instructions for ATLAS.ti 8 Mac on the companion website. As ATLAS.ti offers a native Mac version so that Mac users feel more ‘at home’, the Mac interface has menus and not ribbons. In addition you will find some native features, like the inspector on the right-hand side of the screen, that are not available on Windows computers. Throughout the book, we will be working with a sample project on children and happiness, which I have successfully used in my workshops over the last few years. It’s about whether children make you happy or not. Since almost every person at some point in their lives thinks about whether he/she wants to have children, this is an issue that works well for training purposes worldwide and across cultures. It is also very entertaining to code the data. In addition to this data set, you will find five other sample projects on the companion website that cover various topics and data types. All example projects are described in the introductory chapter. In terms of usability for teaching, I have labeled all practical hands-on sessions as ‘skills training’, and at the beginning of each chapter you will find the learning objectives and a list all the skills training sessions. In preparing the third edition, I am thankful for the continuous support of the team at SAGE publications. Special thanks go to my editor, Alysha Owen, who prepared the way for writing this edition, asked reviewers to provide feedback and continuously encouraged and supported me throughout the writing process. Further, I am grateful for the work done by Charlotte Bush as Editorial Assistant, the Production team under Ian Antcliff, be it copy-editing or laying out the final book, and for the work done by Susheel Gokarakonda in marketing the book. Finally, a big ‘thank you’ to the five anonymous reviewers who provided valuable feedback at the various stages of writing the book.

ATLAS.ti key terms ATLAS.ti: ATLAS.ti stands for ‘Archiv für Technik, Lebenswelt und Alltagssprache’ [Archive for Technology, the Life World and Everyday Language]. The extension ‘ti’ stands for ‘text interpretation’. The ATLAS Project (1989–92) at the Technical University of Berlin was the ‘birthplace’ of an early prototype of the software. Code: Keywords linked to quotations. Code book: A table that contains the code label, the code definition and the code group(s). The code book may be sorted in alphabetical order, by categories and their subcategories, or by code groups. I recommend the second option. The code book can be exported as a report by ATLAS.ti from the Code Manager. Code comment: While coding, you can write notes in the code comment field to explain what you mean by this code and how you want to use it. In the course of the analysis, these comments should be extended to comprehensive code definitions. Code Co-occurrence tools: The Code Co-occurrence Explorer allows you to explore which codes you have applied in an overlapping manner. The Code Co-occurrence Table can be used for a cross-tabulation of selected codes. Thus, you should already have an idea which codes you want to relate to each other. The results can be exported as an Excel table. Code definition: A code definition describes the meaning of a code and how it has been or should be applied to the data. It can contain a coding rule and an example of a typical data segment coded with this code. Writing code definitions helps to improve the methodological rigor of a study. It forces the researcher to think about the meaning of a code in comparison to other codes. While going through the list of codes and writing code definitions, you may notice that the codes have different labels but no distinct meaning. Those codes can then be merged under one common label – or you change the definition in such a way that the codes become distinct. The aim should be that all codes can be applied unambiguously. Code-Document Table: The Code-Document Table shows the frequency of codings for codes or code groups across documents or document groups. It is useful for case or group comparisons. The results can be exported as an Excel table.

ATLAS.TI KEY TERMS xvii Code reference: This consists of the so-called groundedness and density of a code. The groundedness provides the frequency of how often a code has been applied; the density shows the number of links to other codes. Code system: A well-developed coding system describes the data material in all its facets. It shows the main aspects in the data in the form of categories and the variations within a category in the form of subcategories. The coding system can reflect different types of aspects depending on the research questions and the aim of the study. These can be the pure content of the data, the layout, the language used, aspects of time, different speakers or actors, evaluations, level of importance, degree of expression, etc. As a rough guide, computer-assisted coding systems contain on average about 100–250 codes and 12–25 main categories. Codes (abstract): Abstract codes are codes that are not linked to any quotations or codes (groundedness and density are zero). All smart codes that are not linked to other codes are also abstract codes, as they are not directly linked to quotations. Codes – free: A free code has not been used for coding. The groundedness is zero. Codes – in-vivo: For the computer this means that the highlighted characters are used as the code name. In a computer-assisted analysis, in most cases, it does not make a lot of sense to have code words that are the same as the text they code. Generally, a bit of context is needed and this requires extending the quotation beyond the characters used as in-vivo code. Codes – smart: Smart codes represent a saved query. They can be created in the Quotation Manager and the Code Manager – and within the Query Tool after having created a query. They are listed in the Code Manager and display a gray dot at the bottom left of the code icon. They cannot be used for coding. Each time you activate a smart code, the query that it consists of will be run. Thus, a smart code is always up-to-date and changes when the codes that it is based on change. Coding – auto: The Auto Coding Tool scans the text and automatically assigns a preselected code to matching text passages. As search terms you can enter a simple string of characters, but pattern matching using regular expressions is also possible. Auto coding does not replace regular manual coding. It can be used to code structural, repetitive information or in an exploratory manner. In most cases, you need to check the results after auto coding, adjust or remove quotations. Coding – focus group: Focus group coding is a special type of auto coding. It finds speaker IDs based on two default patterns (‘speakername:’ or ‘@speakername:’) or a user-defined pattern. You can confirm the speakers that were found and add further codes to each speaker. The automated coding process codes the section from the beginning of a speaker ID until

xviii ATLAS.TI KEY TERMS the beginning of the next identified speaker ID. Therefore, it does not matter whether a speaker unit is one or more paragraphs long or whether there are empty lines within or after speaker units. Codings: A coding is the link between a code and a quotation. It basically describes the act of coding. Comment: You can write a comment for each entity that you work with in ATLAS.ti (your project, documents, document groups, quotations, codes, code groups, memos, memo groups, networks, network groups, hyperlinks, code–code links, relations). Therefore, it is important that you have a plan of the kind of information you want to write and where. You will find recommendations throughout the book on how to work with comments. Connection (code-document): Code-document connections are virtual links as in reality no direct link exists. Codes are only indirectly linked to documents via the quotations. They can be displayed as blue lines in networks. Documents: The sources you are analyzing. ATLAS.ti supports text, PDF, image, audio, video and geo documents. Special import options are offered for Evernote, reference managers, Twitter and survey data. Document comment: You can write a comment for each document. The recommendation is to write meta information about a document into the comment field. For interview transcripts these might be the interview protocols or interview postscripts. Information like age, gender, etc. is managed in document groups, not the comment field. For other document types, you can use the comment field to specify the source of the document, the context of obtaining the information, a description of who published it, the target audience and so on. Entity managers: The main entity types in ATLAS.ti are documents, quotations, codes, memos, networks, links, relations and groups. An easy way to access most entity types is via the Project Explorer. For each entity type you can open a manager, e.g. by double-clicking on the branch in the Project Explorer. Filter (global): Global filters affect the entire project. You can set them in the Project Explorer or the managers. If a global filter is activated, you see a colored bar on top of the affected lists. The color indicates which entity is filtered according to the entity color (blue = documents; orange = quotations; green = codes; magenta = memos; purple = networks). Filter (local): You can set local filters in managers, e.g. by clicking on an item in the side panel. Local filters only influence the manager where you set the filter. If a local filter is activated, you see a light-yellow-colored bar on top of the entity list.

