using software for qualitative analysis
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Ixchel M. Faniel, Ph.D.
Associate Research Scientist, OCLC Research
March 3, 2014
SCELC Research Workshop Day 2014
Loyola Marymount University, Los Angeles, CA
Using Software for Qualitative
Analysis
Agenda
• Introductions
• Qualitative Analysis
• First Cycle Coding
• Second Cycle Coding
• Additional Thoughts About Coding
• Qualitative Software
2
Qualitative Research
• Focus on observing
events from the
perspective of those
involved
• Understand why
individuals behave as
they do
3
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Qualitative Research Methods
• Observation
• Survey
• Interviews
– Focus Group
– Individual
• Documents
– Diaries
– Journals
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Analysis (n.): summary of
observations or data in such a
manner that they provide answers
to the hypothesis or research
questions
(Connaway & Powell, 2010, p. 262)
(Silipigni Connaway & Powell 2010)
Content Analysis
• Uses set of procedures to
make inferences from text
• Premise is that many
words can be reduced
and organized into
categories where words
or word units share the
same meaning
6
Microsoft Clipart Image
(Silipigni Connaway & Powell 2010)
The Role Coding
Data
Categories
Themes “…coding is the transitional
process between data
collection and more
extensive data analysis.”
(Saldaña, p. 4).
7
8
“A code in qualitative inquiry is most often a
word or short phrase that symbolically assigns
a summative, salient, essence-capturing,
and/or evocative attribute for a portion of
language-based or visual data” (Saldaña 2009,
p. 3).
Coding Cycles
First Cycle
• Initial coding and
recoding of data
Second Cycle
• classifying, prioritizing,
integrating, abstracting,
synthesizing,
conceptualizing, and
theory building
9
(Saldaña 2009)
Image Microsoft Clipart
Building Relationships for the Effective
Development and Delivery of Research
Data Services
• Research questions– What are librarians experiencing in the early stages of
developing and delivering research data services?
– How can librarian experiences and research data services be improved?
• Data collection: 36 librarians
• Data analysis: NVivo
11
(Faniel, Silipigni Connaway, & Parson 2014)
Attribute coding
• Key information about
the setting and
participant
• Provides important
context used during
data interpretation
and analysis
Examples
• Student enrollment at
institution
• Data management
experience
• Title
• Subject expertise
13
(Saldaña 2009)
Structural coding
• Identifies text based
on topics of inquiry
used to frame an
interview
• Basis for in-depth
analysis within or
across topics
14
Image Microsoft Clipart(Saldaña 2009)
From Interview Questions to Codes
• What data management tasks do you help the
researchers perform?
• What prompts you to help them?
• What individuals or groups support you
spending time helping researchers at your
institution manage their data?
• What individuals or groups worry about you
spending time helping researchers at your
institution manage their data?
15
(Faniel, Silipigni Connaway, & Parson 2014)
What data management tasks do you
help researchers perform?
16
Code: Data Services – Data Management Planning
helping researchers think through how to manage data before
the project starts
“Doing final touches on our data management
planning web page that we can direct people to, that
has the DMP tool linked on it, with all of the
templates for the different grant funding agencies, so
it's kind of where we are.”(Faniel, Silipigni Connaway, & Parson 2014)
What data management tasks do you
help researchers perform?
17
Code: Data Services – Data Management
helping researchers manage their data during a research
project
“One of the things I was thinking, again, with,
along lines of “a pot of gold we discover,” would
be offering consultation services on day-to-day
data management, so more on, along the lines of
file protection, file organization, documentation as
you're collecting the data.”(Faniel, Silipigni Connaway, & Parson 2014)
What data management tasks do you
help researchers perform?
18
Code: Data Services – Data Deposit
contributing or storing data/other research output to a
repository such as description, metadata, documentation;
finding repository to deposit; data curation; readying data for
dissemination
“There are some scholars who actually hope the
library will acquire datasets as well as help them
get their data into the right repositories and
curated appropriately for their grant requirements”
(Faniel, Silipigni Connaway, & Parson 2014)
What prompts you to help them?
19
Code: Data Services – Librarian Initiated
The library/librarian reaching out to offer help to researchers with data
management activities; encouraging participation
“Well, I'll talk about how we've done some of the data
management, some of the data management work that
I've done. In some cases, I knew the faculty member
through some other activities, so I brought up the subject
with them and then they send something to me and then
we had back-and-forth that way. In other cases, the
deans met, suggested that we try to contact a research
group, which we did, so then we met with them.”
