Setting Up a Qualitative or Mixed Methods Research Project in NVivo 10 to Code and Analyze Data

Innovative Media andToolsTrack
7th Annual International Symposium
EmergingTechnologies for Online Learning
(sponsored by Sloan-C and MERLOT)
Shalin Hai-Jew
April 2014

NVivo 10 is a widely used qualitative and mixed methods data analysis research tool used in
many domain fields. This presentation will showcase some of the capabilities of this software
by reviewing how to start a project in NVivo, how to manage complex data, how to begin
coding raw information, and some of the methods for making meaning “in vivo” or “within the
living” with various analyses and data visualizations. This presentation will include a
demonstration of NCapture, a feature for data extraction from some social media platform
APIs. For context, this session assumes some basic prior training in qualitative research, such as
ethnographies, surveys, interviews, qualitative meta-analyses, portraiture, grounded theory,
action research, Delphi studies, and / or others. The goals of this presentation are to
 introduce NVivo 10 as a data management and analysis tool for qualitative and mixed
methods research
 introduce the concept of multimodal data (from various types of multimedia sources)
 show data ingestion into the system
 establish how data may be coded for research value
 show how data structures may be created from the coded data and then analyzed
 demonstrate the extraction of a social media platform to open up another data channel for
research
Setting Up a Qualitative or Mixed Methods Research Project in NVivo 10 to Code and Analyze Data 2

 Pre-story
 Types of qualitative and mixed methods
research
 General timeline of the research process
 Quality standards
 Ingestion of data
 Importation of data sets
 File types: multimedia
 NVivo 10 (an overview)

 Data coding
 Folder and node data structures
 Codebooks
 Manual coding
 Two types of auto-coding
 Flexible coding structures
 Coding for matrix analysis
 Human-machine analysis of data

 Data coding (cont.)
 Visual models
 Text search query
▪ Text search query and resulting word tree
 Manual research journaling
 Automated ‘event log’ journaling
 NCapture
 Essential mechanics
 My wants re: the software
 Contact and conclusion
5Setting Up a Qualitative or Mixed Methods Research Project in NVivo 10 to Code and Analyze Data

 Multi-year site license at Kansas State University
 Sociology, anthropology, social work, education,
communications studies, mass media, business,
veterinary sciences, food sciences, family studies,
human ecology, and others; university research office;
multi-campus support
▪ Installed in student labs in multiple departments
 Available to faculty, staff, administrators, and
graduate students through protected website (with
software download and a university-wide license key)
 Y1 webinar trainings

 Grounded theory
 Heuristics / methods of
discovery
 Hermeneutics
 Life history
 Participatory action
research
 Phenomenology
 Ethnography
 Standard causal analysis
(SCA)
 Narratology; serious
narration; storytelling;
historical narration
 Social experimentation
 Formalization; analysis
of “stylized facts”
 Literary critique
 Market research
 Interviews
 Focus groups
 Surveys
 Delphi study; modified e-
Delphi study
 Actor-network analysis;
electronic social network
analysis; social network
analysis, and others

 Mixed methods / multimethodology:
qualitative research methods with
quantitative research elements

QUALITATIVE METHODS
 Relativism (with attendant
theories, tools, and research
practices); post-structuralism
and post-modernism
 Non-measurability of
phenomena
 Narration
 Culturalism
 Emergentism
 Contextualization; social
constraints
QUANTITATIVE METHODS
 Positivism (with attendant
theories, tools, and
research practices)
 Measurability of
phenomena
 Analysis
 Behavioralism
 Individualism
 Non-contextualization;
independent agentry; open
choices

QUALITATIVE METHODS
 Situated knowledge
 Subjectivity
 Constructionism
 Experiential and detailed
 Low n, case-based
 in vivo: “within the living”
(studying empirically in-
world) (in Latin)
 Transcendent or universal
knowledge
 Objectivity
 Realism
 Statistical and numerical
 “Statistical significance” /
relatively high n (and even
“big data”)
 in vitro: “within the glass”
(experimental model) (in
Latin)

QUALITATIVE METHODS
 Rigor, saturation,
immersion, triangulation
 Criticality of identification
(and methodological
mitigation) of researcher
biases
 Centrality of language
 Unclear end point
 Rejection of the null
hypothesis
 Criticality of nullifying
researcher biases through
strict methodologies
 Centrality of numbers
 Generally defined end
point

 Single approach designs, with augmentation
of either qualitative or quantitative strategies
to enhance research richness and outcomes
(with a predominant qualitative or
quantitative approach)
 Mixed approach designs, to enhance
construct validity for multiple analytical
interests

 General steps:
1. Initial secondary and primary research
2. Immersive research and documentation
3. Coding and annotation
4. Data analysis
5. Write-up
6. Dissemination
 Flexible semi-recursive process