ATLAS.TI KEY TERMS xix Groups: The following entities can be grouped: documents, codes, memos and networks. The main purpose of groups is to filter the data. Groups – code groups: Code groups as filters help you to navigate through your list of codes. Therefore, it is useful to create a code group for each category. It is, however, recommended that categories and their subcategories are represented on the code level. Code groups are an additional layer on top but not in a hierarchical sense. A code can be a member of multiple code groups. At later stages in the analysis, you may create further code groups if you need them as filters in an analysis. Groups – document groups: Document groups in ATLAS.ti can be thought of as variables. You can, for instance, group all female and male respondents, all teachers, all postmen, all engineers, all moms, all dads, all singles and all married, unmarried or divorced respondents. You can group all documents by a certain month, year, author or source; all documents from company X into a group called Company X; all documents from companies in industry sector Y to a group called Sector Y; and so on. Groups can be created at any time during the analytic process, then modified, renamed or deleted. Their purpose is to serve as a filter. Thus, you can restrict a search to a group of documents. This applies to text searches as well as code retrievals (see Filter (global); Query Tool). Groups – smart groups: When you need a combination of two or more groups (e.g. to prepare a special filter for a query in the Query Tool, the Code-Document Table or the Code Co-occurrence Table), you can create smart groups. This option is available in the managers and the group managers. Hyperlinks: These are links between quotations. Quotations can be linked via named relations and, thus, are first-class relations. Inter-coder agreement analysis: Inter-coder agreement refers to the extent to which two or more independent coders agree when coding the same content by applying the same coding scheme. Inter-coder agreement – analyst: The analyst is the person who develops the code system and defines each code. She or he prepares the projects for the different coders who will code the data as instructed. Inter-coder agreement – coders: Two or more coders code the data in the project they receive from the analyst. Their task is to code the data based on the information given in the code definitions. If they are not clear about how to apply a code, they write questions and comments in a memo. They do not discuss issues with fellow coders or the analyst as this would bias the results. Inter-coder agreement – multi-valued coding: Multi-valued coding means that coders have applied codes from different semantic domains to the same or overlapping quotation(s).

xx ATLAS.TI KEY TERMS Inter-coder agreement – mutually exclusive coding: Mutually exclusive coding requires that no two codes of the same semantic domain are applied to the same or to overlapping quotation(s). Krippendorff’s alpha coefficients require mutually exclusive coding. The coefficient can only be estimated if mutually exclusive coding is not adhered to. The user will be notified if this is the case. Inter-coder agreement – semantic domain: A semantic domain is defined as a space of distinct concepts that share common meanings. Examples of semantic domains are emotional states, a set of strategies mentioned to deal with something, motivations for achieving something, a range of consequences based on an action, or group membership. Each semantic domain embraces mutually exclusive concepts indicated by a code. Library: The ATLAS.ti library is the location where ATLAS.ti stores all of the project data. The default folder is a hidden folder under ‘AppData\Roaming’ on the C drive. If users are not allowed to work on the C drive or do not want to store their data there for other reasons, they can create libraries at different locations. Libraries currently cannot be shared. Each user works within his or own library, also when working in a team. Link: A link is the line that you draw between two entities in a network. Margin area: The margin area is the space on the right-hand side of a loaded document. By default it displays all codes, memos and hyperlinks. Further options are to display groups, networks and users. The margin area is interactive: you can click on any entity for further actions. Memos: From a purely functional perspective, memos in ATLAS.ti consist of a title, a type and some text. They can be free or linked to other memos, codes and quotations. Throughout the book, I suggest various ways of using memos in ATLAS.ti. Memos are places to write down all sorts of ideas and thoughts. You can use them to remind you of things like what to do next week, what you wanted to ask your supervisor about, what you wanted to discuss with your team members, etc. And you can use memos as a place to write up your analysis and as building blocks for a later research report. Regard memos in ATLAS.ti as containers for ideas. Do not create a memo for every single idea. If the idea cannot be developed or expanded over time, then consider whether your thoughts might better be entered as a comment for a quotation. Memo comment: You can use memo comments for notes to yourself, e.g. where do you want to use this memo in your report? In team projects, team members can annotate each other’s analytical memos – or a supervisor can leave comments for students. Memo type: You can add an additional attribute to memos: the memo type. Default types (based on historic reasons) are memo, comment and theory. The usefulness of those default types is debatable. Using memos as comments is not a good idea, as memos should be clearly distinct from comments. Starting with version 8, memos also have their own comments.

ATLAS.TI KEY TERMS xxi Thus, I suggest you ignore the type ‘comment’ and add your own types instead. Memo types help you to sort and organize your memos. Merging: Merging means combining the contents of various projects into one Master file. The projects can contain either the same documents, different documents or both. In the first case, the documents are merged; different documents are added. The same applies to codes. The project can contain the same codes, different codes or a mixture. All identical codes will be merged; all codes that are different will be added. Identical entities are recognized by their unique ID, not by their name. Thus, everything that should be merged at some point must be created in one project by the project administrator. If an entity with the same name is created in two different projects, it has a different ID and, thus, these two entities cannot be merged. They will be added when merging projects. Networks: Networks offer a place for visualizing relations within your project. You can link almost all entities to each other and visualize your coding, relationships between codes, between quotations, between memos, between memos and quotations, between memos and codes and between a group and its members. The indirect connections that exist between document and codes and document groups and codes can also be displayed. Networks can contain thumbnail images of documents, image and video quotations. Networks can be used for analytic purposes and for presenting results. Remember that a relation between two entities in a network always applies to the entire project. Therefore, it is not meaningful to link, for instance, codes to each other when focusing on just one interview. The relations that are true for interview X may not be true for interview Y. When working with networks, a holistic perspective should be taken. In N–C–T (Noticing–Collecting–Thinking) analysis, networks play an important role in the second analysis phase as the researcher formulates queries and records results in research-question memos, identifying relationships in the data. Nodes: All objects inserted into a network become nodes. They are visualized as code nodes, memo nodes, quotation nodes, etc., but the generic term applies to all of them. Therefore, you need to be careful when deleting a node: you will not just remove the object from the network but also from the entire project. Print with Margin: This is an export option that creates a PDF from your coded documents as you see them on your screen with the codes, memos, hyperlinks, etc. in the margin area on the right-hand side. Project: Different from previous ATLAS.ti versions, you only need to deal with the project file and project bundle files. The project files you see when you open ATLAS.ti and the bundle files are the files that you need to create if you want an external backup copy of your project or if you want to open your project on a different computer.

xxii ATLAS.TI KEY TERMS Project bundle: A project bundle file is the exported version of your project that you need as external backup of your project and when transferring projects between computers. Project Explorer: The Project Explorer is displayed in the navigator on the left-hand side of the screen and shows all entities of a project in a tree structure. You can also access all entities from there. With a double-click on a main branch, you can open the manager of this entity. With a double-click on a quotation, you can open the quotation in context, etc. The context menus offer a variety of options like starting auto- of focus group coding, creating word clouds and word lists or activating global filters. Drag-and-drop operations into other windows are also possible. Project transfer: When you want to transfer a project between computers, you need to export it and create a project bundle file. The bundle file needs to be imported on the other computer. Query Tool: The Query Tool allows you to retrieve quotations based on a combination of codes. It offers four Boolean operators, three semantic operators and seven proximity operators. Further, it allows you to combine code queries with variables, i.e. you can ask for information such as: ‘Find me all quotations that I have coded with code A and code B, but only for women between the age of 31 and 40’. Quotations: Marked data segments that have a clearly defined start and endpoint. Often quotations are coded, but they don’t have to be. Quotations can also be ‘free’. This means they are not linked to any other entity. Free or coded quotations can be used as a source or target to link data segments to each other. Linked quotations are called hyperlinks. A quotation has an ID and a name. The ID consists of the document number that it belongs to and a number that indicates the sequence of when it was created. The position where a quotation can be found within a document can be read by its start and end points. The quotation name is based on either the first 70 characters of a text quotation or for multimedia quotations the name of the document. This automatically generated name can be changed. Quotation comment: Quotation comments are very useful when using an interpretive approach to analysis or when working with multimedia data. Quotation comments can hold interpretations of a data segment or describe what is going on in that part of the multimedia file. Quotation links: see Hyperlinks. Quotation reference: A quotation has an ID and a name. The ID consists of the document number that it belongs to and a number that indicates the sequence of when it was created. The position where a quotation can be found within a document can be read by its start and end points. When citing a quotation in a report, you can use the document number or the full ID and the start and end position, as in (D3, 13–17) or (D3:3, 13–17).

