Book - Qualitative Data Analysis With NVIVO

8/17/2019 Book - Qualitative Data Analysis With NVIVO

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SECOND EDITION

UALITATIVE DATAANALYSIS WITH

NVIVO


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PAT B AZELEY & KRISTI JACKSON


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UALITATIVE DATAANALYSIS WITHNVIVO


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SECOND EDITION

UALITATIVE DATAANALYSIS WITHNVIVO

PAT B AZELEY & KRISTI JACKSON


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Contents

Figures viii

Tables xiiAbout the authors xiii

Preface to the second edition xiv

Chapter outline xvi

Acknowledgements xviii

1 Perspectives: Qualitative computing and NVivo 1

Qualitative research purposes and NVivo 2

The evolution of qualitative data analysis software 4

Issues raised by using software for qualitative data analysis 6Exploring an NVivo project 10

Overview: what’s in an NVivo project? 23

2 Starting out, with a view ahead 2

Exploring the research terrain 24

Explore the terrain with software 26

Looking ahead: connecting a web of data 40

Looking ahead: keeping track of emerging ideas 42

Memos, annotations or links: which should it be? 45

Saving and backing up your project 45

! "esigning an NVivo data#ase $

Qualitative data for your project 47

Thinking cases 50

Preparing data sources 56

Storing qualitative data in NVivo 61

Managing data sources in NVivo 63Questions to help you work through these early phases 67

Coding #asics %&

Goals for early work with data 68

Building knowledge of the data through coding 70


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Storing coding in nodes 75

v

vi contents

Identifying and naming codes 80

Further coding in NVivo 84

Practical issues in coding 88

Moving on 93

' (oing on with coding )'

Creating a structured coding system 95

Organizing and coding with nodes in trees in NVivo 99

Automating routine coding 108Automating coding with word frequency and text search queries 110

Closeness and distance with coded data 117

Moving on 121

% Cases, classi*cations, and comparisons 122

Understanding case types and case nodes 122

Making case nodes 123

Understanding attributes, values, and classifications 128

Creating classifications, attributes, and values 131Using attributes for comparison 141

Using sets to manage data 146

Overview 150

$ +oring with multimedia sources 1'

The promises and perils of non-text data 154

Using images in your research 157

Working with audio and video sources 164

Accessing and using web-based data 171Exporting images, audio, and video 176

& -dding re.erence material to your NVivo pro/ect 1$&

Using reference material in your project 178

Importing, coding, and viewing pdf sources 182

Importing reference material from bibliographic software 188

Capturing web pages with NCapture 192

) "atasets and mi0ed methods 1)'Combining data types in research 195

What does a dataset look like? 200

Managing data in a dataset 201

Coding dataset text 208

Importing and analysing a social media dataset 209


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Analysing datasets and other mixed data types 213

contents

vii

1 ools and strategies .or visuali3ing data 21$

Why visualize? 217

Case analysis using models 219

Grouping and conceptualizing 223

Comparative analysis with charts and graphs 226

Explore relationships via the modeller 230

Build a visual narrative 234

Mapping connections – building theory 234

Exploratory visualization using cluster analysis 236

Exporting models and visualizations 240

Concluding comments 240

1 4sing coding and 5ueries to .urther analysis 22

The analytic journey 243

Queries in NVivo 244

Common features in queries 246

Seven queries 248

Using coding and queries to further analysis 255

Creating and customizing reports 265

1 eamwor with NVivo 2$

Getting ready for teamwork 270

Options for storing and accessing a team project 273

Getting started as a team with NVivo 276

Using NVivo’s tools to facilitate team communication 282

Coding as a team 284

Combining databases 286

Comparing coding by different researchers 290

Moving on – further resources 297

References 299

Index 305


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6igures

The NVivo workspace 13

Viewing hyperlinks, ‘see also’ links, and annotations

from an internal source 16

Nodes, with referenced text and context menu 18

Coding density and coding stripes 20

Filter options for a report 22

Creating a new project 27

Adding a connector to a conceptual map 29

Creating a memo document to use as a journal 31

Importing a file as an internal source 33

Creating an annotation 35

Viewing a see also link 39

Sample of interview text showing headings 59

Importing the description for an internal source 62

Folders for sources 64

Frank’s document showing application of multiple

codes to each passage of text, and annotations 74

Screen arrangement for coding 77

Alternative ways to add coding to text 79Viewing the context of a coded passage 85

Exported node list with descriptions 86

The shopping catalogue ‘tree’ 96

Creating a new node within a tree 101

Alternative coding suggestion for theEnvironmental

Change sample project 105

Using a set for a metaconcept 107

Setting up for auto coding 109Word frequency query with stemmed words 111


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viii

*gures

i0

5.7 Displayed results of a text query, showing keyword

in context 1145.8 Checking coding with a compound query 121

6.1 Automatically assigning a source to a case node

when importing 125

6.2 Cases, attributes and values 128

6.3 The structure of classifications, attributes and values 130

6.4 Creating a new attribute and values 133

6.5 Classification sheet with attribute values for interview

participants 1356.6 Setting up a classification sheet in Excel 136

6.7 Importing a classification sheet – Step 2 137

6.8 Importing a classification sheet – Step 3 138

6.9 Report of cases with attribute values 139

6.10 Making selections for a summary report

of attribute values 140

6.11 Using a matrix coding query to view data sorted

by attribute values 142

6.12 Selecting nodes for rows in a matrix query 1436.13 Selecting attributes for columns in a matrix query 144

6.14 The find bar, immediately aboveList View 148

6.15 Defining criteria to select cases using advanced find 148

7.1 Picture with a newly inserted log entry 161

7.2 The picture tab of a node open inDetail View 163

7.3 The playhead moving across the timeline of an audio

or video source 167

7.4

The playback

group of the7edia

ribbon 1687.5 Coding stripes with shadow coding on a media file 172

7.6 The NCapture icon showing in Internet Explorer Command bar 173

7.7 Collecting a web page with NCapture 174

7.8 Collecting a YouTube video with NCapture 175

7.9 Selecting an NCapture file for importing as a web page 175

7.10 A web page converted into a pdf and opened in NVivo 176

8.1 Selection and retrieval of text in a pdf source 185

8.2 Selection and retrieval of text in a line-numbered

pdf source 186

8.3 Selecting a region of text for coding 187

0 *gures

8.4 Selecting options in the import dialogue for reference


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material from EndNote 190

8.5 Web page ‘captured’ as a pdf source in NVivo 193

9.1 Data from a survey prepared using an Excel spreadsheet 200

9.2 Checking the analysis type for each field whenimporting a dataset 203

9.3 Classifcationsgroup in theCreate ribbon 205

9.4 Locating the nodes to be classified 206

9.5 Modifying (grouping) the values of a dataset attribute 207

9.6 Auto coded node arrangements for social media data 212

9.7 Viewing text and contributor in an auto coded

LinkedIn discussion 212

9.8 Automatically assigned node classifications and

attributes for social media usernames 213

10.1 Chart of coding for Frank showing top 20 codes used 219

10.2 Frank’s document showing nodes most often used

for coding as stripes 220

10.3 A case map for Frank, showing the centrality of ambition

and reputation as driving forces in his life 221

10.4 Sorting nodes into ‘trees’ 223

10.5 Using the coding system to build a taxonomy of experience 225

10.6 Comparing emotional experiences associated with researchfor males and females 226

10.7 Interactive comparative modelling of cases 228

10.8 Construction of a relationship between two items in NVivo 232

10.9 Visualization of a relationship in the modeller 232

10.10 Visualization of multiple relationships in the modeller 233

10.11 Creating a visual narrative 234

10.12 Grouping related concepts 235

10.13 Noting theoretical associations 235

10.14 Mapping evolving theoretical connections 23610.15 Dendrogram and associated statistics based on

clustering nodes by word similarity 237

10.16 3D cluster maps of features of the natural

environment, based on word similarity 238

11.1 Saving a query 247

11.2 Specifying the data in which the query should search (scoping) 248

11.3 Words found using a word frequency query 249

11.4Finds (spread to broad context) from a search for fsh* 250

*gures

0i

11.5 Matrix coding query – frequencies of responses

for each row–column combination 251

11.6 Text retrieved from the cell of a matrix 251


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11.7 Finds from a coding query searching for a combination

of nodes 252

11.8 Finds from a compound query combining two text queries 253

11.9 Nodes coding another node, found with a group query 254

11.10 Results from a coding comparison query 25511.11 Filter options in a node classification summary report 266

11.12 Report view options 267

11.13 Predefined Node Structure Report open in designer 268

11.14 Modifying the fields in a Predefined Node Structure Report 268

12.1 Setting the application options to prompt for user on launch 278

12.2 The Welcome window showing a project copied for

different team members 281

12.3 Coding stripes showing the work of three coderson a source document 292

12.4 Statistical output from a coding comparison query 294

12.5 Comparing the coding detail for two users 294


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a#les

2.1 Memos, annotations and links – summary table 44

Formatting strategies to facilitate case construction

from different types of sources 54

Arranging data sources in NVivo 66

6.1 Folders, sets or cases: which should I use? 151

12.1 Choosing a database storage and management strategy 275


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0ii


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-#out the authors

Pat Bazeley (PhD, Macquarie University) provides research training and consulting

through her company, Research Support P/L, to academics, graduate students

and practitioners from a wide range of disciplines in universities and govern-

ment agencies both within Australia and internationally. Additionally, she has a part-

time appointment as Associate Professor in the Centre for Primary Health Care

and Equity at the University of New South Wales. Since graduating in psychology

she has worked in community development, project consulting and in academic

research development. Her experience with research design and methodology

broadly across the social sciences includes over 20 years of providing training in

the use of QSR software for qualitative and mixed methods research. Her research

and publications focus on qualitative and mixed methods data analysis, and on

the development and performance of researchers.