(Faniel, Silipigni Connaway, & Parson 2014)
What prompts you to help them?
20
Code: Data Services – Researcher initiated
Researchers asking for help, advice from librarians
“And here's the scenario: A professor comes to us
and it's usually as a result now of the requirements
by the grant-funding agencies. That's what's
prompting more and more faculty to come to IT
and to the libraries seeking guidance and help for
this. We, working with Library IT and working with
University IT, are trying to set up a predictable on
boarding system for these type of projects.”(Faniel, Silipigni Connaway, & Parson 2014)
Who supports you spending time helping
researchers manage their data?
21
Code: Data Service Supporters
people, groups, and/or entities mentioned that support
librarians efforts to provide data management help
“I would say that the University and the
Administration has been really supportive, and the
library administration has been really supportive of
my and my colleagues' efforts to promote data
literacy and management on campus, which has
been great.”(Faniel, Silipigni Connaway, & Parson 2014)
Who worries about you spending time
helping researchers manage their data?
22
Code: Data Service Detractors
people, groups, and/or entities mentioned that worry about
librarians providing data management help or don't think
librarian/library help is appropriate or useful
“And I can think of faculty members that are, and
maybe other faculty members that think it's... That
think it's unexpected or not necessarily sort of
appropriate for a library to do work in this area. ”
(Faniel, Silipigni Connaway, & Parson 2014)
Descriptive coding
• Word or phrase used
to identify the main
topic of a passage
• Depending on
researcher needs
may have more
detailed sub-codes
23
(Saldaña 2009)
Faniel, Silipigni Connaway, & Parson 2013)
Use Descriptive Coding
It's interesting, it's challenging, it's fun. So, that's my
personal [laughter] benefit that I get out of it. It's one
thing that I really enjoy about my job is that... And so, I'm
a former researcher and I wanted to become a librarian,
so I didn't have to do the lab work 24/7, but I still am
passionate about science and so it's a fun way for me to
still be involved in science and help push the research
forward and make sure that we're preserving the
scientific record. So, it's something that I'm passionate
about and so it feeds that passion, I guess.
(Faniel, Silipigni Connaway, & Parson 2014)
24
Librarian Benefits
It's interesting, it's challenging, it's fun. So, that's my
personal [laughter] benefit that I get out of it. It's one
thing that I really enjoy about my job is that... And so, I'm
a former researcher and I wanted to become a librarian,
so I didn't have to do the lab work 24/7, but I still am
passionate about science and so it's a fun way for me to
still be involved in science and help push the research
forward and make sure that we're preserving the
scientific record. So, it's something that I'm passionate
about and so it feeds that passion, I guess.(Faniel, Silipigni Connaway, & Parson 2014)
25
Use Descriptive Coding
Exactly. Right, absolutely. So I think that, that's a challenge. And
another is I feel like we... We talk about data at the subject
specialist level, we're really pushing our IT infrastructure in
our libraries, in ways that's very uncomfortable for them. Um,
you know, should--Should the library be the place to store this
data? Can we store... Can we actually afford this? Are we
gonna look for researchers to write to their grants, data
storage costs now? Because as a library, we can't afford to
take on these costs. And so, whenever we come with a really
exciting big data project, our IT folks say, "We can't store that
in perpetuity." You know, what are we going to do to plan for a
future where we just don't have endless amounts of storage
and funding to handle that?
26
(Faniel, Silipigni Connaway, & Parson 2014)
Challenges with Infrastructure
Exactly. Right, absolutely. So I think that, that's a challenge. And
another is I feel like we... We talk about data at the subject
specialist level, we're really pushing our IT infrastructure in
our libraries, in ways that's very uncomfortable for them. Um,
you know, should--Should the library be the place to store this
data? Can we store... Can we actually afford this? Are we
gonna look for researchers to write to their grants, data
storage costs now? Because as a library, we can't afford to
take on these costs. And so, whenever we come with a really
exciting big data project, our IT folks say, "We can't store that
in perpetuity." You know, what are we going to do to plan for a
future where we just don't have endless amounts of storage
and funding to handle that?