ATLAS.TI KEY TERMS xxiii Relations: These are the names that you can give to a link. This is possible for code–code links and for quotation–quotation links. Relations Editor: This is the tool where you define new relations or modify existing ones. Serendipity: Webster’s Dictionary defines serendipity as ‘a seeming gift for making fortunate discoveries accidentally’. In relation to ATLAS.ti, serendipity can be equated with an intuitive approach to data. A typical operation that relies on the serendipity effect is browsing. This information-seeking method is a genuinely human activity: When you spend a day in the library (or on the World Wide Web), you often start with searching for a specific book (or keywords). After a while, you find yourself increasingly engaged in browsing through books that were not exactly what you originally had in mind. Examples of tools and procedures ATLAS.ti offers for exploiting the concept of serendipity are the word clouds and lists, browsing quotations you have coded, comparing coded data using the Code-Document Table or exploring relations using the Code Co-occurrence tools. SPSS Export/Export for further statistical analysis: All codings can be exported as an SPSS syntax file. When running the syntax in SPSS, you get a data matrix where each quotation is a case, and each code and code group is a variable. Additional variables are documents, document groups, data type and start and end positions of quotations. Thus, you have all information that you need if you want to aggregate the data in SPSS. In addition, there is a generic export for statistical programs in the form of an Excel file that can be used in R, SAS, STATA and SPSS. Survey import: The survey import option allows you to import data via an Excel spreadsheet. Its main purpose is to support the analysis of open-ended questions. In addition to the answers to open-ended questions, data attributes can also be imported. These will be turned into document groups in ATLAS.ti. The survey import option can also be used for other case-based data that can easily be prepared as an Excel table. Tools – cleaning up: Redundant codings analyzer, checking for mutually exclusive coding (the latter was not yet implemented at the time of writing) Tools – exploratory: Word clouds, word count, project search, keywords in context view, auto coding. Tools – further analysis: The further analysis tools offered by ATLAS.ti are the Code Co-occurrence Explorer and Table, the Code-Document Table and the Query Tool. The latter can be combined with global filters or scope setting. Creating smart codes, smart groups and snapshot codes and groups are functions that are also more advanced analytics tools. Tools – team: Creating user accounts, logging in, ‘created by’ and ‘modified by’ information for every entity, project merge, inter-coder agreement analysis, redundant codings analyzer, view coder in the margin area.

xxiv ATLAS.TI KEY TERMS User account: ATLAS.ti automatically creates a user name based on the name you use on your computer. You can modify this name or create a new user account under Tools & Support (or the Tools menu on the Mac). All newly created entities in ATLAS.ti are stamped with the user name. This is useful and essential when working in teams. User names are stored with the project file. Thus, when you export the project and work on a different computer, your user name is also available on the other computer. Variables: See Groups – document groups. Visualization: Visualization is one of the key components of ATLAS.ti. The object-oriented design of ATLAS.ti seeks to keep the necessary operations close to the data to which they are applied. The visual approach of the interface keeps you focused on the data. Tools are available to visualize complex properties and relations between the accumulated entities during the process of eliciting meaning and structure from the analyzed data. Word cloud: Word clouds are an electronic image that shows words used in selected documents, codes or quotations in the form of a cloud. The words in the cloud are of different sizes according to how often they are used in the text. You can apply stop and go lists either to exclude words that you do not want to count, like ’the’, or to include only those words that you want to count. Other options are to view a selected word in the context of the data or to start the auto coding process. Word list: Word lists show all words that occur in selected documents, codes or quotations in a table. In addition to the total count, the table includes word length and relative frequencies per item if you have selected multiple items.

Introduction Software like ATLAS.ti allows us to analyze qualitative data in a systematic and transparent way without precluding openness – to query data in ways that are not imaginable with traditional methods. Based on a well-coded project, you can ask questions and find answers that remained hidden in the data before. You don’t have to rely on intuitive hunches. You can follow up on ideas without spending three days just finding out they were a dead end. A further advantage is that it is possible for a third person to double-check your findings. All of this improves the quality of qualitative research, thereby enhancing scientific and human knowledge. ATLAS.ti belongs to the genre of CAQDAS programs. CAQDAS stands for Computer Assisted Qualitative Data AnalysiS (Fielding and Lee, 1998). Another acronym that can be found in the literature is QDA software, which stands for Qualitative Data Analysis software and may be responsible for some of the misunderstandings and misperceptions related to CAQDAS.1 ATLAS.ti does not actually analyze data; it is a tool for supporting the process of qualitative data analysis. Computers are generally very good at finding things like strings of characters or coded data segments in a large variety of combinations, but the researcher first needs to tell the computer, by way of coding, which data segment has what kind of meaning (see also Konopásek, 2007). Welsh (2002) describes two camps of researchers: those who see software as central to their way of analyzing data and those who feel that it is peripheral and fear that using it leads to a ‘wrong’ way of analyzing data. In part, this reflects expectations about the potentials of computers expressed in the 1960s: ‘Experienced classic grounded theorists continue to await a “package” that can replicate the complex capabilities of the human brain for conceptualization of latent patterns of social behavior’ (Holton, 2007: 287). Even in the era of ‘smart’ devices, no one would expect computer software to perform such wonders. This was already expressed by Strauss when, in 1996, he wrote that the developer of ATLAS.ti, Thomas Muhr, made no claim to have produced a program that performs miracles. Rather, he stated that it is still the researcher who must ‘have the ideas and the gifts to do exceptional research’ (Muhr, 1997: 4). Computers can be of great help though. There are first implementations in various text analysis packages to automate the coding process based on algorithms. The result is a list of topics based on words that appear in the text, and you can choose to code them. Some programs also suggest context-specific topics and allow the user to control and refine the results. 1The acronym CAQDAS was developed by the directors of the CAQDAS networking project at the University of Surrey, Guildford, UK (www.surrey.ac.uk/computer-assisted-qualitative-data-analysis?).

xxvi INTRODUCTION In the process, the algorithms ‘learn’ and can, thus, improve the results. Web-based solutions offer more potential because they can access larger databases on the Internet. However, this requires that you upload your data to a cloud. Reading between the lines and linking content – for example, based on a statement made at the beginning of an interview and later during the interview – continue to be analytical tasks reserved for the human researcher. What can be said is that software frees us from all those tasks that a machine can do much more effectively, like coding data, modifying code words and coded segments, retrieving data based on various criteria, searching for words, integrating material in one place, attaching notes and finding them again, counting the number of coded incidences, offering overviews at various stages of a project, comparing data based on specific criteria and so on. By using ATLAS.ti, it becomes much easier to analyze data systematically and to ask questions that you otherwise would not ask because the manual tasks involved would be too time-consuming. Even large volumes of data and those of different media types can be structured and integrated very quickly with the aid of software (Saillard, 2011). In addition, a carefully conducted, computer-assisted qualitative data analysis also increases the validity of research results, especially at the conceptual stage of an analysis. When using manual methods, it is easy to ‘forget’ the raw data behind the concepts as it is quite laborious to get back into the data. In a software-supported analysis, the raw data are only a few clicks away, and it is much easier to remind yourself about the data and to verify or falsify your developing theoretical thoughts. Three or six months into the analysis your ideas about the data are likely to be different from those in the very early stages, and modification of codes and concepts is an innate part of qualitative data analysis. With the aid of computers, this process can easily be documented. The steps of analysis can be traced, and the entire process is open to view. For a more extensive discussion on the advantages as well as the disadvantages of using computers for qualitative data analysis, see Rodik and Primorac (2015). Looking back at my own experience, when I started to use software to analyze qualitative data in 1992, I did what most novices do: I looked at the features, played around a bit, muddled my way through the software and the data, gained some insights and wrote a report. It worked – somehow. But it was not very orderly or systematic. Since then, my way of working with CAQDAS and ATLAS.ti has gradually become much more systematic and – to a certain point – even standardized. This did not happen overnight: it took many years of working with the software and teaching it, as well as involvement in many project consultations. As each qualitative research project is to an extent unique, this allowed me to test my ideas in different contexts and to develop a set of steps and procedures that guides users through the process of computer-assisted analysis. Several books and articles have been written on the typical use and usefulness of software for qualitative data analysis, the early ones often expressing concern as well as enthusiasm about how software can help (Alexa and Zuell, 2000; Barry, 1998; Hinchliffe et al., 1997; Morrison and Moir, 1998; Richards and Richards, 1994; Seidel, 1991). Occasionally, a chapter on computer-assisted analysis is included (e.g. Silverman, 2000; Mayring, 2015; Guest et al., 2012), or there are short descriptions and screenshots showing how certain analysis steps could be implemented (Corbin and Strauss, 2008). Also, in a book first

