Using Software for Qualitative Analysis

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

Microsoft Clipart Image

Qualitative Research Methods

• Observation

• Survey

• Interviews

– Focus Group

– Individual

• Documents

– Diaries

– Journals


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


(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

Some First Cycle Coding Methods

10

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)

First Cycle Coding Exercise

12

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


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)



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)



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”


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.”


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. ”


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.


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


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


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)


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…


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…


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...


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...


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)


Values coding

• Applies codes that mirror

participants values,

attitudes, and beliefs

• Helps explore

participants’ cultural

values and intra- and

inter- personal

experiences

34


(Saldaña 2009)

Developing the Codebook

• Codes

• Definitions

• Usage guidelines

• Example text

35

http://www.flickr.com/photos/themadguru/3546619930/

http://www.flickr.com/photos/themadguru/3546619930/

36

The

Codebook

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

38

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


Partial Code Set

• Context

– Associated documentation

– Data descriptive information

– Data quality indicators

– Exemplars

– Relationships among dataset

– Study descriptive information

– Research design

– Weighting

43


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


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


My Method of Organizing

Code Code … Comments

Needs/actions List of interviewees

… My form of memos

Needs/actions … …

Needs/actions

…

46

Excel Spreadsheet


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.


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


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).


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)

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Second Cycle Coding Exercise

54

Additional Thoughts about Coding

• Coding Practices

• Solo vs. Team?

• Manual vs. Electronic?

• Quantifying qualitative

data

55


Coding Practices

• My lessons learned

• Unit of analysis

• Code coverage

56


Solo vs. Team Coding

57



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)

61

Coding Manually vs. Electronically


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


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


67

Frequency interviewees linked

repository functions and trust


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


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


70

Frequency trust factors mentioned

(Yakel, Faniel, Kriesberg, & Yoon 2013) 71

NVivo 10 Demo

72

Overview of Other Qualitative

Software

73

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

Dedoose Bubble Plots

76

Image from Analysis Video: http://www.dedoose.com/Discover/VideoGuides

• 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

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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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Additional Sources for QualitativeSoftware

Thank You!

©2013 OCLC. This work is licensed under a Creative Commons Attribution 3.0 Unported License. Suggested attribution: “This work uses content from [presentation title] © OCLC, used under a Creative Commons Attribution license: http://creativecommons.org/licenses/by/3.0/”

Ixchel M. Faniel

[email protected]

http://creativecommons.org/licenses/by/3.0/

Using Software for Qualitative Analysis

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