27
(Faniel, Silipigni Connaway, & Parson 2014)
In Vivo coding
• Application of a word or
phrase actually uttered by
participants
• Useful for understanding
participants’ cultures,
worldviews, and honoring
their voice
28
(Saldaña 2009)
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Use In Vivo Coding
Well, I mean, I'm a new person, so like I don't know, maybe this is totally [chuckle] [inaudible]. But one thing that it does feel different to me coming in is that because it's
kind of like a wild west, no man's land at this point. What's different about it is like we can plant our flag and make of it what we want. So, I feel like that does feel... I mean, all of it feels good to me, but that feels different
than other parts of my job where someone's sort of training me, there's a set way of doing it, you know,
there’s sort of certain best practices already in place. It's like, we're still kind of figuring out what are the best pract—you know? Like, I don't know. That does feel different to me…that kind of…that no man’s land…
(Faniel, Silipigni Connaway, & Parson 2014)
29
Use In Vivo Coding
Well, I mean, I'm a new person, so like I don't know, maybe this is totally [chuckle] [inaudible]. But one thing that it does feel different to me coming in is that because it's
kind of like a wild west, no man's land at this point. What's different about it is like we can plant our flag and make of it what we want. So, I feel like that does feel... I mean, all of it feels good to me, but that feels different
than other parts of my job where someone's sort of training me, there's a set way of doing it, you know,
there’s sort of certain best practices already in place. It's like, we're still kind of figuring out what are the best pract—you know? Like, I don't know. That does feel different to me…that kind of…that no man’s land…
(Faniel, Silipigni Connaway, & Parson 2014)
30
Use In Vivo Coding
So, people are territorial and, you know, campus politics. Right? So, that's been really challenging, but also fear. I make a lot of... Especially, on the
public services end, there's fear that if you address it, if you acknowledge that this is happening, “well, is my position no longer relevant?” So, if you ignore it,
then “I can keep my job” versus acknowledging it and being open to... “I can still provide the same
service, I can still connect the user with the information, or curate that information. But now I
need to use different tools and be more collaborative.” So, I think fear and territorialism...
(Faniel, Silipigni Connaway, & Parson 2014)
31
Use In Vivo Coding
So, people are territorial and, you know, campus politics. Right? So, that's been really challenging, but also fear. I make a lot of... Especially, on the
public services end, there's fear that if you address it, if you acknowledge that this is happening, “well, is my position no longer relevant?” So, if you ignore it,
then “I can keep my job” versus acknowledging it and being open to... “I can still provide the same
service, I can still connect the user with the information, or curate that information. But now I
need to use different tools and be more collaborative.” So, I think fear and territorialism...
(Faniel, Silipigni Connaway, & Parson 2014)
32
Initial coding
• Breaks data into parts
for examination of
similarities and
differences
• Provides paths for
exploration to
determine direction of
a study
33
(Saldaña 2009)
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Values coding
• Applies codes that mirror
participants values,
attitudes, and beliefs
• Helps explore
participants’ cultural
values and intra- and
inter- personal
experiences
34
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(Saldaña 2009)
Developing the Codebook
• Codes
• Definitions
• Usage guidelines
• Example text
35
http://www.flickr.com/photos/themadguru/3546619930/
Moving between first and second cycle
coding
• Group the data and read the text excerpts
that have the same codes
• Reflect and memo – How they are similar and different?
– What are some possible categories?
– What codes works vs. don’t work?
37
Second Cycle Coding Methods
• Advanced way to
reorganize and reanalyze
data from first cycle
• Organizes first cycle
codes into categories,
themes, concepts, and/or
theories
• End with smaller, select,
broader groups of codes
39
(Saldaña 2009)Microsoft Clipart Image
“Pattern Coding develops a ‘meta-
code’ – category label that
identifies similarly coded data”
(Saldaña 2009, p. 150).
(Connaway & Powell, 2010, p. 262)
Focused coding categorizes data
based on thematic or conceptual
similarity” (Saldaña 2009, p. 150).
40
Data Reuse and Sensemaking among
Novice Social Scientists
• Research question
– How do novice social science researchers make
sense of quantitative social science data?