INTRODUCTION xxvii published in 2010 you can still read descriptions of pile sorting and other manual methods of data analysis (Bernard and Ryan, 2010). The authors point out that the various procedures they describe can also be accomplished using a software package, but they do not explain how. The assumption appears to be that it goes without saying: you simply load the software and it is immediately obvious how to adapt manual procedures. I argue that this is not the case. Today, with the new possibilities available, you can approach data analysis differently. Software changes the way you build up coding systems, for instance. The process becomes much more exploratory due to the ease of renaming and modifying codes. Computers also change the ways you ask questions about the data. Data analysis procedures have become much more sophisticated because, for a computer, it is much easier to find things in the data and to output results. Also, CAQDAS makes it easier to combine qualitative and quantitative methods, which, of course, does not preclude a purely qualitative approach. It allows qualitative researchers to make the entire analytic process more transparent and to leave the black box. And it allows them to work in teams and across geographical boundaries. This creates new opportunities as well as challenges. CAQDAS is transforming our ways of collecting, handling and analyzing qualitative data. I am advocating that qualitative data analysis traditions need to be transformed – not because technology forces us, but because it enhances the research process and allows us to gain insights that otherwise could not have been achieved. This book guides you through the process of a computer-assisted analysis with the software ATLAS.ti. This requires, on the one hand, teaching the technical aspects of handling the software and, on the other, methodological and procedural aspects like how to best set up a project for later data interrogation, how to build a coding system to get the best use of the analytical tools at your disposal, how to proceed with the analysis and your analytical writing and how to write a report. Albeit not possible to cover specific methodological approaches in detail, you will find guidance on how to approach analysis in an inductive, deductive or abductive manner. You will learn how to perform a content analysis and how to use the software if you use a hermeneutic and more interpretive analysis approach. You can work purely qualitatively or, if applicable, quantify some of the findings. Whether you come from an ethnographic, a phenomenological or a grounded theory tradition, whether you have conducted action research, narrative interviews, focus groups or biographical research, whether you have structured or unstructured data, observational data or audio-visual material, I am interested in teaching you how to approach the analysis of your data in a systematic computer-assisted way. FOR WHOM DID I WRITE THE BOOK? I wrote this book for ATLAS.ti novices, as well as for more experienced users of ATLAS.ti who would like some guidance on the process of analysis in combination with the technical aspects of the software. I have answered a lot of questions that are frequently asked at the ATLAS.ti help desk or during my courses – questions that cannot be answered by a technical software manual. They relate to issues like how to best set up a project, how to organize

xxviii INTRODUCTION teamwork, how to build a coding system, how long coded segments should be, whether to apply multiple layers of codes, what for and when, what to do with the data once coded, how to visualize data, how to write up analysis and what goes into a report. I also wrote the book for teachers of qualitative data analysis courses. My personal conviction is that, in the twenty-first century, qualitative data should be analyzed with the support of software. It is time that what has long been standard practice in quantitative statistical research is applied to qualitative data analysis as well. As methodological training is quite diverse, the book can be used for undergraduate as well as postgraduate courses. It is suitable for undergraduates where method training comprises a large part of their study program. My aim for those students is to teach them descriptive-level analysis (phase 1) and, thus, they could work productively up to Chapter 5 in this book. At this stage, students have learned how to set up and manage projects, when to write comments and memos and what for (see Chapter 3), how to code data (see Chapter 4) and how to build up a coding system (see Chapter 5). While working through the chapters and coding their own data material, they are likely to have learned a lot about their data and gained some insights. They will have mapped out their data landscape (see Figure 5.12). They have not yet learned the various, more complex retrieval options, but queries can also be based on simple retrievals (i.e. querying single codes). From my experience, students by this time are motivated enough to look at the network function on their own and begin to visualize their findings. For advanced, Master’s and PhD courses, you can take the students one step further and teach them the more advanced analytical functions (phase 2). This means teaching them about the various analysis tools described in Chapter 6, talking more extensively about writing memos, inserting a lecture or two on issues of reliability and validity in qualitative research and showing the students how this is supported by the software. All this can be rounded off by teaching and more intensively working with the network function, including the possibilities of working inter-textually using hyperlinks (see Chapter 7). From the book’s companion website, lecturers can download presentations and sample data for use in tutorials, etc. The samples contain raw as well as coded data and projects which follow up the skills training sessions in the book. CHAPTER OVERVIEW In Chapter 1, I provide an overview of the process of computer-assisted qualitative data analysis and invite you to start thinking about methodology and computer-assisted analysis. In Chapter 2, I take you on a tour through the ATLAS.ti interface and the terminology used by the software. There is unfortunately no common language among the different CAQDAS packages. What is called a variable in one software package is an attribute in another. Codes might be referred to as keywords or as nodes. In ATLAS.ti, however, a node is a nodal point in a network, and so on. Hence, it is first necessary to learn the language of the software. With time, this will enable you to sound like a pro, to become a member of the ATLAS.ti community. If you already have some experience of working with ATLAS.ti 8, you will probably be

INTRODUCTION xxix familiar with the terminology and can skip Chapter 2 on getting to know the interface. If you have been using a previous version of ATLAS.ti, I recommend that you work through the skills trainings in Chapter 2 as version 8 is very different from previous versions. In Chapter 3, you are ready to rock – and to begin an ATLAS.ti project. It is a very important chapter: data management issues are often dismissed as boring and, thus, get neglected, and this frequently leads to difficulties, time-wasting and sometimes data loss further down the line. You learn about the data file formats that ATLAS.ti supports, when and for what purpose to choose which file format and how to prepare data transcripts so that you can best utilize software features later. Different from earlier editions of the book, this chapter also contains information on how to begin to explore data using word clouds, word lists and keywords in context searchers. You also learn how to integrate memos from the beginning of your project work. In addition, this chapter also holds instructions on survey import and the import of reference manager data. In Chapter 4, I explain the technical aspects of coding: creating new codes, applying codes, modifying and replacing codes, renaming and deleting codes, modifying coded segments, merging codes, writing code definitions, importing and exporting lists of codes, coding focus group data and working with different media types like audio, video, image and geo data. This is followed by a chapter on the methodological aspects of coding. Coding on its simplest level refers to the process of assigning a label to a data segment that the analyst deems to be relevant for some reason. Whether the code is merely a description, a paraphrase of the text or a concept or category on an abstract level makes no difference to the software. Software offers the option to code; what users do with this option is up to them. In Chapter 5, you will learn everything that you need to know about building a well-structured coding system. This is the prerequisite for the continuing conceptual-level analysis, the topic of Chapters 6 and 7. Chapter 6 discusses the various advanced analysis tools, the Code Co-occurrence Table, the Code-Document Table and the Query Tool. You will also learn how to work with smart codes and global filters. There are exercises for the different tools and options and you will be guided step by step. Chapter 7 is about visualizing ideas and findings in the form of networks and hyperlinks. These are tools that enable you to create links within and across data. The purpose of networks is to help you visualize relationships that you see in the data but also to discover them. As in previous chapters, there is a mix of technical explanations and methodological considerations. In Chapter 8, I provide some ideas on how to prepare and write your research report. As recently examined by Paulus et al. (2014), often little is written in published papers about how ATLAS.ti or other software packages have been used throughout the analysis process. In this chapter, I provide some ideas on how the work already done in the software can help you to prepare and write your report. Chapter 9 is about teamwork and the application and implementation of inter-coder agreement analysis. Therefore, it may also be of interest to those users who work alone but who want or need to calculate inter-coder agreement to validate their coding system. I describe some typical teamwork scenarios and things that you need to keep in mind when