Kristi Jackson (MEd, University of Vermont) was Vice President of an evaluation

research firm in Denver, Colorado until 2002, when she founded Queri, a quali-

tative research consulting company. Queri provides assistance to large entities

(e.g., the Centers for Disease Control and Prevention, the National Institutes of

Health, and the US Government Accountability Office) as well as many small,

nongovernmental organizations in rural and urban settings. Her conference

pres- entations and published papers often focus on the implications of the

growing importance of qualitative data analysis software, and she is currently

studying conceptualizations of qualitative research transparency among

researchers who do and do not use this genre of software. In her 17 years ofexperience as an evaluation researcher, she served as principal investigator, co-

investigator or advisor on a diverse array of qualitative research projects,

primarily in education and health.


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0iii


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Pre.ace to thesecondedition

Since the publication of the first edition of this book, the world of qualitative

data analysis has changed yet again. The tools in NVivo have adapted to the

increasing popularity of mixed methods research, research involving use of

multimedia, and the greater involvement of teams in qualitative projects. They

have adapted, as well, to a world-wide increase in the digitization of data and

new modes of digital communication, for example, through social media sites

such as Facebook, LinkedIn, Twitter and YouTube. While the impact of these

advancements in the software required additions to this book, another wave of

change also informs this edition. Where the emphasis in early qualitative data

analysis software was on coding as a primary strategy, this emphasis is progres-

sively shifting to provision of tools to facilitate thinking, linking, writing, modelling

and graphing in ways that go beyond a simple dependence on coding. As a

response to these many developments, in this edition we include new chapters

on working with multimedia sources, datasets from surveys, and data from

social media sites; using NVivo for your literature review; ways to leveragevisualizations to explore your data and communicate your findings; and on

strategically using NVivo in the context of team research.

One of the consequences of our need to include new material in this edition

of the book, compared to the first, is a subtle shift in focus from approaches to

undertaking a qualitative project in the context of one or another methodological

approach, supported by NVivo, to a more direct focus on the tools NVivo pro-

vides and how these can support qualitative research more generally. For read-

ers who found the first edition helpful methodologically as well as technologically

and who notice the change (and for others who would benefit from help with

methods), Pat offers the extended alternative of a book dedicated to providing

down-to-earth help for researchers working with qualitative data,Qualitative

Data Analysis: ractical !trategies (Sage, 2013). Each book is, in many ways, a


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companion to the other. Our primary goal in this book remains, however, to

help you thought"ully make use of the software as you apply it strategically to

your data.

Qualitative data analysis software, and NVivo in particular, has becomeincreasingly flexible in adapting to the demands of modern research projects. An

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pre.ace 0v

unfortunate but unavoidable consequence of increasing flexibility is increased

complexity. QSR International, the developers of NVivo, have responded by pro-

viding guidance for users in various forms – video tutorials, webinars, and exten-

sive, detailed on-line Help – all of which are extremely valuable if you know

what to ask for, and for stimulus in using the software. They are less helpful in

providing guidance as to what tools to choose for a particular research problem,

and in guiding the user through the strategies needed to work right through a

research task without becoming lost in the multiple options available.

The multiplicity of approaches to analysis of qualitative data poses particular

problems not only for writers of Help files and tutorials, but also for a book of

this nature, with the purpose of walking you through a project: How best to

organize and sequence the tasks and the introduction of different tools? As

there is no standard pathway through a project, we don’t expect everyone to work

straight through this book. Nevertheless, the book is organized along broad

sequential stages in working from raw data through conceptualization to strategies

for analysis. We attempt, therefore, to take you right through a series of actions

to complete necessary steps along the most common pathways trav- elled with

each primary tool, source of data, or approach, but you will need to step forward

or back at various times to find the instruction, suggestions or discussion you most

need for particular points in your project. At the same time, at each step we point

you to where you might find additional ideas and assistance in case your

journey requires a digression or raises a complication.

Following the chapter-by-chapter sequence will take you through all the ele-ments you need to consider: this might be the best general approach for some-

one new to the software. Those who already have some knowledge of the

software from earlier versions might use the brief description of each chapter

or the table of contents to identify where they might find the major discussion

of a particular topic within the chapters. A more detailed index is provided, of

course, at the end of the book. A companion website(www.sagepub.co.uk/

bazeleynvivo) accompanies the book, providing you with access to sample data,

additional technical help, links to useful resources, and updates to information

in this book. The Help files provided by QSR are both detailed and comprehen-sive, and they are always updated as the software is updated. They can be

consulted, therefore, to resolve any discrepancies between our instructions or

screen shots and the software that might result from ongoing updates to the

software.1

1The current version of the NVivo software, at the time of writing, was version 10 forWindows. Check the companion website also for supplementary notes to assist withfeatures changed or added in later versions.

http://www.sagepub.co.uk/http://www.sagepub.co.uk/


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Chapter outline

Chapter #: erspectives: Qualitative computing an$ %Vivo – Settle in with a

little grounding in the history and relevance of this genre of software to help you

prepare for the journey. After considering the potential advantages and disadvan-

tages of using software for this type of work, we take you on a gentle tour of an

existing database to start putting things into perspective.

Chapter &: !tarting out' with a view ahea$ – Start a project in NVivo well

before collecting or constructing data if you can. Begin with a question and your

thinking around that question, or perhaps with a theory. Craft an early model of

these ideas, add some literature (or any source material), and start writing

memos. Create links to help trace the connections in these early ideas. By starting

your project in NVivo earlier rather than later, you will lay a sure foundation for

working with data and verifying the conclusions you draw from them.

Chapter (: Designing an %Vivo $atabase – Prepare for the work ahead by

reflecting on what a ‘case’ might be in your research and tie this to the

strategies you employ as you prepare your data. While the software supports a

wide range of text, audio, video, picture, spreadsheet and web page formats,

you can do things to prepare the data (text-based files in particular) to

maximize the benefit of some of the software tools you might use later.

Chapter ): Co$ing basics – Discover the day-to-day activities of

reflecting on the data and coding as you work through your first sources. Connect

this coding process with the strategies you learned for memoing and linking, as

ideas take shape. Observe and manage your use of inductive and deductive coding

strategies as you learn the basics of creating codes (nodes) and coding.

Chapter : +oing on with co$ing – Go beyond the basics to start developing a

codingsystem designed to address your research questions. Learn to move

nodes, combine and rename them. Consider different conceptual approaches

to creating the system and learn some tips to keep you from creating a volumi-

nous, chaotic coding structure. Experiment with automated coding strategies

(the auto code and text-mining tools) before taking steps to review your coding by

standing back and then coming close.

Chapter ,: Cases' classifcations an$ comparisons – Learn how to

store demographic and other kinds of quantitative information as attributes of

cases. Learn different ways of entering these into your project, and how to use

them to make comparisons and examine patterns in your data.


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0vi

chapter outline 0vii

Chapter -: .orking with multime$ia sources – Seriously consider why

you might or might not want to use audio, video, images and web pages as

material in your project and prepare yourself for a host of methodological

considerations. Then learn how to capture, import, code and link this material and

examine the way it exports as either Word or html files.

Chapter /: A$$ing re"erence material to your %Vivo pro0ect –

Understand the opportunities and hazards associated with importing and

coding pdf files, and see how to import material from bibliographic databases

and web pages. Consider the many different reasons why you might use

reference material and push your thinking beyond a basic literature review.Chapter 1: Datasets an$ mi2e$ metho$s – Explore the new ways you can

combine data types in NVivo, in particular through the use of structured

questionnaires, web surveys, and social media that typically contain both forced-

choice and open-ended fields. Learn how to prepare, import, code and sort such

data, and read an assortment of examples to get you thinking further about

different types of data and the rationale for using them in NVivo.