• Data collection
– 22 semi structured interviews (n=22)
• Data analysis
– NVivo
41
(Faniel, Kriesberg, & Yakel 2012)
Steps I took for second cycle coding
• Selected partial set of codes to examine
• Reviewed results of NVivo Queries for each code
• Identified needs and actions novices took
• Commented on excerpts categorized into the needs and actions – Decided which to look at together as a group to gain
additional insight into the data
– Generate codes and categories
– Decided which were not useful to examine on their own
42
(Faniel, Kriesberg, & Yakel 2012)
Partial Code Set
• Context
– Associated documentation
– Data descriptive information
– Data quality indicators
– Exemplars
– Relationships among dataset
– Study descriptive information
– Research design
– Weighting
43
(Faniel, Kriesberg, & Yakel 2012)
Needs and Actions
• Having 3rd party critiques of data
• Having 3rd party support of data
• Going back to the original article
• Getting broad overviews of the study
• Reading codebook before downloading data
• Understanding codes and coding, procedures/decisions, measurement, variable meaning/definitions, weighting
• Getting data producers’ justifications
• Confirming/matching own views and beliefs
44
(Faniel, Kriesberg, & Yakel 2012)
Needs and Actions
• Doing simple data analysis
• Doing checks or confirmation data are correct
• Having basic descriptive stats
• Knowing anomalies, limitations with data
• Knowing changes to data (questions, measures)
• Not having too many missing values
• Integrating data – matching variables across datasets
• Integrating data – dealing with differences across datasets …
45
(Faniel, Kriesberg, & Yakel 2012)
My Method of Organizing
Code Code … Comments
Needs/actions List of interviewees
… My form of memos
Needs/actions … …
Needs/actions
…
46
Excel Spreadsheet
(Faniel, Kriesberg, & Yakel 2012)
Further Reduction of Categories
47
(Faniel, Kriesberg & Yakel 2012)
24 “Needs and Actions”
categories were reduced
to 3
Making Sense of Quantitative
Social Science Data
48
Understanding
how data were
transformed
from qualitative to
quantitative
Doing simple data analysis
Doing checks or confirmations
Having basic descriptive stats
Understanding codes and
coding, etc.
(Faniel, Kriesberg, & Yakel 2012)
Making sense of transformations from
qualitative to quantitative data
• Direct maps not enough (e.g. White=0, Black=1, Asian=2, etc.)
• “…I want to find out when they ask the question to the parent or to the
student, how was that question asked and was there follow-up
questions in terms of did they ask what is your race as opposed to
allowing the parent or the student to tell them what their race was”
(CBU10).
• Interested in how direct maps developed
• “So they use New York Times continuously for like the 30 years. New
York Times, it has changed. So I want to know like what years New
York Times was used to gather data. I'm sure they used more than
one newspaper. Also, I want to know which ones those were, for
example” (CBU03).(Faniel, Kriesberg, & Yakel 2012)
49
Making Sense of Quantitative Social Science Data
50
Understanding
how data captured
concepts not well
established in the
literature
Having 3rd party critiques to data
Having 3rd party support of data
Getting data producers’
justifications
Confirming/matching own views
or beliefs
(Faniel, Kriesberg, & Yakel 2012)
Making sense of concepts not well-
established in the literature
• Do beliefs match data producer actions
• “And that’s not to exclude it just by the nature of it being a right wing
organization, but I would want to evaluate their methods to see if
that’s the methods that I would’ve chosen…” (CBU09).
• How will reusing data impact research
• “some parties,… had only like one or two experts rating them, in the
Dutch case, which makes it not super reliable, so that’s what’s kind of
like [it made me think,…] ‘Oh I should really pay attention that that’s
not going to hurt me…” (CBU17).
(Faniel, Kriesberg, & Yakel 2012)
51
Making Sense of Quantitative Social
Science Data
52
Understanding
how data can be
matched and
merged
Integrating – matching
variables, key variables,
unique Ids, etc.
Integrating – dealing with
differences across datasets
Knowing changes to data
(questions, wording) (Faniel, Kriesberg, & Yakel 2012)
Making sense of matching and merging
capabilities across multiple datasets
• Combining longitudinal data
• “If they're not asking the same question over years,… [it’s] particularly
difficult because if they’ve changed the question wording, are then
people answering differently and so there were several discussions
that I had with my dissertation advisor…” (CBU18).
• Merging data from different sources
• “…authors will create a variable, they’ll average across a four or five
year period, and I’m trying to match that with a variable that was
coded for a single year period. So making an argument…that these
two things should be put together …, is something I always have to be
wary of …So when dealing with that,…I’ll see if it’s been done by
others” (CBU04).(Faniel, Kriesberg, & Yakel 2012)
53
Additional Thoughts about Coding
• Coding Practices
• Solo vs. Team?
• Manual vs. Electronic?
• Quantifying qualitative
data
55
Microsoft Clipart Image
Inter-rater Reliability
58
“measures the consistency of understandings or
meanings held by two or more coders”
(Silipigni Connaway & Powell, p. 176) .60
.79
.86.91
.88
.76
.95.74
.93
.82
.66
.53
Holsti’s Coefficient of Reliability
59
C.R.2M
N1 + N2=
M is the number of judgments
on which both of the coders agree.
N1 and N2 are the total number of judgments made by both coders.
