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Using Computer-assisted Qualitative Data Analysis Software (CAQDAS) to Re-examine Traditionally Analyzed Data: Expanding our Understanding of the Data and of Ourselves as Scholars

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As diverse members of a college of education evaluation committee one of our charges is to support faculty as we document and improve our teaching. Our committee asked faculty to respond to three qualitative questions, documenting ways in which interdepartmental and cross-department conversations are used to promote reflective thinking about our practice. Three of us investigated the use of CAQDAS to provide an additional level of analysis and how we learned more about ourselves as scholars through this collaboration. Our findings include recommendations regarding the use of CAQDAS to support collaborative efforts by diverse scholars. © 2012: Linnea L. Rademaker, Elizabeth J. Grace, Stephen K. Curda, and Nova Southeastern University.
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The Qualitative Report 2012 Volume 17, Article 43, 1-11
http://www.nova.edu/ssss/QR/QR17/rademaker.pdf
Using Computer-assisted Qualitative Data Analysis Software
(CAQDAS) to Re-examine Traditionally Analyzed Data:
Expanding our Understanding of the Data and of Ourselves as
Scholars
Linnea L. Rademaker, Elizabeth J. Grace, and Stephen K. Curda
National Louis University, Chicago, Illinois, USA
As diverse members of a college of education evaluation committee one of
our charges is to support faculty as we document and improve our
teaching. Our committee asked faculty to respond to three qualitative
questions, documenting ways in which interdepartmental and cross-
department conversations are used to promote reflective thinking about
our practice. Three of us investigated the use of CAQDAS to provide an
additional level of analysis and how we learned more about ourselves as
scholars through this collaboration. Our findings include
recommendations regarding the use of CAQDAS to support collaborative
efforts by diverse scholars. Key Words: Multi-disciplinary Research,
Multiple Perspectives, CAQDAS, Qualitative Research.
In this paper we discuss our work on a college-wide evaluation committee, in
which we investigated faculty’s use of conversation and reflection to demonstrate
professionalism in teaching. The three of us, as members of the committee, all are
members of different college departments, and had very different perspectives towards
research and the use of computers. Linnea is a professor of research methods in a college
of education, and primarily teaches qualitative research and action research. Linda had
the most experience with “Computer-Assisted Qualitative Data Analysis Software” or
CAQDAS, and wanted to bring together Elizabeth and Steve to collaborate on this
project. Elizabeth is a professor in the department of special education and primarily
teaches math methods, assessment, and disability studies. Elizabeth has a diagnosis of
autism and had minimal experience with CAQDAS before this study. Steve is the
director of assessment for the college and a professor in the technology department. Steve
has extensive experience with quantitative research and with computers, using computers
in his quantitative research, but had little experience with qualitative research or the use
of computers in qualitative research before this study. Significantly, we wanted to make
visible the ability of CAQDAS to assist diverse scholars in collaborating in research and
data analysis. We initially conducted the study to highlight the importance of authentic
assessment of faculty teaching in an era of accountability that often tends to examine only
quantitative assessments, specifically student course evaluation data. But our further
work with this data enlightened us to the collaborative benefits of CAQDAS. After
completing the data collection (described below), completing a traditional group hand-
analysis of the data, and presenting our findings to our college, Linnea wanted to expand
the project by inputting the data into CAQDAS. She asked Elizabeth and Steve to join
her, because of their unique backgrounds. She knew that Steve had extensive computer
experience, and that Elizabeth had extensive qualitative research experience. But, Linnea
2 The Qualitative Report 2012
wondered how the three of us would be able to work together on a project, and how the
use of computers might change our understanding of the data. In essence, we wanted to
expand upon the work of the committee’s group qualitative analysis; but, more
importantly, we wanted to see if three diverse researchers could work together using
computer software to assist our collaboration and to see if the three of us would find
additional insights into one another’s ways of thinking about qualitative data analysis.
Research Question and Rationale
We began our inquiry with the following guiding question:
When analyzing qualitative data, how will the use of CAQDAS change
our analysis and interpretations, compared to our first round of coding an
analysis within our larger committee?
However, this question changed over the time of our work together. We naїvely expected
that using a computer software program to help us analyze might help us uncover hidden
themes and issues, but Elizabeth was skeptical of this because of her reticence to believe
that CAQDAS had any value in aiding analysis. Of course data analysis in qualitative
research can be daunting, due to the sheer volume of languaged, video, audio and
pictorial data that is available. When we investigated the literature on computer software
in qualitative data analysis, we changed our question (will follow the rationale) and
provide the following rationale as a snapshot of that literature search.