Chapter #3: 4ools an$ strategies "or visuali5ing $ata – Apply the

visualization tools in NVivo to think through your cases, concepts, relationships

and theories. Models, charts, graphs and diagrams can also be used to display your

results and communicate findings, and while in the software they also provide

you with direct access to the data they represent.

Chapter ##: 6sing co$ing an$ 7ueries to "urther analysis – Consider

the corpus of these sophisticated search tools and the various ways you can

use them to search for relationships and patterns in the data. This might be the

culmination of your analytic journey, or you might experiment with a few

queries early on in the project. Learn the framework for the overall structure

and purpose of the queries, before turning to other chapters where a contextually

grounded example of each of the seven queries is located.

Chapter #&: 4eamwork with %Vivo – Dive into the exciting and challenging

experience of team research and learn how NVivo can help with organizing team

activities and framing the analytical process. Some tools in NVivo are specifically

designed for team research and others should be addressed anew in the context

of team research. Practical help includes strategies for tracking team member

contributions, ways of coding as a team, combining copies of databases andcomparing the coding of different researchers.


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-cnowledgements

We are pleased to specifically acknowledge the original developers of the software,

Lyn and Tom Richards, who launched both of us on this journey back in the

early 1990s. While we learned and changed from our interactions with other

researchers through the years, the entire journey would never have started with-

out Lyn and Tom, their enthusiasm, and especially their vision for qualitative

research evidenced in the intellectual foundation of NVivo. More recently, we

owe thanks to the many staff members at QSR, especially Marcus Ogden for his

tireless responses to our enquiries and suggestions. The march ahead with new

ways of thinking about qualitative data is also due to the many dedicated

researchers around the world who continue to explore, advance, challenge and

discuss the role of software in qualitative research. While we owe a great dealto the collective work of our colleagues and the stimulation of our clients, there

are too many to mention specifically. Know that we are thankful for the many

ideas you provide in our ongoing conversations; they most certainly helped

shape this book. We are indebted, too, to the team at Sage, especially Jai Seaman

and Anna Horvai, for their encouragement and support for this second edition

of this book.

Writing about constantly evolving software is always time pressured. This

collaboration of two long-term teachers of research using software has been

built on a shared perspective on what software can and cannot do, and how best touse software to support research. On a more personal level, it has been built on

mutual respect, tolerance and good humour. Each of us learned from the other,

made demands on the other, and supported the other. For Pat, after a long and

intense period of solo writing, this has been a refreshing experience. This

collaboration made this revision possible, with friendship surviving a tight time

frame not only intact but strengthened: thank you, Kristi!

Starting farther back in her own voyage, Kristi would also like to acknowledge her

parents, Buzz and Lainie: thank you for preparing me with perhaps the most

important provision for the ongoing journey – endless encouragement to pursue

my interests. I also thank one very dedicated and talented Pat Bazeley for such a

happy collaboration.


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0viii


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1

Perspectives:Qualitative

computing andNVivo

Perspective influences the way we approach any new experience – including

the way we approach the use of software for qualitative analysis. The history of

qualitative data analysis software (QDAS has influenced the current tra!ec" tory

of software develop#ent$ and this history is also lin%ed to current researcher

perceptions of advantages and disadvantages of software. Depend" ing on when

current qualitative research experts chose to adopt (and in so#e cases

su&sequently a&andon QDAS$ they have different understandings of the

purpose and applica&ility of software tools. 'any of us who use$ teach and

write a&out QDAS encounter &oth positive and negative clai#s regarding the

software that are o&solete &ut have survived as part of the discourse a#ong

qualitative #ethods instructors and scholars. n this chapter we place so#e of

the clai#s and counterclai#s in perspective &efore providing you with a &rief

exploration of how )*ivo$ as one exa#ple of QDAS$ can assist analysis of

qualitative data.

8n this chapter:

• iscover ho! the use of soft!are can support you in doing qualitative research.

• ?nderstand the historical contet and ongoing development of this type of

soft!are.

• )onsider issues and obEections people have raised about the use of soft!are

for qualitative research.

• 2egin !ith a tour of an eisting database to understand the overall

composition of the soft!are.


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1


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2 5ualitative data analysis with nvivo

Qualitative research purposes and NVivo

+esearchers engage in pro!ects involving interpretation of unstructured or se#i"

structured data for a variety of reasons. These #ight include exploration$

description$ co#parison$ pattern analysis$ theory testing$ theory &uilding$ or evaluation. 'ethodologists routinely urge researchers to assess the fit &etween

purpose and #ethod$ with the choice to use a qualitative approach &eing deter"

#ined &y the research question and purpose$ rather than &y prior preference of

the researcher ('axwell$ ,-/0 +ichards 1 'orse$ ,-,. Qualitative #ethods

will &e chosen in situations where a detailed understanding of a process or

experience is wanted$ where #ore infor#ation is needed to deter#ine the

&oundaries or characteristics of the issue &eing investigated$ or where the only

infor#ation availa&le is in non"nu#eric (e.g.$ text or visual for#. Such inves"

tigations typically necessitate gathering intensive and2or extensive infor#ationfro# a purposively derived sa#ple.

9ow NVivo supports 5ualitative analysis

QS+ nternational$ the developers of )*ivo$ pro#ise only to provide you with

a set of tools that will assist you in underta%ing an analysis of qualitative data.

The use of a co#puter is not intended to supplant ti#e"honoured ways of

learning fro# data$ &ut to increase the effectiveness and efficiency of such

learning. )*ivo was developed &y researchers$ and continues to &e developed

with extensive researcher feed&ac% to support researchers in the varied ways

they wor% with data. The efficiencies afforded &y software release so#e of the

ti#e used to si#ply 3#anage4 data and allow an increased focus on ways of

exa#ining the #eaning of what is recorded. The co#puter4s capacity for

recording$ sorting$ #atching and lin%ing can &e harnessed &y researchers to

assist in answering their research questions fro# the data$ without losing

access to the source data or contexts fro# which the data have co#e. n so#e

instances$ researchers reported that the software opened up new ways of seeing

their data they #issed when #anaging the infor#ation without software.

The average user of any software progra# typically accesses only a s#all portion of its capa&ilities0 this is no dou&t true for users of )*ivo also. f you

are using )*ivo for a s#all descriptive pro!ect$ you can wor% without having

5xa#ples include exploring #ultiple #eanings in the data (6. +ichards$ ,--,$

challenging researcher assu#ptions and first i#pressions of the data (7arcia"8orta 1

7uerra"+a#os$ ,--9$ &eco#ing aware of gaps in the collected data (:ic%ha# 1

:oods$ ,--;$ revisiting data with a new conceptual lens (Sin$ ,--%an$ ,--?$ reflecting on the social construction of

evidence (@ac>yns%i 1 @elly$ ,--?$ and unpac%ing so#e of the tacit views of research

transparency a#ong qualitative researchers (ac%son$ ,--9.


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5ualitative computing !

to learn co#plex procedures$ &ut if you are underta%ing co#plex analytical

tas%s$ you can find the additional tools you need. Bhoices a&out what tools to

use and how to use the# are entirely up to you.

Csing )*ivo during the analysis of qualitative data will help you

• 4anage data F to organiCe and /eep trac/ of the many messy records that go into

ma/ing a qualitative proEect. hese might include not Eust ra! data files from inter7

vie!s, questionnaires, focus groups or field observations, but also published research,

images, diagrams, audio, video, !eb pages, other documentary sources, rough notes

and ideas Eotted into memos, information about data sources, and conceptual maps

of !hat is going on in the data.

• 4anage ideas F to organiCe and provide rapid access to conceptual and

theoretical /no!ledge generated in the course of the study, as !ell as the data

that support it, !hile at the same time retaining ready access to the contet from

!hich those data have come.

• Buery data F to as/ simple or comple questions of the data, and have the program

retrieve from your database all information relevant to determining an ans!er to those

questions. *esults of queries are saved to allo! further interrogation, and so querying

or searching becomes part of an ongoing enquiry process.

• isualiCe data F to sho! the content and3or structure of cases, ideas, concepts, sam7

pling strategies, timelines, etc., at various stages of the interpretive process, and to

visually represent the relationships among these items in a range of =often interactive>

displays.

• *eport from the data F using contents of the qualitative database, including informa7

tion about and in the original data sources, the ideas and /no!ledge developed fromthem, and the process by !hich these outcomes !ere reached.