Essentially, this equation is calculating reliability as:
agreements
agreements + disagreements(Holsti 1969)
Scott’s pi
60
Scott’spi
(% observed agreement) – (% expected agreement)
1 – (% expected agreement) =
(Holsti 1969)
Example of Manual Coding via Affinity
Diagramming
62
(Holtzblatt & Beyer 1998; Holtzblatt, Wendell, & Wood 2004)
Example of Manual Coding via Affinity
Diagramming
63
Groupings of NotesBlue Label
Pink LabelGreen Label
Quantifying your qualitative data
• Numerical descriptions of
data.
• Tallying mentions of
specific factors.
• Weighting codes
64
n
%
Trust in Digital Repositories
• How do data consumers associate repository actions with trustworthiness?
• How do data consumers conceive of trust in repositories?
(Yakel, Faniel, Kriesberg, & Yoon 2013)
65
Research Methodology
Data Collection
• 22 archaeologists
• 22 novice social scientists
• 22 expert social scientists
Data Analysis
• Code set developed and expanded from interview protocol
• Frequency counts done for categories of interest
Image http://www.english.sxu.edu
(Yakel, Faniel, Kriesberg, & Yoon 2013)
66
Findings: Repository Actions Matter
• Metadata creation– ‘They're very keen on producing the comprehensive metadata.
And it's not that I trust each research … but I trust that the metadata is there for me to go back and check…on my own. I don't give [the archaeological repository] a sort of blanket trust that all the data in there is correct…they provide enough metadata for me to check that on my own…I sort of trust going there because I know that I can find the information I need to validate it’ (CCU02).
• Selection– ‘I mean I wouldn't use a scale from a very overtly conservative or
overtly liberal organization that was involved in other kinds of political activities outside of collecting data because that would make you question what the goal is in collecting that data. So that would I think affect sort of the trustworthiness of repositories at least in my field’ (CBU14).
Recognizing Trustworthy Actions by Repositories
(Yakel, Faniel, Kriesberg, & Yoon 2013)
67
Frequency interviewees linked
repository functions and trust
(Yakel, Faniel, Kriesberg, & Yoon 2013)
68
• Identification
– ‘Data migration is critical…I believe, that a good
repository has to be field-centric. That is to say, if
you're going to put archaeological data into a
repository, that repository has to understand
archaeology. Because when the data must be
migrated, they need to be able to look at it and to
understand whether or not the migration is correct. It's
one thing to say we got all the bits moved, it's another
thing to say it still makes sense for archaeological data’
(CCU21).
Engendering Trust
(Yakel, Faniel, Kriesberg, & Yoon 2013)
69
• Social factors: Disciplinary practice
– ‘I guess that's, well, trust …my own experience with using the
data and then the organization’s long history, and then within the
profession, it's very well spoken of. So, largely, informal
mechanisms are why I trust [repository name]’ (CBU32).
• Structural assurance and preservation
– ‘They're the only repository that I know around for individual
investigator data. They've existed for a long time, they have
incredible reputation for being able to maintain data, keep it well
preserved, the issue of preservation is key, and that they go
through extensive interrogation of the data to make sure that it is
of high enough quality to be allowed to be part of their repository’
(CBU28).
Engendering Trust
(Yakel, Faniel, Kriesberg, & Yoon 2013)
70
74
Dedoose
• Developed by professors from UCLA
• Designed by researchers for researchers for
medical, market, academic, and social policy
research
• Windows, Mac, Linux, Android, iOS, Web-based
Image from http://dirt.projectbamboo.org/resources/dedoose
• Web-based
• Ability to add weight to codes
• Interactive data visualizations
• Simultaneous, real-time access
• Videos: http://www.dedoose.com/Discover/VideoGuides
75
Image from http://dirt.projectbamboo.org/resources/dedoose
• Developed by ATLAS.ti Scientific Software Development GmbH
• PC (operating system requirements: Windows XP, Windows Vista, Windows 7, Windows 8)
• Coming to the Mac OS July 2014
• Videos and Webinars: http://www.atlasti.com/videos.html
77
Image from http://forum.atlasti.com/
• Ability to code multiple documents at the same
time (up to four)
• ATLAS.ti Mobile for the iPad
• Like NVivo, can import PDF’s
ATLAS.ti Distinctive Features
78
Image from http://www.atlasti.com/dlcenter.html
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Faniel, I., Silipigni Connaway, L., & Parson, K. N. (2014, June). Building relationships for the effective development and delivery of research data services. Presentation at the American Library Association Annual Conference, Las Vegas, NV.
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