The use of computers to aid in the analysis challenge is fairly new, when
compared with the history of qualitative research and traditional analysis through hand-
coding, sorting, and memoing. Computer data analysis software for qualitative research
was available widely to the public in the late 1990s (compared with the traditional
analysis of qualitative data, which began in the anthropological literature and dates back
to the work of Boas, 1858-1942; Malinowski, 1884-1942; Mead, 1901-1978 and others.
It was adopted by other disciplines seeking to document ethnographies of culture in
education in the 1960s and 1970s with the work of Wolcott, and Geertz, followed by
Lincoln and Guba, Miles and Huberman, and Corbin and Strauss and others in the
1980s). Interestingly, although NUD*IST was “born” in 1981 (Richards, 2002), version
2 in 1987 was still only available on mainframes, and not commercially. It wasn’t until
1993 that it was available on MACs only, then later on PCs. Atlas ti. released their first
commercial version of their CAQDAS in 1993 (http://www.qualitative-data-
analysis.com/atlas-ti-company-history.html) and the first English version of MAX QDA
appeared in 1995 (http://www.maxqda.com/products/maxqda/history). Literature on
CAQDAS began to appear in the late 1990s, with the majority of literature appearing
after 2000 (In a search of EBSCO host databases, we found two references for 1996, one
for 1999, and the rest were post-2000). Most of this literature addressed the capabilities
of the software and caveats for novice users. Yet we wondered what such programs could
offer us as three diverse researchers that might support us as we sought to work together
to present an analysis and interpretation of our study? How might the results be
expanded? How would the software help us collaborate? What could we learn that will
facilitate our cohesive interpretations?
Linnea L. Rademaker, Elizabeth J. Grace, and Stephen K. Curda 3
Theoretical Framework
In conducting a literature search on the usage and issues of CAQDAS, we
discovered that with the improvement of software over the last 15 years, authors have
written increasingly more about the topic in qualitative research journals. Some offered
caveats or implications for continued use of such software (e.g., Bazeley, 2006; Johnston,
2006; Lee & Esterhuizen, 2000; Smith & Short, 2001). Lage and Godoy (2008) presented
a review of literature about the benefits and issues of using CAQDAS. Specifically, the
authors discuss the literature via a categorization of four themes: (a) “the effectiveness of
software data management”; (b) “the increase of researcher’s closeness with the date”; (c)
the ease of researchers’ communication about the data; and, (d) the possibility of bias
in analytical process(p. 77, abstract). We were cognizant of these issues as the three of
us undertook our analysis with computer software following the committee’s previous
analysis of the data. After reviewing the literature, we confirmed to each other our
suspicions that we wouldn’t find changes in our analysis (e.g., new themes, differing
results), as Linnea knew and Elizabeth and Steve soon understood that computer-assisted
data analysis is largely dependent upon our work in entering and coding the data. We
wondered, however, after continued searches into the literature, how the software might
improve our own trustworthiness and confirmability (Lincoln & Guba, 1985) of our
findings, thereby allowing us to perhaps go deeper into the data, and expand our findings.
Ryan (2009) wrote about “trustworthiness” in her own study, concluding that while the
software did not increase rigor for her work, nor change the analysis, it did allow her to
improve her organizational abilities, and she felt the trustworthinessof her work
increased, due to the linking capabilities of the software in being able to represent her
analyses and connect various sources of data to interpretations and themes. Ryan
concluded: “Software programs . . . enable researchers to make visible their
methodological processes for a more 'trustworthy' study” (p. 158). Ryan’s work
continued to influence our thinking throughout our analysis, as we felt our diverse
perspectives made such “trustworthiness” more complex via our different lenses and our
different approaches to qualitative data understanding and analysis. We expand on this
issue in our discussion section.
Davis and Meyer (2009) examined the procedures of analysis, comparing hand
coding with computer-assisted coding, detailing advantages, disadvantages and common
assumptions held in the use of computer-assisted qualitative data analysis in sports
psychology research. Particular caveats included the unpredictable malfunctions of
computers and the misperception that the program will analyze for the researcher (as
quantitative software might). They suggested that it is essential that the researcher learn
the software before using it for a project, and that you cannot separate the researcher from
the programthat is, the researcher decides which features and codes to use and when;
the program will simplify organization, recall of data, and visual representation. As
mentioned earlier, Linnea had extensive previous experience with the software, and Steve
was computer-proficient in many quantitative analysis programs, so we felt that we had
sufficient experience to undertake this project.