There is a widely held perception that use of a co#puter helps to ensure rigour

in the analysis process. n so far as co#puter software will find and include in

a query procedure$ for exa#ple$ every recorded use of a ter# or every coded

instance of a concept$ it ensures a #ore co#plete set of data for interpretation

than #ight occur when wor%ing #anually. There are procedures that can &e

used$ too$ to chec% for co#pleteness$ and use of a co#puter #a%es it possi&le to

test for negative cases (where concepts are not related. Perhaps using a co#"

puter si#ply ensures that the user is wor%ing #ore #ethodically$ #ore thor"

oughly$ #ore attentively. n these senses$ then$ it can &e clai#ed that the use

of a co#puter for qualitative analysis can contri&ute to a #ore rigorous analy"

sis. 5ven so$ hu#an factors are always involved$ and co#puter software cannot

turn sloppy wor% into sound interpretations$ nor co#pensate for li#ited inter"

pretive capacity &y the researcher. As #uch as 3a poor wor%#an cannot &la#e

his tools4$ good tools cannot #a%e up for poor wor%#anship.

f you are co#ing to )*ivo without first #eeting qualitative #ethodology or

#ethods$ then you are strongly advised to read so#e general texts such as Ea>eley

(,-/$ Flic% (,--9$ 'axwell (,-/$ Patton (,--,$ +ichards (,--9$ +ichards and'orse (,-,$ or introductory texts fro# within your own discipline. Then use

the


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5ualitative data analysis with nvivo

reco##ended reading lists in those texts to further explore the #ethodological

choices availa&le to you. Qualitative #ethods are a rich$ diverse$ and co#plex

sphere of %nowledge and practice. Ee careful a&out adopting the first approach

you encounter (e.g.$ ethnography or pheno#enology as the only approach$ or

assu#ing that &ecause you are wor%ing fro# data up that you are doing grounded

theory. 6earn a&out the relevant #ethodological de&ates regarding data collection$

#anage#ent and interpretation &efore fully fra#ing your research.

Perhaps surprisingly$ the tools descri&ed in this &oo% are 3#ethod"free4$ in so

far as the software does not prescri&e a #ethod &ut rather supports a wide

range of #ethodological approaches. Different tools will &e selected or e#pha"

si>ed and used in alternative ways for a variety of #ethodological purposes.

He reiterate that no single soft!are pac/age can be made to perform qualitative

data analysis in and of itself. he appropriate use of soft!are depends onappreciation of the /ind of data being analyCed and of the analytic purchase the

researcher !ants to obtain on those data. =)offey < At/inson, 1996# 166>

There are$ nevertheless$ so#e co##on principles regarding the #ost effective

use for #any of the tools$ regardless of #ethodological choices. For exa#ple$

the la&els used for coding categories will vary depending on the pro!ect and the

#ethods chosen$ &ut the principles e#ployed in structuring those categories

into a &ranching coding syste# are co##on to #any #ethods where coding

ta%es place. These co##on principles allow us to descri&e in general how you

#ight use the various tools. t is then your tas% to decide how you #ight apply

the# to your pro!ect.

he evolution o. 5ualitative data analysis so.tware

Alongside the various strands of qualitative #ethods applied and refined in the

9G-s$ university faculty fro# Australia$ 7er#any$ and )orth A#erica &egan

independently developing software progra#s to facilitate the analysis of quali"

tative data. The developers of these software progra#s &elieved that a pri#ary purpose of their enterprise was to facilitate data #anage#ent and pro#ote the

rigour of qualitative research.,

nitially$ these early developers were wor%ing in isolation$ unaware of paral"

lel develop#ents &y other researchers (Davidson 1 di 7regorio$ ,-0 Fielding$

,--G. After networ%s of researchers &egan infor#ally sharing their experi"

ences with software in qualitative analysis$ the first Surrey +esearch 'ethods

conference was held at the Cniversity of Surrey in the C@ in 9G9. This

,

For detailed discussions on the purpose and evolution of )CDHST and )*ivo$ see+ichards and +ichards (99? and T. +ichards (,--,.


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5ualitative computing '

conference esta&lished a dialogue &etween developers and early users (Fielding 1

6ee$ ,--


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% 5ualitative data analysis with nvivo

software progra#. A few products too% the lead around ,--;$ so#e have fallen

&y the wayside$ and as of today the BAQDAS networ%ing site provides reviews

of only nine qualitative analysis progra#s.

To #ore fully investigate the influence that different QDAS options have on

the research process$ and to re"exa#ine whether the choice of one of the current

progra#s over another has an influence over the research findings$ organi>ers

of the )etherlands Association for Qualitative +esearch (@:A6=) designed a

co#parative investigation (5vers$ Silver$ 'ruc%$ 1 Peeters$ ,-. 5xperts in

several of these software pac%ages (AT6AS.ti$ Bassandre$ 'AKQDA$ )*ivo$

and Transana independently analysed a co##on set of data. These participants

were in widespread agree#ent that they ca#e up with very si#ilar conclusions

regarding the pri#ary research questions and that the i#pact of a particular

QDAS in analysing the data was negligi&le. This corro&orates the clai#s &y

7il&ert$ di 7regorio$ and ac%son (,-/ that over the last ,- years QDASsoftware has si#ultaneously &eco#e #ore co#prehensive$ #ore applica&le to

a diverse range of #ethodologies$ and #ore ho#ogeneous.

8ssues raised #y using so.tware .or5ualitative data analysis

3Tools extend and qualitatively change hu#an capa&ilities4 (7il&ert$ ,--, ,,,.

Csers of )*ivo4s tools can face opposition fro# those who express dou&ts

a&out using software for analysis of qualitative data$ or who si#ply have an

aversion to technological solutions. )onetheless$ the develop#ent of software

tools (and technology in general has a significant i#pact on how research is

done. The constantly expanding use of the we& to provide access to data is now

extending and changing the range of qualitative source data as well as the

structure of surveys and survey sa#ples. The advent of social networ%ing will

have an as yet un%nown influence on social research and #ethod. 8istorically$ the

widespread use of tape recorders in interpretive research changed &oth the

level and %ind of detail availa&le in raw #aterial for analysis$ and as video

recording &eca#e #ore co##on$ data and #ethod changed again.7iven this context$ it is dangerous to adopt a si#plistic understanding of the

role of QDAS. Tools range in purposes$ power$ &readth of functions$ and s%ill

de#anded of the user. The effectiveness with which you can use tools is partly

a software design issue &ecause software can influence your effectiveness &y

the nu#&er or co#plexity of steps required to co#plete a tas%$ or &y how

infor#ation is presented to the user. t is also a user issue &ecause the relia&ility

(or trustworthiness of results o&tained depends on the s%ill of the user in &oth

executing #ethod and using software. The danger for novices using a sophis"

ticated tool is that they can 3#ess up4 without reali>ing they have done so

(7il&ert$ ,--,.


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5ualitative computing $

8istorically$ the use of QDAS has facilitated so#e activities (such as coding

and li#ited others (such as seeing a docu#ent as a whole or scri&&ling #e#os

alongside text. n so doing$ early co#puter progra#s so#ewhat &iased the way

qualitative data analysis was done. 8istorically$ also$ qualitative researchers

were inclined to &rand all 3code"and"retrieve4 software as supporting grounded

theory #ethodology – a #ethodology which has &eco#e rather u&iquitously

(and inaccurately associated with any data"up approach – with the i#plication

that if you wanted to ta%e any other %ind of qualitative approach$ software

would not help.?

Boncerns a&out the i#pact of co#puteri>ation on qualitative analysis have

#ost co##only focused around four issues

• the concern that computers can distance researchers from their dataI

• the dominance of code7and7retrieve methods to the eclusion of other analytic activitiesI• the fear that use of a computer !ill mechaniCe analysis, ma/ing it more a/in to quan7

titative or Jpositivist' approachesI and

• the misperception that computers support only grounded theory methodology, or

!orse, create their o!n approach to analysis.

Closeness anddistance

5arly critiques of QDAS suggested that users of software lost closeness to data

through poor screen display$ seg#entation of text$ and loss of context$ there&y

ris%ing alienation fro# their data. Despite enor#ous changes in technology

and in software$ these attitudes persist in so#e co##unities of practice. The

alternative argu#ent is that the co#&ination of full transcripts and software

can give too #uch closeness$ and so users &eco#e caught in 3the coding trap4$

&ogged down in their data$ and una&le to see the larger picture (7il&ert$ ,--,0

ohnston$ ,--I.