xxx INTRODUCTION creating a project and building the coding system for inter-coder agreement analysis. In addition, this chapter contains ideas on how to work with students in the classroom. SAMPLE PROJECTS On the companion website you will find several sample projects that you can use in class or if you teach a workshop. If you work through this book on your own, it is best to work with the Children & Happiness project that I use as an example throughout the book, plus your own data material. In the following, each project is briefly described to give you a general idea about the content and data file formats included. Children & Happiness (sample project) When looking for example data I came across an article on children and happiness written by Nattavudh Powdthavee (2009) in the journal The Psychologist. Nattavudh reports on several academic studies that repeatedly found a negative correlation between having children and levels of happiness, life satisfaction, marital satisfaction and mental well-being. Since most people, regardless of their cultural background, religion or geographic location, will at some point ask themselves whether or not they want to have children, this topic promised to be of interest to many ATLAS.ti users. In addition to the journal article, the following documents are included: a post from a parenting blog about this article, the comments from its readers, comments from readers of a New York Times Magazine article on the same topic and a document with some findings from happiness research. Furthermore, the sample contains some fictional survey data imported via an Excel spreadsheet. The survey data contain answers from 24 respondents to two open-ended questions: the reasons for having and for not having children and some socio-demographic characteristics of the respondents. This sample project is suitable for demonstrating and practicing the full range of functions including coding, analysis and linking and building networks. Evaluation of Minecraft (survey) This is a second example of a survey import. It is based on real data found on the Internet, which has been prepared so that the survey import feature can be used. The data consists of reviews of the game Minecraft. Three groups of people – educators, parents and children/ adolescents – playing the game made a summary assessment followed by a lengthier explanation. Some of them also rated the game in terms of pedagogical value and gave an age from which the game is suitable for children to play. This project can be used to practice survey import, perform exploratory analysis with word clouds and word lists, test automatic coding, practice regular coding and try out the various advanced analysis tools. Available are the raw data and a coded version of the project.

INTRODUCTION xxxi Ratings and comments on the TV series The Big Bang Theory (survey) This is an entertaining data set that you can work with if your students like The Big Bang Theory TV series. The data include viewer ratings and comments from 2013 and 2016. You can easily extend the project by, for example, adding videos of some episodes and their transcripts. The transcripts of most episodes can be found online. Friendship (thematic analysis, inter-coder agreement) The data for this project is taken from the TQRMUL dataset teaching resources. It consists of four interviews with undergraduate students on the subject of friendship. The interviews were conducted in Spring 2008 at Liverpool John Moores University by Tanya Corker and Alasdair Gordon-Finlayson, specifically for the purpose of being made available online as teaching resource. You will find the full user guide on the companion website (Forrester, 2010). The raw data and coded versions of two coders are available for practicing inter-coder agreement analysis. If you are interested in including the audio or video data from the interviews, they are available via the TQRMUL teaching resources. War experience (grounded theory) This data set consists of three interviews with war veterans on their experience of war. It is from the book Basics of Qualitative Data Research (Corbin and Strauss, 2008). Corbin uses the data to demonstrate the various steps of a grounded theory analysis without using a CAQDAS package. In my example project, I show how it can be done in a computer-assisted way. Available are the different phases of the project: data without coding; projects that show the different phases of coding in building a category system; and a project that shows how findings can be integrated – for instance, by using the network function. The projects show how open and axial coding can be implemented and how comments, hyperlinks and memos can be used in the analysis process. Not all memos are filled with content, so there is room for your own further analysis. There are several publications available that describe how I conducted the analysis in ATLAS.ti (e.g. Friese, 2016a/b; Friese, 2019). Iceland (video) This project contains some video data that I recorded myself during a trip to Iceland. The data can be read like a travel diary. There is no guiding research question unless you are interested in finding out how I experienced the trip and which places to visit if you tour Iceland yourself one day. The aim of the project is to illustrate how you can use ATLAS.ti to

xxxii INTRODUCTION work with video and geo data. Skills trainings 4.15–4.17 in Chapter 4 uses the data of this project. Focus group (sample files) The files provided consist of three different versions of an excerpt of a focus group transcript to practice the automated coding function for focus group data. In the different versions, the speaker IDs have been transcribed in different ways, so the patterns by which ATLAS.ti recognizes speaker units can be practiced. FURTHER READING Dey, Ian (1993). Qualitative Data Analysis: A User-friendly Guide for Social Scientists. London: Routledge. Fielding, Nigel G. and Lee, Raymond M. (1998). Computer Analysis and Qualitative Research. London: Sage. Friese, Susanne (2005). Software and fieldwork, in Wright, Richard and Hobbs, Dick (eds) Handbook of Fieldwork, part nine: Fieldwork, science and technology. London: Sage. Friese, Susanne (2016a). Qualitative data analysis software: The state of the art. Special Issue: Qualitative Research in the Digital Humanities, Bosch, Reinoud (ed.) KWALON, 21(1), 34–45. Friese, Susanne (2016b). CAQDAS and grounded theory analysis. MMG Working Paper 16–07, October 2016. Friese, Susanne (2019). Grounded theory analysis and CAQDAS: A happy pairing or remodeling GT to QDA?, in Bryant, Tony and Charmaz, Kathy (eds) The SAGE Handbook of Grounded Theory, chapter 11. London: Sage. Hesse-Biber, Sharlene (2003). Unleashing Frankenstein’s monster? The use of computers in qualitative research, in Hesse-Biber, S.N. and Leavy, P. (eds) Approaches to Qualitative Research: A Reader on Theory and Practice, chapter 25. Oxford: Oxford University Press. Kelle, Udo (ed.) (1995). Computer-aided Qualitative Data Analysis. London: Sage. Legewie, Heiner (2014). ATLAS.ti – how it all began (a grandfather’s perspective), in Friese, Susanne and Ringmayr, Thomas (eds) ATLAS.ti User Conference 2013: Fostering Dialog on Qualitative Methods. Berlin: University Press, Technical University Berlin, http://nbnresolving.de/urn:nbn:de:kobv:83-opus4-44140. Mangabeira, Wilma C., Lee, Raymond M. and Fielding, Nigel G. (2004). Computers and qualitative research: Adoption, use and representation. Social Science Computer Review, 22(2), 167–78. Morrison, Moya and Moir, Jim (1998). The role of computer software in the analysis of qualitative data: Efficient clerk, research assistant or Trojan horse? Journal of Advanced Nursing, 28(1), 106–16. Muhr, Thomas and Friese, Susanne (2001). Computerunterstütze qualitative Datenanalyse, in Hug, Theo (ed.) Wie kommt die Wissenschaft zu ihrem Wissen? Band 2: Einführung in die Forschungsmethodik und Forschungspraxis, 380–99. Hohengehren: Schneider Verlag.

INTRODUCTION xxxiii Richards, Lyn (2009). Handling Qualitative Data: A Practical Guide, 2nd edn. London: Sage. Richards, Lyn and Morse, Janice M. (2013). Readme First for a User’s Guide to Qualitative Methods, 3rd edn. Thousand Oaks, CA: Sage. Silver, Christina and Lewins, Ann (2010). Computer assisted qualitative data analysis, in McGaw, Barry, Peterson, Penelope and Baker, Eva (eds) The International Encyclopedia of Education, 3rd edn, Volume 6, 326–34. Oxford: Elsevier

Overview of the process of computer-assisted analysis 1 Computer-assisted qualitative data analysis consists of various consecutive phases, which are on the most general level: preparing data and creating a project file, coding the data, using the software to sort and structure the data and querying the data with the aim of discovering patterns and relations. The emphasis on coding will be different depending on the chosen methodological approach, and I will write more about it when explaining the various entities and tools that are at your disposal in ATLAS.ti in later chapters. The logic of the software, though, is built around coding. None of the analysis tools for querying the data can be used without the user having coded the data. In coding the data, you describe what is in the data. These might be people, artifacts, organizations, emotions, attitudes, actions, strategies, consequences of actions, contextual factors and the like. Depending on the chosen methodological approach, this may mean that you are tagging the data at a nominal level or that you are developing code labels based on a more detailed interpretation of data segments (see Chapter 5). Once the data are coded and a code system is developed, it can be interrogated (see Chapters 6 and 7). Both phases are described in more detail below.