 Various quality standards at every step (depending
also on ontology and research methods)
 Protection of human participants (informed consent;
protection from harm; voluntary participation throughout;
anonymity and confidentiality; beneficence)
 Data saturation (comprehensive literature review)
 Data triangulation (multi-sourcing of data)
 Clear definitions of terms
 Address of researcher bias
 Inter-rater reliability; rater norming to standards
 Transparency in research methods

 Internal logic and non-contradiction; plausibility
 Falsifiability of hypotheses and assertions
 Logical analysis
 Bounded and delimited assertions
 Sufficient detail and depth
 Consistent and appropriate unit(s) of analyses
 Explanatory power
 Clear language
 Intellectual elegance and originality

 Puzzle solving
 Problem-solving
 Informative for decision-making
 Practical applications (decision-making; policies)
 Data protection against leakage and compromise

PRIMARY
 Interviews
 Surveys (.xls, electronic
platform files)
 Focus groups
 Cases
 Observations
 Oral histories
 Text, photos, videos, and
audio files
 Social media platform
contents (NCapture)
SECONDARY
 Literature reviews
 YouTube videos (links only)
 Web pages
 Social media contents
 Grey literature

 AUTOMATED (data import wizard, third-
party tools like Survey Monkey)
 From electronic survey sites and tools
▪ Cells whited out for open-ended questions; grayed-out
cells for fixed response (attribution details)
 From social media platforms and sites (NCapture
in IE and Chrome and extracted into NVivo)
 From content providers
 From Excel (codebooks); SPSS; SAS, etc.
 From bibliographic data managers / citation tools

 MANUAL (drag-and-drop)
 From online libraries and repositories
 From Excel

 Text files (.docx, .doc, .rtf, .txt, .pdf, and others)
 Slideshows (.pptx, .ppt)
 Datasets (.xls, .rtf, .txt, .doc, and others)
 Websites captured as .pdfs (using NCapture or
other tools)
 Video files (and link-outs to video files on
content-sharing platforms)
 Audio files (and link-outs to audio files)
 Image files (virtually all digital image files)
 and others…

 Requires manual transcription of audio and
video files
 Requires annotation
 Annotation of audio
 Annotation of video stills and video segments

An overview

 Formerly NUDIST (non-numerical
unstructured data indexing, searching, and
theorizing)
 Runs on PCs and parallels / virtual machines
on Macs; professional Mac version
forthcoming June 2014; mobile app access in
development
 Includes add-ins that enable extraction of
social media platform data
 Created by QSR International

 Tool as theory and research methodology “agnostic”
and flexible
 May be a longitudinal many-year project or a short-term
one
 May be individual researcher-based or team-based
(without requirement for co-location, even with non-
server based licensure because of data integration
options)
 Live support webinars with responsive presenters (for
both broad audiences and organizations with site
licenses, at additional cost)
 Pre-recorded help webinars
 Textual help supports well documented

 Project files saved as .nvp (QSR
NVivo Projects) files
 Back up to server, cloud service,
external hard drive, or other device
beyond main work device
▪ Coding is time-sensitive and
irrecoverable and impossible to
recreate if there is data loss
 May hover over file names at
bottom left of opening screen to
see the whole path of the files

A PRIORI DATA STRUCTURE
 Pre-defined with general
constructs, data
categories, and ideas
(based on deductive logic
and prior research)
 A priori and emergent data
structure
EMERGENT DATA STRUCTURE
 Initially undefined
structures with information
extracted from the
research documentation
and researcher journal and
other sources (based on
inductive logic and
interpretation of collected
information); aka
freecoding
MIXED DATA STRUCTURE

 Units of analysis
 Data manageability
 Relevance
 Ways to flexibly query the data using the
software
 Semantics and terminology
 Should document and describe all nodes for
coding clarity (particularly if there are teams
working together on one project); codebook
definitions, too

 Definitions of codes and their meanings (often
with examples)
 May be static (and predefined, partially defined based
on hypothesis and secondary research)
 May be evolving (and emergent and recorded in
codebooks along the way…but initially unstructured)
 Digital or on paper
 May be used with one coder or a group
 Inter-rater reliability (Kappa coefficient in coding
comparisons between coders) and norming

 Categories or classifications as nodes
 Textual / auditory / visual (still) / video
information highlighted and marked as
belonging to a certain node
 Highlighting and dragging text (of text or .pdf
OCR-text) to node or right clicking and coding to
node
 Highlighting an area (of image or sound or video)
and dragging to node or right clicking and coding
to node

“STYLES” IN WORD
 Ingestion and
simultaneous autocoding
of MSWord files with
questions coded as
headings and answers as
paragraph styles (styles
used to code a mass of
data)
ALGORITHM-BASED
 Machine-based emulation
of qualitative / mixed
methods researcher
highlighting of contents
into certain nodes
 Machine-coded
information clearly
identified as such (with
coding easily reversible)