Qualitative software was designed on the assu#ption that researchers need

&oth closeness and distance (+ichards$ 99G closeness for fa#iliarity and

appreciation of su&tle differences$ &ut distance for a&straction and synthesis$and the a&ility to switch &etween the two. Bloseness to data – at least as #uch as

can &e had using #anual #ethods – is assisted &y enlarged and i#proved screen

display$ i#proved #anage#ent of and access to #ultiple sources and

?@elle (99


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& 5ualitative data analysis with nvivo

types of data$ rapid retrieval of coded text and easy a&ility to view retrieved

seg#ents of text in their original context. =ther tools are designed to provide

distance$ for exa#ple$ tools for #odelling ideas$ interrogating the data&ase to

generate and test theory$ or su##ari>ing results. These ta%e the researcher

&eyond description to #ore &roadly applica&le understanding. Tac%ing &ac%

and forth &etween the general and the specific$ exploiting &oth insider and

outsider perspectives$ is characteristic of qualitative #ethods and contri&utes to

a sophisticated analysis.

"omination o. code and retrieve as a method

The develop#ent of software for textual data #anage#ent &egan when qualita"

tive researchers discovered the potential for text storage and retrieval offered

&y co#puter technology. 8ence$ early progra#s &eca#e tools for data storageand retrieval rather than tools for data analysis$ &ecause that was what co#put"

ers were &est a&le to do. The few progra#s that went &eyond retrieval to

facilitate as%ing questions a&out the association of categories in the data$ par"

ticularly non"Eoolean associations such as whether two concepts occurred within

a specified level of proxi#ity to each other$ were less rather than #ore co##on$

and in these early stages were given special status as second"generation 3theory"

&uilding4 progra#s (Tesch$ 99-.

Bo#puters re#oved #uch of the drudgery fro# coding (cutting$ la&elling

and filing0 they also re#oved the &oundaries which li#ited paper"&ased #ar%ing

and sorting of text.

Hhen recoding data involves laborious collation of cut7up slips and creation of ne!

hanging folders, there is little temptation to play !ith ideas, and much inducement to

organiCe a tight set of codes into !hich data are shoved !ithout regard to nuance.

Hhen an obediently stupid machine cuts and pastes, it is easier to approach data

!ith curiosity F as/ing J!hat if + cut it this !ayK', /no!ing that changes can be made

quic/ly. =4arshall, # 6D>

Si#ply #a%ing coding #ore efficient was not$ in itself$ a conceptual advancefro# #anual #ethods of data sorting. Briticis# that seg#ents of text were

re#oved fro# the whole$ creating a loss of perspective$ was frequently levelled

at co#puter software (apparently without recognition that cutting up paper did

the sa#e thing$ with even greater ris% of not having identified the source of the

seg#ent. Fears were expressed that co#puters would stifle creativity and

reduce variety as code and retrieve &eca#e the do#inant approach to wor%ing

with data.

'ost pro&le#atically$ the facility for coding led to what 6yn +ichards co#"

#only referred to as 3coding fetishis#4 – a tendency to code to the exclusion of

other analytic and interpretive activities$ which &iases the way qualitative


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5ualitative computing )

research is done$ and which often contri&utes to a report that co#prises only

3the#es fro# the data4. Prior to the develop#ent of co#puter software for cod"

ing$ #ore e#phasis was placed on reading and rereading the text as a whole$

on noting ideas that were generated as one was reading$ on #a%ing lin%s

&etween passages of text$ on reflecting on the text and recording those reflec"

tions in !ournals and #e#os$ and on drawing connections seen in the data in

3doodles4 and #aps. #prove#ents in the #e#oing$ lin%ing$ and #odelling

tools within current qualitative software now provide a#ple capacity for these

approaches to analysis$ allowing the user to stri%e a &alance &etween coding

and reflecting and lin%ing as they wor% with data.

Computers and

mechani3ationFears that the co#puter$ li%e 8A6 in Arthur B. Blar%e4s !pace 8$yssey series$

#ight ta%e over the decisions and start controlling the process of analysis

ste# in part fro# the historical association of co#puters with nu#eric pro"

cessing. Adding to that concern is the co#puter4s capacity to auto#ate

repetitive processes or to produce output without #a%ing o&vious all the

steps in the process.

There are software progra#s designed to auto#ate the coding process

entirely$ using co#plex dictionaries and se#antic rule &oo%s to guide that pro"

cess$ &ut these are specifically designed for quantitative purposes$ and theresults of their coding are generally interpreted through the use of statistics

with #ini#al recourse to the original text. @eyword searches within

7ualitative analysis will al#ost always &e preli#inary to or supple#ental to

interactive coding of the data$ if they are used at all.

Auto#ated coding processes have a place in handling routine tas%s (such

as identifying the spea%ers in a focus group$ or what question was &eing

answered$ in facilitating initial exploration of texts$ or in chec%ing thorough"

ness of coding. These re#ove drudgery without in any way hindering the

creativity or interpretive capacity of the researcher. They do not su&stitute

for interpretive coding that still needs to &e done interactively (live onscreen.

=ne of the goals of this &oo% is to ensure that researchers using )*ivo under"

stand what the software is doing as they #anipulate their data$ and the logic

on which its functions are &ased – !ust as artisans need to understand their

tools. Such #etacognitive awareness ensures researchers re#ain in control of the

processes they are engaging in and are getting the results they thin% they

as%ed for (7il&ert$ ,--,. 'ore aware$ creative$ and adventurous users can

experi#ent with new ways of using )*ivo4s tools to wor% with their data$ !ust as

the good artisan %nows how to #a%e his or her tools 3sing4 to produce a creative

piece of wor%.


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9omogeni3ation o. 5ualitative approaches to analysis

Pri#arily in the early literature on QDAS$ software was tal%ed a&out as if it

pro#oted a narrow view of qualitative #ethodology (Boffey$ 8ol&roo%$ 1

At%inson$ 99I. So#e current scholars express their concern that unguidednovices #ight still view software as having its own #ethod (8utchison$ ohnston$

1 Erec%on$ ,--90 ohnston$ ,--I$ while software experts critique the si#pli"

fied views of software portrayed &y individuals without QDAS expertise

(Barva!al$ ,--,0 7il&ert et al.$ ,-/0 ac%son$ ,--/0 'ac'illan 1 @oenig$ ,--?.

The oversi#plification of qualitative #ethods has occurred and continues to

occur whether software is involved or not. +esearchers tal% a&out 3doing

qualitative4 as if to i#ply there is !ust one general approach to the analysis of

qualitative data. :hile there are so#e generally accepted e#phases$ different

approaches to qualitative analysis are shaped &y differences in foundational

philosophies and understandings of the nature of social reality$ the nature of the

questions &eing as%ed$ and the #ethodological approaches adopted.

+esearchers #ust integrate their chosen perspective and conceptual fra#ewor%

into their choices regarding what tools they will use$ what and how they #ight

code$ and what questions to as% of the data. 4his is the role o" the

researcher

whether or not they use so"tware9

0ploring an NVivo pro/ect

Throughout this &oo% we will &e illustrating the principles and activities &eing

discussed with exa#ples fro# a nu#&er of our own pro!ects$ those underta%en

&y colleagues or students$ pro!ects fro# the literature$ and so#e practice"

infor#ed vignettes. To give you an overview of the tools availa&le for wor%ing

in an )*ivo pro!ect and of what you #ight &e wor%ing towards$ we will start &y

ta%ing a loo% at the sa#ple pro!ect that co#es with the software. Eecause this

is a #oderately #ature pro!ect$ these instructions are not designed to show you

how to #a%e a start on wor%ing in your )*ivo pro!ect$ &ut rather what will

&eco#e possi&le as you progress through your analysis.

As you read these instructions and others in later chapters$ you will

encounter a nu#&er of special icons

� indicates these are steps =actions> for you to follo!.

�

indicates a tip or series of tips F handy hints to help you through.

; indicates a warning F ignore at your perilL

< indicates !here to find this topic or tool in the 9elp files. Access ivo elp by clic/7

ing on the question mar/ near the top right7hand side of the screen !hen ivo is

open. ivo elp also provides a glossary, should you come across unfamiliar terms

=you might also chec/ for these in the inde of this boo/ as it !ill point you to !here

they are described>.


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5ualitative computing 11

n presenting instructions$ we have adopted a nu#&er of conventions

• *ibbon tabs are in bold italic text . Group names !ithin the ribbon are in italic text .

• he three main vie!s in the interface =Navigation, List , and Detail > are in italic text .

• Source names and node names are !ritten in italics.

• Hords that are copied from the screen as part of a clic/ instruction are in #old.

8nstalling theso.tware

f you don4t already have the software on your co#puter$ then your first step

to using )*ivo will &e to install either a fully licensed or a trial version on your

co#puter. These are availa&le through the QS+ we&site www.qsrinternational.

co#.; Cse the free +etting !tarte$ guide to find #ini#u# co#puter require"#ents and detailed instructions for installing the software. Easically$ insert a dis%

or dou&le"clic% the downloaded software and follow the steps as they appear on

screen after launch. t is li%ely that you will &e required$ as part of this process$

to install several supporting progra#s prior to installing )*ivo itself the

installation wi>ard will guide you through the necessary steps.