2 QUALITATIVE DATA ANALYSIS WITH ATLAS.TI PHASE 1 DESCRIPTION OF THE DATA — CREATION OF A CODE SYSTEM The aim of descriptive-level analysis is to explore the data, to read or to look through them and to notice interesting things that you begin to collect during first-stage coding. This requires reading transcripts, field notes, documents, reports, newspaper articles, etc., viewing video material or images or listening to audio files. Generating word clouds and word lists may also be a starting point when you have lots of data. To capture the interesting things that you notice, you may write down notes, mark the segments you find interesting, write comments or, as is most common, attach labels (= coding). These labels are referred to as ‘codes’ in the software, for historic reasons. I will write more about this in Chapters 4 and 5. You may also think of them as ‘tags’. At this point in the analysis process, the labels can be descriptive or already conceptual, lower- or higher-order. Developing codes and a code system is a process, and the labels you create at this stage of the analysis process are likely to change. Thus, you do not have to worry too much whether a label is right or wrong. Reading further, you will very likely notice a few things that are like others you have noticed before. If they fit under a label that you already have, you apply it again. If an issue is similar but does not quite fit a tag that you already have, renaming it may allow you to subsume the data segments. The labels do not have to be perfect yet. You can continue to collect more similar data segments and later, when you review them, it will be easier to think of better and more fitting labels to cover the substance of the material you have collected. The intellectual work that you do at this stage is the same as described in the past for manual ways of analysis. As Strauss and Corbin wrote in 1998: As the researcher moves along with analysis, each incident in the data is compared with other incidents for similarities and differences. Incidents found to be conceptually similar are grouped together under a higher-level descriptive concept. (73) The initial process of collecting interesting things (i.e. coding) can be manifold depending on the underlying research questions, research aim and overall methodology you are using. To name just a few of the various procedures that you will find in the literature: • descriptive or topic coding (Miles et al., 2014; Richards and Morse, 2013; Saldaña, 2009; Wolcott, 1994) • process coding (Bogdan and Biklen, 2007; Charmaz, 2002; Corbin and Strauss, 2008) • initial or open coding (Charmaz, 2006; Corbin and Strauss, 2008; Glaser, 1978) • emotion coding (Goleman, 1995; Prus, 1996) • values coding (Gable and Wolf, 1993; LeCompte and Preissle, 1993) • narrative coding (Cortazzi, 1993; Riessman, 2008) • provisional coding (Dey, 1993; Miles and Hubermann, 1994). Researchers may choose to follow just one of the suggested procedures or combine them. The things you collect in your data may include themes, emotions and values at the same time.

OVERVIEW OF THE PROCESS OF COMPUTER-ASSISTED ANALYSIS 3 You can code the data using deductively derived codes as in provisional coding; or you can develop codes inductively (e.g. initial or open coding) or abductively, which is often the case when developing categories. Some researchers develop about 40 codes, others a few hundred or even a few thousand. In this book, I give some answers for what to do with your list codes. Often there is a lack of methodological understanding of what a code is. The software does not explain it; it just offers functions to create new codes, to delete, to rename or to merge them. The metaphor of collecting helps to understand better that a properly developed code is more than just a descriptive label for a data segment and that it does not make sense to attach a new label to everything one notices. You learn more about this in Chapter 5. The aim of the first phase of coding is to develop a code list that describes the issues/ aspects/phenomena/themes that are in the data, naming them and trying to make sense of them in terms of similarities and differences. This results in a structured code list which you can apply to the rest of the data during second-stage coding. Very likely the code list will need to be refined further and there will be a few more cycles of noticing and collecting until all the data are coded and the coding schema is fully developed. In parallel you can comment on data segments and begin to write memos. Figure 1.1 The process of computer-assisted qualitative data analysis PHASE 2 QUERYING DATA — FINDING ANSWERS — IDENTIFYING RELATIONSHIPS At some point, all data are coded, and you can enter the next phase of analysis. So far, you have been working at the data level. The aim now is to look at the data from a different angle: the perspective of the research questions. Starting from one of your questions, you begin to query the data based on your coding. ATLAS.ti offers a variety of analysis tools such as the Code-Document Table, code co-occurrence analyses, the Query Tool and the networks.

4 QUALITATIVE DATA ANALYSIS WITH ATLAS.TI The results of queries can be displayed in the form of numbers, the coded quotations or as a visualization. However, the actual analysis takes place during the writing process by summarizing and interpreting the results. For this the ATLAS.ti memo function can be used. How to use memos is described throughout the book in various chapters. While writing comments as well as memos, you move the analysis further step by step, dig deeper, look at details and begin to understand how it all fits together. When beginning to see how it all fits together, visualization tools like the network function in ATLAS.ti are used. Working with networks stimulates a different kind of thinking and allows further explorations. Networks can also be used as a means of talking with others about a finding or about an idea to be developed. Before you reach the last step of the analysis, several networks will probably have been drawn, redrawn, deleted and created anew. The aim is to integrate all the findings and to gain a coherent understanding of the phenomenon studied; or, if theory building was your aim, to visualize and to present a theoretical model. THE ANALYTIC PROCESS Analysis might proceed in a sequential manner, where you move directly from noticing to coding to discovering interesting insights as shown in Figure 1.2. However, unless you have a very structured and simple project like the analysis of an open-ended survey question, then this will seldom be possible. Figure 1.2 The process from project set-up to discovery

OVERVIEW OF THE PROCESS OF COMPUTER-ASSISTED ANALYSIS 5 More likely is a recursive process where you move back and forth between noticing and collecting – for instance, when developing subcategories. You may also want to go back to noticing and recoding after already having discovered some relations and created networks. The visualization gives you a different angle on the data: it may be used to talk about your ideas with others, and then you may notice you have overlooked something and need to go back, rethink and recode. You may also decide that you need to collect some more data. Figure 1.3 Analysis in most cases will be iterative and recursive Real-life qualitative data analysis often looks as shown in Figure 1.3 – a bit messy but fascinating and exciting. Did you note the new element in Figure 1.3, the report? Usually there are deadlines for submitting a research report or your thesis or a paper. Memos are the building blocks for your report. If you follow the suggestions in Chapter 5, the transition from ATLAS.ti to writing your thesis or the result chapters of your thesis will run smoothly. A third variation is to go about analysis in a holistic manner. If you get to a point where you cannot see the wood for the trees, or if you feel you simply cannot sit in front of a computer screen any longer, then it is time to look at the whole again. Take a printout of one or

6 QUALITATIVE DATA ANALYSIS WITH ATLAS.TI more transcripts. In summer, look for a delightful place outside; in winter, find a cozy spot in front of the fire. Have something to eat and drink and then spend some time reading your data from beginning to end in a sequential order, reminding yourself of the interview situation or other contexts. Do the same with videos or other types of data you may have. There is no need to sit in front of the computer all the time. Reading the data via a different medium, such as on good old-fashioned paper, is likely to give further insights. It may help you to see how it all fits together and how the results that you have so far can be integrated. Freeman (2017) invites readers of her book Modes of Thinking for Qualitative Data Analysis to think outside the box of theoretical perspectives and methodologies and suggests five modes of approaching data analysis: categorical thinking, narrative thinking, dialectical thinking, poetical thinking and diagrammatical thinking. ATLAS.ti can support you in all five modes. The simplest to see are the implementation of the categorical mode (encoding data) and the implementation of the diagram mode (creating networks). The other approaches have more to do with how you think about the data. This may affect the types of code you use, but it is mainly about writing comments and memos (see, e.g., Skills training 5.7). Additionally, for narrative thinking, the Code-Document Table can be used to compare different narratives (see Skills Training 6.4). Hyperlinks can be useful when using dialectical thinking mode (see Skills Training 7.5). Seeing the implementation certainly requires some experience in dealing with ATLAS.ti, but I definitely recommend the book. When you’re ready, it can give you inspirations on how to think about your data. DOES MY METHODOLOGICAL APPROACH FIT A COMPUTER-ASSISTED ANALYSIS? You first need to decide what your objective is and what you want to achieve considering the research question that you want to find an answer for. Which methodological approach is most suitable? Are you interested in analyzing discourse, in gaining insights through a person’s life story and narrative or in developing theory, or can the research question(s) best be answered by conducting a survey including several open-ended questions, organizing a series of focus group discussions or going into the field and writing observational notes? If you are not skilled in an approach that you think is suitable, you have two choices: to learn it or to modify your research question so you can find answers using an approach you are familiar with. The next question you need to ask is whether the methodological approach you are considering lends itself to coding. As the software cannot decide for you the meaningful units and concepts in your data, you need to ‘tell’ the computer what is relevant and meaningful given your research question. This is done by creating quotations and coding the data. After deciding on a methodological approach, you may decide to use the ATLAS.ti iPad or Android for data collection (not covered in this book). You may decide to use ATLAS.ti to help with your literature review. Your literature review will in most cases be a separate project with its own coding system. Once data has been collected, the analytical journey within ATLAS.ti begins. This can be as early as the availability of your first interview transcript – for example, if you have chosen a grounded theory approach; or as late as after