 Nodes used for
 data structuring (and data visualizations)
 data clustering for analysis (for patterns and
researcher sense-making and understanding)
 further data querying
 pattern identification
 Folders and nodes may be re-named, moved,
annotated, regrouped, and changed at any time
 Coding stripes to indicate density of coding and
identify patterns
 Can always uncode, revise, move nodes around,
merge nodes, and even delete them

 May export to Microsoft Excel for larger
range of data visualizations
Variable Variable Variable Variable
Individual
or Group
(based on
attributes)
”
Variable Variable Variable Variable
Variable
Variable

 Matrix analysis enabling ability to slice and
dice data for findings and insights
 Within and between-group analysis
 Enabling comparisons of variables from
categories as rows and columns
 Enabling simple cell counts for comparisons,
anomaly detection, others (if relevant)

 Code…
 Participants / interview subjects as own nodes
 Participants with demographic data (in attributes
and categorizing / classifying data fields such as
age group, sex, and others)
 Participants with close-ended query responses as
attributes (such as Likert scale responses,Y/N
responses)
 Questions as own nodes (variables)
 Matrix analyses results saved in new node(s)

 Text frequency count (with a built-in
and modifiable stop-words list)
 Word clouds (word size and placement
centrality = frequency)
 Treemap (size = frequency)
 Cluster analysis (branch = co-
appearance)
39

 Human observations and readings /
interpretations of the research
 Text searches
 Text frequency searches
 Machine-enhanced data queries
 Data visualizations and structures

 Node hierarchies in any direction (parent,
child, etc.)
 Types of layouts
 Circular
 Directed
 Hierarchical
 Orthogonal
 Revealing of relationships between entities
 More effective with sparse contents

 Words used in a group with “and” to indicate
co-location in the same document (not in the
same sentence or near-proximity)
 Data management
 Find a document or identify an author
 Identify information for use in a quote
 Use “summary” view to look at types of
sources

 Enables intuitions and leads; provides a gist of
the data
 May click over a part of the branch to get its full
phrasing
 May request broader captures (than the narrow
5 words on either side of the focus term), termed
“spread coding”
 May put cursor over phrases in a tree to see the
source (which is linked and may be accessed for
further exploration)
 Words in red are ones in initial text search query

 Via built-in memo and Memo Links
 Via unique folder or node (and variety of
multimedia files)
46

 Apply real-time event logging to project

 May highlight text for annotation
(highlighted in blue and viewable from
various approaches)
 Analyze -> Annotations -> New Annotations
 Limit annotations because too much blue
highlighting will be ignored
 If too many annotations are needed, that
may indicate the need for a coding node
instead

 Per Dr. Cynthia Jacobs (of QSR International)
 Keep a memo for justifying methods for the coding
 Set up a node for “I am struggling with how to code
this” issues for later reference
 Maintain a node for “good quotes”
 Keep a “to do” node list
 Do not delete nodes which you don’t plan to use, but
save them in a folder for possible future reference if
needed
 Use a “left off” node to remember where the coding
was stopped, so you can find your place later

An add-on to GoogleChrome or Internet Explorer browsers

 Websites (with or without ads)…as .pdfs
 Twitter microblogs
 Facebook text and images
 LinkedIn text and images
 YouTube links
 May require log-ins on the various platforms to
access the social media platform APIs (application
programming interfaces)
 A sociometric online ethnography

 Collection
 Coding
 Analysis
 Output

 Collection of data in folder and file structures
 Manual coding and analysis of data
electronically (or on paper) (cognitively taxing
no matter what form)
 Wordle (for word clouds), Freemind (spatial
outlining), and others
 Notetaking in MSWord
 Manual modeling in MSVisio
 Outputs for dissemination in MS Office Suite
and other tools

 Wizarding and templating: Clearer setup for
different types of qualitative and mixed
methods research
 Quant analysis features: A wider range of basic
quant and statistical analysis packages for more
of an all-in-one feel
 A friendlier learning curve: Simpler semantics;
more aggressive and interventionist help
supports
 A built-in source bibliographic data manager /
citation tool (or an integrated freeware one):
Not just EndNote, which is a commercial tool

 A public repository for NVivo data sets (to be
released with formal publications)
 A repository of research journals linked to NVivo-
managed data sets and archived research
 Agreements to enable the release of copyrighted
materials in NVivo-based data sets (for this
limited purpose)

 Dr. Shalin Hai-Jew, Instructional Designer
 InformationTechnology Assistance Center (iTAC)
 212 Hale Library
 Kansas State University
 Manhattan, KS
 785-532-5262
 shalin@k-state.edu
 The presenter has no tie with QSR
International.

Setting Up a Qualitative or Mixed Methods Research Project in NVivo 10 to Code and Analyze Data

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Setting Up a Qualitative or Mixed Methods Research Project in NVivo 10 to Code and Analyze Data