=nce you have co#pleted the installation$ if you own the software$ or your

institution has a site licence$ you will need to have availa&le the licence nu#&er

that ca#e with your software or is availa&le through your institution. :hether

you are using a licensed version or the /-"day trial (a fully functional version of

the progra# that operates on your co#puter for /- days without a licence %ey$

you will need to activate the software &efore you can &egin to use it.

Activation can &e done via the internet$ or$ if necessary$ &y phone or e#ail. n

addition$ the first ti#e you launch the software after installation$ you will &e

as%ed for your na#e and initials. This pro#pt for the current user occurs once

only$ unless you change the default option to always 3Pro#pt for user on

launch4 (6ile = >ptions = R Prompt .or user on launch?.'ore a&out the potential need to change this default is in Bhapter , on

tea#wor%.

� +n order to /eep using ivo beyond the 07day trial period, you do not need to

uninstall the trial and reinstall the soft!are. All you !ill need is to enter and

activate a ne! licence /ey to etend your eisting version. =)lic/ 6ile = 9elp =

0tend @icense to enter your ne! licence /ey.>

� ?nless you Eust do!nloaded the soft!are from the BS* !ebsite, you might also go to

6ile = 9elp = Chec .or So.tware 4pdates to ensure you have the latest

version on your computer.

� +f you have an earlier version of ivo on your computer, you do not need to remove

it before installing the latest version of ivo. +f, ho!ever, you have more than one

; =ur instructions regarding installation$ user passwords$ etc. pertain to the

standalone version of the software0 if you are using )*ivo Server you should access

the )*ivo 8elp files for alternative installation instructions.


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version of ivo on your system, your computer !ill default to open the most

recent soft!are, even if you launch from a proEect created in an earlier version.

ivo !ill then !al/ you through the steps to convert your older proEect so it can

be used in the ne! version. As a result, you !ill have t!o copies of the same

proEect in t!o different versions of the soft!areI naming them carefully !ill helpavoid confusion later.

; +f you convert a proEect to the ne! version of ivo, you cannot reopen or resave

that copy of the proEect in an earlier version of the soft!are.

< Hhen you first open the soft!are, vie! the video tutorials, accessed via 6ile = 9elp

= NVivo utorials. hese provide a demonstration of the various elements in an

ivo proEect, using data from the Environmental Change sample proEect.

� Alternatively =or as !ell>, !or/ through the instructions belo! as an introduction

to ivo using the Environmental Change data.

he Environmental Change Down East pro/ect

The Environmental Change Down East pro!ect explores the attitudes of

individu" als in / co##unities in an area of )orth Barolina %nown as 3Down

5ast4. The goal of the data collection and analysis was to foster dialogue a#ong

sta%ehold" ers (residents$ land developers$ legislators$ &usiness owners$ etc.

regarding co##unity planning$ land use$ and sustaina&le develop#ent.

Throughout the &oo%$ you will find illustrative exa#ples drawn fro# the Envi

ronmental Change Down East pro!ect (referred to here as the

Environmental Change pro!ect and fro# Pat4s ;esearchers pro!ect. These

sa#ple pro!ects provide #ate" rial on which you can explore the software and

practise using it. The Environmen tal Change pro!ect acco#panies every

licence as an e#&edded sa#ple pro!ect and is installed (&y default in Pu&lic

Docu#entsL)*ivo Sa#ples. t is availa&le also for download fro# the QS+

nternational we&site via a lin% fro# the co#panion we&site for this &oo%. The

;esearchers pro!ect is also availa&le fro# the co#pan" ion we&site. t co#prises

focus groups$ extracts fro# interviews$ and so#e other sources designed to help

answer the questions of what &rings people to engage in research$ and what it

is a&out their experience that %eeps the# researching.

>pen a pro/ect

:hen you dou&le"clic% on the )*ivo icon to open the software$ )*ivo opens to

the :elco#e window$ with options (at the left of the window to create a new

pro!ect or to open an existing pro!ect. The 7y Recent Pro/ects list

contains the five #ost recently opened pro!ects on your co#puter. f you want to

open a pro!ect that is on your co#puter$ &ut not on the list$ you will need to clic%

on >pen Pro/ect and then navigate to locate the pro!ect. =pening a pro!ect

ta%es you into the pro!ect wor%space fro# which you can access all the software

tools.

�

&pen the Environmental Change proEect by clic/ing on its title in the Helcome

screen, or , if it isn't listed, go to >pen Pro/ect and loo/ for it in the ivo

Samples folder in your $ublic ocuments library.


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5ualitative computing 1!

he pro/ectworspace

Figure . illustrates the wor%space and its co#ponents for the EnvironmentalChange pro!ect.

A ribbon with nine standard ta&s (for#erly %nown as #enus spans the top

hori>ontal position in )*ivo (e.g.$ Home$ Create$ External Data$ with

sup" ple#entary ta&s that open when the researcher is active in #edia$

#odelling$ or any other special purpose tool. te#s availa&le in the ri&&on can

&e availa&le or greyed out$ depending on which part of a pro!ect is active.

:ithin each ta& of the ri&&on$ groups help to organi>e the #any options. For

instance$ if you select the Home ta&$ you will find group na#es in grey text

along the &otto# of the ri&&on$ including .orkspace$


40/404


n each of these views$ a context"sensitive #enu can &e accessed &y right"clic%ing.

� Hhenever you're not sure !hat to do or !here to loo/ for an action !hen you are

!or/ing in ivo, ensure your mouse pointer is pointing directly to the relevant item

on your screen, and try right7clic/ing as a first option to find !hat you !ant. *ight7clic/

options change, depending on !hat you are pointing to.

As you explore )*ivo using the Environmental Change pro!ect$ you will

gain so#e appreciation of how )*ivo can assist with organi>ing and analysing

your data. Sources are neatly filed0 cases are identified with de#ographic and

other details0 ideas are recorded and appropriately lin%ed to their sources0

descriptive #aterial and evidence for e#erging understanding and ideas are

captured using codes0 codes are organi>ed to facilitate querying the data so that

research ques" tions #ight &e clarified$ developed and answered0 and for thosewho li%e to wor% visually$ e#erging understanding can &e explored in #odels

and charts.

0plore sources in NVivo

he !or/space !ill first open to sho! Sources in the Navigation View , and !ill

default to the 8nternals folder. here are several types of +nternals stored in the

Environmental Change proEect# these are organiCed into subfolders designed toassist !ith data management. he follo!ing activities sho! you ho! to open and

loo/ at the materials, but it is simply a gentle tour. +nstructions are provided later in

the boo/ regarding the steps needed to import, edit, code, and lin/ your sources.

View an internal document Bthe pro/ect description?

� he top7level folder for Internals is already selected. +n List View , double7

clic/ on Overview of Sample Project to open it in Detail View . *ead this for

additional detail about the sample data.

�

ote the use of heading styles in this =and other> sources. he level of heading is indicated in the Home ribbon, Styles group. eading styles can

be added or changed at different levels.

�

)lic/ on the first line of the document. (ou !ill see this is in eading 1 style.

� )lic/ on the line that says Intro!ction and you !ill see this is in eading style.

� eadings brea/ the tet into parts. +f you are unfamiliar !ith headings, you

can learn more about them in 4icrosoft Hord or ivo elp files. (ou can as/

ivo to code across =or !ithin> your sources to collect all the data mar/ed by

a par7 ticular heading level.

View a pro/ect interview recorded as video

� +n Navigation View , epand the 8nternals folder =clic/ on the > to see

further folders for various document sources =e.g., Area and o!nship,

+ntervie!s, and e!s Articles>.


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5ualitative computing 1'

� o see the list of proEect intervie!s, select Sources = 8nternals =8nterviews.

�

+n List View , select any intervie! and double7clic/ to open it in Detail View .

�

6ouble7clic/ on "etty an Pa!l to see a video record of an intervie!.

� "rom the Media ribbon, you can select Play to hear and !atch the video.)lic/

Stop after you loo/ at a sample of the file.

� o close "etty an Pa!l =and any other intervie!s you opened> clic/ on the ×

net to their names in the tab.

View an internal dataset

A dataset is a table that holds the /ind of information you !ould generate from a

structured survey !ith both open and closed questions.

� +n Navigation View , select Sources = 8nternals = Survey.

� +n List View# double7clic/ on S!rvey $esponses.

�

his dataset !as imported from an cel file. As you scroll across it, you !ill

see it contains some columns !ith nominal, ordinal or interval data, and

several columns !ith open7ended responses.