OVERVIEW OF THE PROCESS OF COMPUTER-ASSISTED ANALYSIS 7 all data becomes available – for example, if you have chosen a mixed-method approach, using ATLAS.ti to aid with the analysis of open-ended questions from a survey. How to approach the analysis is dependent on your methodology. This is your starting point. Based on this, you need to ask yourself which function or tool in the software you need to use to advance your analysis. Thereby, you can proceed step by step, as I also do in the book. You do not need to know the sixth step before the third. It all begins with data preparation. Transcripts for discourse analysis might need to be prepared in a different way from transcripts for thematic analysis. Once the data are ready for import into the software, the question arises how to organize the data in the software. This requires that you know the tools that you can use, but at the same time you need to know by which criteria you want to organize your data. The latter relates to your research questions. You see the dance that occurs between methodology, software functions and tools, and the questions that you hope to find an answer for. It is important that you always start with your research goals and do not let a software tool drive the analysis. Ask yourself step by step along the way what you want to achieve; think about which functions and tools in the software can help you to achieve it – this is the process of translating your methodology into executable steps in ATLAS.ti (see also Woolf, 2014; Woolf and Silver, 2017). Trying to cover the different approaches in this book would be a task doomed to failure right from the start. You will, however, find an introductory chapter on qualitative data analysis on my blog (https:// www.quarc.de/blog). I have written some papers and book chapters that illustrate the translation of grounded theory (Friese, 2016b, 2018, 2019), and thematic analysis using ATLAS.ti (Friese et al., 2018). If you are not an academic researcher and are not concerned with epistemologies and methodologies of scientific research, or you just have some qualitative data at hand that you want to analyze but you are more familiar with quantitative methods, or you are a beginning researcher interested in learning more about qualitative methods and you are not yet familiar with a particular methodological approach, or you simply need to finish a class assignment, you can opt for the N–C–T method1 presented in this book as it describes a generic approach to computer-assisted qualitative content analysis. N–C–T stands for Noticing–Collecting–Thinking. This process will always be at the core of any computerassisted analysis, variated by and adapted to a given methodological approach. FURTHER READING Bazeley, Pat (2013). Qualitative Data Analysis: Practical Strategies. London: Sage. Bong, Sharon A. (2002, May). Debunking myths in qualitative data analysis. Forum Qualitative Sozialforschung/Forum: Qualitative Social Research, 3(2), Art. 10, www.qualitative-research. net/fqs-texte/2-02/2-02bong-e.htm. 1The term ‘method’ is used here in an epistemological sense as a set of steps and procedures by which to acquire knowledge, as distinct from the more encompassing term ‘methodology’, which includes the entire research process starting with ontological considerations of what there is that can be studied (Blumer, 1969; Hug, 2001; Strübing and Schnettler, 2004).

8 QUALITATIVE DATA ANALYSIS WITH ATLAS.TI Bourdon, Sylvain (2002, May). The integration of qualitative data analysis software in research strategies: Resistances and possibilities. Forum Qualitative Sozialforschung/Forum: Qualitative Social Research, 3(2), Art. 11, http://nbn-resolving.de/urn:nbn:de:0114-fqs0202118. Dey, Ian (1993). Qualitative Data Analysis: A User-friendly Guide for Social Scientists. London: Routledge. Elo, Satu and Kyngäs, Helvi (2008). The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107–15. Friese, Susanne (2014). On methods and methodologies and other observations, in Friese, Susanne and Ringmayr, Thomas (eds) ATLAS.ti User Conference 2013: Fostering Dialog on Qualitative Methods. Berlin: University Press, Technical University Berlin, http://opus4. kobv.de/opus4-tuberlin/frontdoor/index/index/docId/4413. Friese, Susanne (2016b). CAQDAS and grounded theory analysis. Working Papers WP 16-07, October 2016 (MMG Working Papers Print), http://mmg.mpg.de/fileadmin/user_upload/ documents/wp/WP_16-07_Friese-Theory-Analysis.pdf. Friese, Susanne (2018). Computergestütztes Kodieren am Beispiel narrativer Interviews, in Pentzold, Christian, Bischof, Andreas and Heise Nele (eds) Praxis Grounded Theory. Theoriegenerierendes empirisches Forschen in medienbezogenen Lebenswelten. Ein Lehr- und Arbeitsbuch, S. 277–309. Wiesbaden: Springer VS. Friese, Susanne, Soratto, Jacks and Pires, Denise (2018). Carrying out a computer-aided thematic content analysis with ATLAS.ti. MMG Working Paper 18-02, online. Gibbs, Graham (2007). Analysing Qualitative Data (Qualitative Research Kit). London: Sage. Hansen, Brett (2014). Grounding ethnographic content analysis, etic as well as emic strategies; a study of context for instructional designers, in Friese, Susanne and Ringmayr, Thomas (eds) ATLAS.ti User Conference 2013: Fostering Dialog on Qualitative Methods. Berlin: University Press, Technical University Berlin, http://nbn-resolving.de/urn:nbn:de:kobv:83- opus4-44183. Kelle, Udo (2004). Computer-assisted qualitative data analysis, in Seale, C., Gobo, G., Gubrium, J.F. and Silverman, D. (eds) Qualitative Research Practice, 473–89. London: Sage. Rambaree, Komalsingh (2014). Three methods of qualitative data analysis using ATLAS.ti: ‘A posse ad esse’, in Friese, Susanne and Ringmayr, Thomas (eds) ATLAS.ti User Conference 2013: Fostering Dialog on Qualitative Methods. Berlin: University Press, Technical University Berlin, http://nbn-resolving.de/urn:nbn:de:kobv:83-opus4-44270. Richards, Lyn (2009). Handling Qualitative Data: A Practical Guide, 2nd edn. London: Sage. Richards, Lyn and Morse, Janice M. (2013). Readme First for a User’s Guide to Qualitative Methods, 3rd edn. Thousand Oaks, CA: Sage. Silver, Christina and Lewins, Ann (2014). Using Software in Qualitative Research: A Step-by-step Guide, 2nd edn, chapter 7. London: Sage. Starks, Helene and Brown Trinidad, Susan (2007). Choose your method: A comparison of phenomenology, discourse analysis, and grounded theory. Qualitative Health Research, 17(10), 1372–80. Woolf, Nick (2014). Analytic strategies and analytic tactics, in Friese, Susanne and Ringmayr, Thomas (eds) ATLAS.ti User Conference 2013: Fostering Dialog on Qualitative Methods. Berlin: University Press, Technical University Berlin, http://nbn-resolving.de/ urn:nbn:de:kobv:83-opus4-44159. Woolf, Nick and Silver, Christina (2018). Qualitative Analysis Using ATLAS.ti: The Five level QDA Method. Routledge: New York.

Getting to know ATLAS.ti 2 For this chapter, you will work with the Children & Happiness sample project that you will find on the companion website. You can play around with the project material and explore as many functions and possibilities as you like – you do not have to be afraid of causing any severe damage. It is just ‘dummy’ material! Within this sample project you will get to know the user interface and the key features of the software. Please do not expect to learn about all the functions of the program at once. The aim is to give you a quick and easy insight into the possibilities of the software, to show you what a coded text or a network looks like or how to use the context menus. The operational parts of the architecture are the same for all sorts of functions, so having seen a few, you will easily recognize others and find your way through the program. This chapter also functions as an overview of what is to come. All later chapters go into further detail about the various aspects and functions previewed here. LEARNING OBJECTIVES In this chapter you will learn about important terms and concepts used in ATLAS.ti, like the six main entity types: documents, quotations, codes, memos, networks and links. You will preview some of the major tools needed for a qualitative data analysis, like the Code Manager, the Query Tool and the networks.

10 QUALITATIVE DATA ANALYSIS WITH ATLAS.TI SKILLS TRAININGS Skills training 2.1: starting the program and importing the sample project Skills training 2.2: getting to know the user interface Skills training 2.3: working with the entity managers Skills training 2.4: working with docked and floated windows Skills training 2.5: loading documents Skills training 2.6: creating tab groups and viewing documents Skills training 2.7: simple data retrieval Skills training 2.8: looking at a network Skills training 2.9: previewing the Query Tool SKILLS TRAINING 2.1 STARTING THE PROGRAM AND IMPORTING THE SAMPLE PROJECT Open ATLAS.ti. Currently the interface is available in English, German, Spanish, Portuguese and simplified Chinese. ATLAS.ti recognizes the language of your operating system and adjusts the display language automatically. All instructions in this book will be using the English-language interface. If you want to change the display language: • Select Options at the bottom left of the screen and under DISPLAY OPTIONS the display language that you want to use. When you open ATLAS.ti for the first time, a user account automatically has been created. ATLAS.ti uses the login name that you use on your computer as your user name. Figure 2.1 Import Project Bundle dialog

GETTING TO KNOW ATLAS.TI 11 Next, import the sample project for this chapter. The name of the project bundle file is Children & Happiness sample project (chapter 2). • Select the Import Project Bundle button from the opening screen. If ATLAS.ti is already open, select File/New, and from there the Import Project Bundle button. You have the choice to rename the project before importing (Figure 2.1). This is useful for team projects and if you do not want to overwrite an existing version. If you previously imported the project, it already exists in your project library and you have the choice either to overwrite the existing project or to rename the project that you are currently importing. SKILLS TRAINING 2.2 GETTING TO KNOW THE USER INTERFACE After importing the project, you see the ribbon on top, the project navigator on the lefthand side and the project name in the middle of the working area. At the top of the screen, you see the title bar where the name of the project is displayed. It also includes the Save, Undo and Redo functions on the left-hand side. Below the title you see ribbon tabs. Figure 2.2 The ATLAS.ti user interface I assume that you are familiar with ribbons from other Windows software. A ribbon is a graphical control element in the form of a set of toolbars placed on several tabs. Ribbons are grouped by functionality rather than entity types, as was the case in older versions of ATLAS.ti and other Windows programs. Ribbons, in comparison, use tabs to expose different sets of controls, eliminating the need for numerous parallel toolbars.

12 QUALITATIVE DATA ANALYSIS WITH ATLAS.TI The ATLAS.ti ribbon The five core tabs in ATLAS.ti are: • Home • Search Project • Analyze • Import & Export • Tools & Support Figure 2.3 Core tabs that give access to the various functions Depending on the function you are using, additional contextual tabs will appear. These will be highlighted by a colored box at the top of the ribbon. The colored box has the same color as its entity type. The Home tab is the starting point for most projects. Figure 2.4 The Home tab You can start here to add documents to a project, create new codes, memos and networks, open various browsers to be displayed in the navigation area on the left-hand side of the screen, write a comment for your project or open the various managers. Under the Import & Export tab you will find several other options to import data. You can import Twitter data, data stored in Evernote, data from reference managers like EndNote, Mendeley and the like to support the analysis of literature, and data from Excel Figure 2.5 Import & Export options

GETTING TO KNOW ATLAS.TI 13 to analyze open-ended questions from survey data. I will discuss most data import options in Chapter 3. Further, you can import and export codes lists (see Chapter 4) and document groups, and you can export your codings as an SPSS syntax file or as an Excel file for further quantitative analysis in other statistical programs. The Search Project tab allows to search through all entities of your project. Later in this tour, there will be an exercise where you practice its functionality. Figure 2.6 The Search Project tab The Analyze tab offers several advanced functions to analyze your data after coding. These functions will be discussed in Chapters 6 and 9. Figure 2.7 The Analyze tab Under the Tools & Support tab, you will find three things: (1) you can directly get in touch with the ATLAS.ti team by reporting a problem or sending a suggestion; (2) you can access resources like the ATLAS.ti Web page, video tutorials, the user forum and also the companion website for this book; and (3) you can create and change user accounts in the User Management group and clean up redundant codings. See the chapter on teamwork for further details. Figure 2.8 Tools & Support options

14 QUALITATIVE DATA ANALYSIS WITH ATLAS.TI The backdrop Under FILE you will find all options that concern your project, like creating a new project, opening existing projects, saving and deleting projects, exporting and merging projects. All project management options are discussed in Chapter 3. Project merge is discussed in Chapter 9, on team project work. The navigation panel On the left-hand side of the main editor you will find the navigation panel. It opens automatically if you start the software and displays the Project Explorer. • To open a branch, click on the triangle in front of each entity or right-click and select Expand from the context menu. With a double-click on a main branch, the respective manager of the selected entity type opens. Figure 2.9 The backdrop menu Figure 2.10 Project Explorer in the navigation panel From the main branches you can access the different entity types that you will be working with in ATLAS.ti. These are: documents, codes, quotations, memos, networks and the various entity groups. Below you will find a description for each entity. In addition to the Project Explorer that holds all project items, you can also open browsers that only contain one entity type. Browsers are available for documents, codes, quotations, memos and networks. The single-entity browsers open in tabs next to the Project Explorer. The Project Explorer and all browsers have a search field on top of the list of entities. This helps working with long lists.

GETTING TO KNOW ATLAS.TI 15 • Open the Code Browser either by clicking on the drop-down arrow of the Navigator button or launch it directly from the Navigator Group. Description of entities that you are going to work with in ATLAS.ti Documents are the data you add to an ATLAS.ti project. These can be text, image, audio, video or geographic materials that you wish to interpret. In Chapter 3 you learn more about the types of documents that can be added to a project and how this is done. A quotation is a segment from a document that is considered interesting or important to the user. Usually, the researcher manually creates quotations. However, for repetitive words, phrases or structural information like speaker units, the Auto Coding Tool can be used. It automatically segments the data and assigns a code to them. Although the creation of quotations is almost always part of a broader task like coding or writing memos, quotations can also be created without coding. They are called ‘free’ quotations. If you are using discourse analysis or an interpretive approach to analysis, or if you’re working with video data, free quotations can be your starting point for analysis rather than coding the data right away. Codes are used as classification devices at various levels of abstraction (see Chapters 4 and 5). The purpose of coding is to describe the data – so you can later retrieve data segments by topics – to query them and to make comparisons. You can also think of codes as tags and the process of coding as tagging. Memos capture your analytic thoughts and ideas. They can ‘stand alone’ or they can be linked to quotations, codes and other memos. Memos can also be included in the analysis as data by converting them into a project document. You will learn more about memos throughout the book. Networks allow you to conceptualize your data by connecting sets of related elements together in a visual diagram. With the aid of networks, you can express relationships between codes, quotations, memos, documents and groups. Code–code and quotation– quotation links can be named using relations like ‘is associated with’, ‘is part of’, ‘leads to’, ‘is consequence of’, ‘supports’, ‘discusses’ and the like. There are a few standard relations. But you can create any relation you need for your analysis. Groups are a way to build clusters of documents, codes, memos and networks to be used as filters. Document groups fulfill a special function as they can be regarded as quasidichotomous variables. For instance, you can group all female interviewees into a document group named ‘gender: female’ and all male interviewees into a group named ‘gender: male’. You can do the same for different professions, marital status, education levels and so on. Document groups can later in the analysis be used to restrict code-based searches like: ‘Show Figure 2.11 Entity browsers