� ?se the tab on the right margin of the Detail View to see the data in 6orm

vie! rather than a#le vie!.

ivo allo!s you to automatically code much of the information in a dataset. (ou

can then, additionally, interactively code the detail !ithin people's open7ended

responses.

View an internal picture

� +n Navigation View , select Sources = 8nternals = -rea and ownship.

�

+n List View , double7clic/ on Competing water !ses%

�

o the right of the picture, clic/ on the number 1 net to the first ro! of tet

to illuminate the part of the image associated !ith this observation.

�

+f you need to enlarge the image in the Detail View , try using the Coom toolat the bottom right7hand side of the screen =!ithin the ivo !indo!>.

�

+f you need more room, go to View ribbon, &inow group and

unchec/ "oced. Hait, and the Detail View !ill open in a ne! !indo!. his is

especially helpful if you have t!o monitors =you can vie! your proEect on one

screen, and !hatever is in Detail View on the other screen>.

View social media data

� +n Navigation View , select Sources = 8nternals = Social 7edia.

�

+n List View , double7clic/ on Cartaret Co!nty on 'witter .

his dataset !as collected !ith )apture. )apture is a bro!ser etension for

+nternet plorer or Google )hrome that accompanies ivo. +t is designed to

(Contin!e)


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ontinue!

$tu%e #t# %o' #"e)oo*+ Lin*eIn+ YouTu)e #n T,itte% #n "on-e%t t.e/e #t# /ou%"e/ o% u/e in NVi-o0

osing items in "etail ViewTo "1o/e #n2 ite' 2ou .#-e o$ene u%in3 t.e tou% /o #%+ "1i"* on t.e ne4t to t.e n#'e o t.e ite' in t.e D

ace the lins .rom internal sources2ou %e# t.%ou3. t.e O-e%-ie, o"u'ent+ noti"e /o'e o t.e te4t i/ "o1ou%e o% .i3.1i3.te (i3u%e 506!0 T.e

Retu%n to N#-i3#tion Vie, = Sources = 8nternals+ t.en to Li/t Vie, #n ou)1e7 "1i"* on t.e O-e%-ie, o S#'

gure 1A2 Vie,in3 .2$e%1in*/+ 9/ee #1/o: 1in*/+ #n #nnot#tion/ %o' #n inte%n#1 /ou%"e

yperlins to e0ternal items

In t.e ;%/t $#%#3%#$. o t.e O-e%-ie,+ )1ue une%1ine te4t ini"#te/ t.e $%e/7 en"e o # hyperlin to #

1% 5ualitative data analysis with nvivo


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5ualitative computing 1$

See also lins to internal items

+n the fourth paragraph of the Overview , a pin/ highlight indicates that a see also

link has been created.6

See also lin/s ta/e you to other items =tet, images, video,models> or to portions of internals that relate to the mar/ed tet.

�

Go to the View ribbon, Lin*s group = R See -lso @ins. he lin/editems become visible at the bottom of the screen. +n this instance, three aerial

photo7 graphs of the region have been lin/ed to the tet.

�

)lic/ on the pin/ highlight, then double7clic/ on the associated number to

open the lin/ed item.

-nnotations on te0t

+n the seventh paragraph, blue highlighting indicates an annotation. Annotations

are comments, reminders, or reflections on the tet.

� Go to the View ribbon, Lin*s group = R -nnotationsA he lin/edannotation is no! visible at the bottom of the screen.

� )lic/ on the blue highlight and the associated comment !ill be highlighted.

� )lic/ on the number net to an annotation at the bottom of the screen, and

the related passage !ill turn from a light blue to a dar/er teal.

� )lose Overview of Sample Project and any associated items in Detail View .

@ined memos

otes and thoughts related to a document =or node> are recorded in its lin/ed memo.

� *eturn to Navigation View = Sources = 8nternals = 8nterviews.

he names of the people !ho !ere intervie!ed !ill sho! in List View . An

icon to the right of the document name in List View indicates that the document

has a lin/ed memo =e.g., for +en in the Interviews folder>.

�

over =hold the mouse pointer> over the document name, right7clic/ andselect

7emo @in = >pen @ined 7emo, or use CtrlShi.t7 on your

/eyboard.here are further memos stored in the 7emos folder under Navigation View = Sources.

0plore nodes and coding

Nodes provide the storage areas in ivo for references to coded tet. ach node

serves as a container for everything that is /no!n about one particular concept or

category. odes can be used also as a tool to organiCe qualitative data in particular

!ays, to be discussed later.

(Contin!e)

I :e recogni>e$ &ut have learned to live with$ the aw%ward gra##atical construction

3see also lin%s4 creates. :e hope you will &e a&le to as wellM


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ontinue!

odes .or concepts and categories coded .rom the data

In N#-i3#tion Vie,+ "1i"* on Nodes0 T.e Li/t Vie, ,i11 o$en to i/$1#2 noe/ /to%e #t t.e to$ 1e-e1 in t.eIn Li/t Vie, "1i"* on t.e ne4t to one o t.e/e to$71e-e1 noe/ to e4$#n it to /.o, t.e /u)noe/ )e1o,0Dou)1e7"1i"* on # /u)noe to /ee t.e "oe #t# in t.e Det#i1 Vie, )e1o, (i3u%e 50>!0 T.e /ou%"e o e#".Vie, t.e "onte4t %o' ,.i". # /e1e"te $#//#3e "#'e u/in3 t.e %i3.t7"1i"* 'enu? RightDclic = CodinC%e#te # ne, noe )2 /e1e"tin3 /o'e te4t %o' # /ou%"e o% #n e4i/tin3 noe?ghtDclic = Code Selection = Code Selection -t New Node BCtrl6!? +

gure 1A! Noe/+ ,it. %ee%en"e te4t #n "onte4t 'enu+ #n /.o,in3 %et%ie-e (B%o#! "onte4t o% C.#%1e/:

1& 5ualitative data analysis with nvivo


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5ualitative computing 1)

name the ne! node and clic/ >G . (our ne! node !ill sho! at the top level of

the node hierarchy =double7clic/ on your ne! node to see the passage you

coded>.

racing lins .rom nodes

� ote that the node Comm!nity,Connection to Down East,Local ientity has

a memo symbol net to it. RightDclic = 7emo @in = >pen lined

memo =or CtrlShi.t7> to see the notes made about the !ay people

tal/ed about local identity.

�

ote also that many of the notes are shaded pin/, indicating the presence of

see also lin/s. +f you clic/ on the View ribbon, Lin*s group = See -lso

@ins, you !ill be able to read the specific passages that prompted theresearchers' comments.

Nodes to organi3e and manage data

+n Navigation View , the subfolders under Nodes organiCe and manage data.

hese include Places an People !here all data for each individual location and for

each separate person in the proEect are stored, and -!tocoe $esponses for

every7 one's responses to each question as/ed.

�

"rom the Navigation View , select Nodes = Places and People, andfrom the List View , select 8nterview Participants to see the list of

people !ho !ere intervie!ed. ouble7clic/ on "ar.ara and see all the

qualitative data she contrib7 uted to the proEect. +f 2arbara !as intervie!ed

t!ice, you !ould first see the content of 2arbara's initial intervie!, and then

the contents of her second inter7 vie! as you scrolled do!n the page.

� Hhile in the Detail View for this case, go to the View ribbon and choose

Coding Stripes = Nodes 7ost Coding to see the nodes coded

most often for this case.

� over over the coding density bar =the vertical stripe !ith segments in

different shades of grey> to see a list of nodes coding the adEacent tet

="igure 1.->.

he heading styles used in the intervie! transcripts made it possible to auto code

everyone's responses to the structured questions as/ed in these intervie!s.

� Select Nodes = -utocoded Responses, and in List View ,

e0pand -utocoded 8nterview Questions.

� ouble7clic/ on /%0% Connection to Down East . All of the echanges

in response to Buestion 1, throughout the intervie! data, have beengathered here, based on auto coding the questions.

(Contin!e)


46/404

ontinue!

gure 1A Coin3 en/it2 #n "oin3 /t%i$e/ /.o,in3 noe/ "oe on B#%)#%#:/ te4t

0plore classi*cations and attri#utes

%ti"i$#nt/ .#-e attri#ute values+ t.#t i/+ # %e"o% o e'o3%#$.i" #n @u#ntit#7 ti-e #t# *no,n #)out t.e'

Vie, t.e #tt%i)ute -#1ue/ 1in*e to # $#%ti"i$#nt )2 3oin3 to t.e N#-i3#tion Vie, /e1e"tin3 Nodes = Places and People0n Li/t Vie,+ e4$#n 8nterview Participants = B#%)#%#0 RightDclic = Node Properties (o% CtrlShi.t

assi*cations .e1$ to o%3#nie t.e /t%u"tu%e o 2ou% #tt%i)ute/ #n -#1ue/0 You "#n .#-e ie%ent t2$e/ o "#/e/+ #n N#-i3#tion Vie,+ 3o to Classi*cations = Node Classi*cations0 You ,i11 /ee #t# o%3#nie in t,o ,#2/? )E4$#n Person to /ee # 1i/t o #tt%i)ute/ %e1e-#nt to $#%ti"i$#nt/0 Dou)1e7"1i"* on #n #tt%i)ute (e030+ ownsh



47/404

5ualitative computing 21

Attribute values for all participants can be vie!ed =and modified> in the

Classifcation Sheet . hey can be entered one at a time, or they can be

imported from a spreadsheet or created from a dataset.

�

Select Person, RightDclic = >pen Classi*cation Sheet. o change a

value in any cell, choose from the drop7do!n list for that cell.

View sets Bin Collections?

Sets in ivo contain shortcuts to any nodes and3or any documents, as a !ay of

holding those items together !ithout actually combining =merging> their content.

hey are used primarily as a !ay of gathering items for use in handling queries,

reports or models, or simply as a !ay of indicating that these items Jhang together'

in some !ay =perhaps conceptually or theoretically>.

�

+n Navigation View# select Collections to see a list of Sets. )lic/ on Noes

for coing comparison to see the items in the set sho!n in List View .D

Review 5ueries

Qeries store questions you !ant to as/ of your data. Bueries might be

about the occurrence of a !ord or !ords, about patterns of coding, comparison

of groups, or some combination of these elements. hey can be created and

run once, or stored to use again !ith more data, or !ith a variation. !eslts

hold the data found by the query to help you ans!er your questionsI they canbe stored alongside the queries.

� +n Navigation View , select Queries.

� +n List View , RightDclic on &or 1re2!ency /!ery in interviews =

Query Properties to see ho! a simple !ord frequency query !as set up.

� )lic/ on Run. he results !ill open in Detail View .

� )lic/ on the tabs at the very right7hand margin of your screen =Summary,

ag Cloud, ree 7ap, Cluster -nalysis> to see different !ays of vie!ing

these results.

�

ouble7clic/ a !ord from the Summary tab to see all instances of this!ord found by the query.

� o! loo/ at a more comple query. +n List View , RightDclic on -ttit!e

a.o!t environment .y longevity Down East = Query Properties to see

ho! it !as set up, and clic/ on Run. he results !ill appear as a matri !ith

counts of pas7 sages coded =coding references> in each cell. ouble7clic/ a

cell to see the tet. %ater you !ill learn ho! to obtain other /inds of

information from the matri result.

(Contin!e)

< n this pro!ect$ sets were used in a very li#ited way only.


48/404

ontinue!

hec some prede*ned reports

eports $%o-ie/ $%ee;ne out$ut t2$e/0 It i/ #1/o $o//i)1e to "u/to'ie # %e$o%t0 =#n2 o t.e %e$o%t/ $%o-ien N#-i3#tion Vie,+ /e1e"t Reports0n Li/t Vie,+ ou)1e7"1i"* on Node Summary Report0C.e"* ne4t to Node 9ierarchical Name (i3u%e 50!+ t.en Select = Ealance = >G = >G 0e %e$o%t ,i11 /.o, -#%iou/ /t#ti/ti"/ #)out .o, oten t.e noe ,#/ u/e to "oe te4t0

n Li/t Vie, ou)1e7"1i"* on Coding Summary #y Node Report0C.e"* t.e ;%/t ;1te% o$tion+ Select = Ealance = >G = >G 0 T.e %e$o%t ,i11 "ont#in #11 te4t "oe #t t.#t noe0

gure 1A' i1te% o$tion/ o% # %e$o%t

0plore models and visuali3ations ;n#112 (o% no,!+ models and visuali3ations i/$1#2 ie#/ #)out t.e %e1#tion/.i$/ )et,een $%o8e"t ite'/0

ew a model

n N#-i3#tion Vie,+ /e1e"t 7odelsA Dou)1e7"1i"* on (eographic units used in this pro/ect0To o)t#in # u11e% -ie, o t.e 'oe1 on # /e$#%#te /"%een+ 3o to t.e View %i))on #n un".e"* "oced+ o% u/e

0periment with visuali3ations

o )#"* to Nodes in N#-i3#tion Vie,0 C1i"* #n %#3 to /e1e"t #11 t.e noe/ une% N#tu%#1 en-i%on'en



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5ualitative computing 2!

>verview: whatHs in an NVivo

pro/ect<

An )*ivo pro!ect typically co#prises

• data records =e.g., transcriptions, field notes, other documents, video, audio, photo7

graphs, !eb pages>I

• records of your thin/ing about the data =memos>I

• nodes to store coded references to your data =so you can retrieve all you /no! about

a topic, idea, case or relationship>I

• variable7type information =attribute values> relating to sources or cases in your study

=e.g., demographic details, responses to categorical or scaled questions, dates>I

• records of and results from interrogative queries conducted on your dataI and

• models sho!ing relationships bet!een items in your proEect.

All of these are held in a single data&ase"style file$ which$ if the file location

options have not &een changed$ will &e located in the "ocuments area of your

co#puter.

he visual sho!s !hich nodes are most similar based on !ords used in the

coded tet. )hange the number of clusters to -, and eperiment !ith other

!ays of visu7 aliCing this information from the options in the Clster

"nal#sis ribbon.

Save changes

Hhile you !ere loo/ing at the sample proEect, you !ere !arned that it !as

15 minutes since the proEect !as last saved, and as/ed if you !anted to save

changes made to the proEect. his is ivo's !ay of ma/ing sure you are regularly

saving changes to your proEect, in case of po!er failure or freeCing. Hhen you are

!or/ing on your o!n proEect, it is strongly recommended that you save each time

you are as/ed, unless you are simply eperimenting, do not !ant to save your

changes, or you are in the middle of an ?ndo operation.

Close the proEect by selecting 6ile = Close or, if you !ant to quit !or/ing in


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6

Starting out, with aview ahead

So#ething in your social or wor%ing environ#ent excites interest$ and investi"gation &egins. Jou #ight start in the li&rary or &y o&serving 3the field4$ perhaps

with so#e exploratory discussions with relevant people or with reflection on

personal experience. +ight fro# the start$ you will find tools in )*ivo that will

support your wor% as you explore possi&ilities$ refine questions$ and thin%

through pro!ect design.

The tools you use and the ha&its you develop early will &eco#e integral to

your wor% throughout your pro!ect. Analysis is as #uch a&out reflecting on

data and #a%ing connections across data as it is a&out categori>ing and

#anipulat" ing data in codes. Jou will find$ therefore$ that !ournals$ #e#os andlin%s will &eco#e essential to the quality of your analysis.

:0ploring the research terrain

A research pro!ect &egins well &efore you gather data. Thought and planning

at this stage will do #uch to ensure a s#oother process of data collection and

a deeper and #ore #eaningful interpretation of those data.

2

8n this chapter:

• St#%t to %#'e 2ou% %e/e#%". $%o8e"t0

• St#%t ,o%*in3 in t.e /ot,#%e? "%e#te # $%o8e"t ,it. # 'oe1+ 'e'o+ #n/ou%"e o"u'ent0

• C%e#te 1in*/ in t.e #t#)#/e u/in3 #nnot#tion/+ /ee #1/o 1in*/ #n.2$e%1in*/0

• Di/"o-e% ,#2/ in ,.i". t.e/e too1/ 'i3.t )e e4tene to )ui1 # ,e) o


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starting out 2'

"evelop 5uestions

Qualitative research often &egins with a vaguely defined question or goal. t

#ay well &egin 3with a &it of interesting NdataO4 (Seale$ 7o&o$ 7u&riu#$ 1

Silver#an$ ,--? 9. *isuali>ation techniques (concept #aps and thoughtexperi#ents can help to clarify what #ight &e useful questions (Ea>eley$ ,-/0

'axwell$ ,-/. Jour initial explorations serve to refine your question$ so #ore

deli&erate (3purposive4 data gathering can occur. These explorations &eco#e

part of your data$ and can &e #anaged within )*ivo.

+ecord these starting questions as you set out. n )*ivo$ you create a

research !ournal to record the#. They will help you to #aintain focus as you

wor%$ and then later to evaluate the direction you are ta%ing. @eep notes a&out

any rando# (or less rando# thoughts you have around those questions as you

read$ discuss$ o&serve$ or si#ply refl

Documents

Book - Qualitative Data Analysis With NVIVO