Gilbert (2002) further investigated the issue of coding and balancing “closeness
and “distance” from the data. We concur that the use of the software left us feeling
closeor (for some) overly-familiar with the data, sometimes making analysis difficult.
4 The Qualitative Report 2012
Her emphasis on the tactile-digital divideseemed a bit dated for our usage, however.
None of us had difficulty working with data in a digital manner from a computer usage
perspective.
Method of Data Collection/Sampling
Since our college-level (college of education) committee was charged with
assisting faculty in the documentation and reporting of the quality of faculty teaching
(college-level constitutional charge), we chose to survey our faculty (using internet-based
survey softwarehttp://www.SurveyMonkey.com) about their perceptions of their
collaborations in reflecting about teaching. We believed that providing faculty with an
alternative to student-course evaluations, via the documentation of their professional
activities, would provide faculty with additional materials with which to make their case
for tenure and promotion. Our tenure and promotion process requires strong
documentation and persuasive materials to show how we’ve improved our teaching in the
years leading up to tenure application. We sent out a link to an internet-based survey
which contained three questions:
1. What is something you have changed in your teaching recently and why have you
changed?
2. Do you have conversations within your program about effective teaching? Please
provide sample topics.
3. Do you have conversations with others outside of your program about effective
teaching? Please provide sample topics.
We received 39 responses (out of approximately 148 full-time education faculty).
Data were copied and transcribed from the internet survey software, and initially
organized by each of the three questions.
Methods of Data Analysis
Initially, each of the six members of the original committee (representing six
different departments in the college of education) was asked to reflect on the responses.
At our May, 2010 meeting, we chose to break into three groups, each group further
examining one of the three questions. We came back together as a group to share our
themes from our group coding, and reviewed all three questions as a group. We agreed
upon themes from each of the questions, and wrote about it in an executive summary to
the entire faculty. We chose the themes (Table 1) for each question based upon our small
group analysis.
Linnea L. Rademaker, Elizabeth J. Grace, and Stephen K. Curda 5
Table 1. Themes from Hand-Analysis
Question (n=39)
Themes from small group hand-analysis
Question 1: What have you
changed in your teaching
and why have you
changed?
Technology: faculty are weaving technology into their
teaching in a wide variety of formats
Diverse Learners: Faculty are incorporating universal design,
differentiating instruction, and using literature specific to
learners
General Things: Faculty are incorporating theoretical
frameworks, such as Darling-Hammond; changing
expectations; chanting texts; using topics outside of texts;
altering assignments, using current events; incorporating
more discussion
Question 2: Have you had
conversations in your
department or program
about effective teaching?
83% said
“yes”
Curriculum: Conversations surrounding curriculum and
curriculum issues dominated this category
Pedagogy: Faculty also discussed issues of pedagogy and
meeting the needs of all learners
Assessment: Some faculty found issues with assessment in
their courseshow to accurately and adequately assess
learning
Question 3: Have you had
conversations outside of
your department or
program about effective
teaching? (participants
indicated they have
conversations with PK-12
school personnel,
friends/family, people
outside of work, and
colleagues in national
organizations)
80% said
“yes”
Our teaching: This topic dominated the findings for this
question, indicating that many faculty discuss teaching with
other faculty or others outside of their department or
program
The nature of teaching: This indicates that some faculty
engage in philosophical discussions about what it means to
teach in higher education and to teach future teachers
Research, and other tasks related to teaching: Some faculty
discuss other tasks of being a higher education professor,
specifically their research and issues related to it
Policy/issues of education: Some (but few) indicated
conversations about education policy and other issues in
education
6 The Qualitative Report 2012
For Elizabeth, this was a new adventure in learning and doing simultaneously, as
she was learning to use and trust the software, and learning to work with Linnea and
Steve as collaborators. Not only was Elizabeth skeptical that the software would be
capable of providing added value, but she had nagging doubts from her traditionalist
ethnographic upbringing that using technology in any way might “taint” the nature of the
analysis in ways that “doing it by hand” (tantamount, in her mind, then, to “organically”)
would not. Elizabeth knew that Linnea was aware of these doubts but also believed that
talking about them would have the possibility of tainting the data in another direction, so
for her part, she proceeded with the study in the spirit of seeing what would happen,
shelving, for the moment, her extremely binary-minded nature.
We initially broke out the data into the three questions (similar to the original
committee’s work, as indicated above). However, each of us looked at the responses to
each question, and began to create “tree nodes” as we noted them in each response. In
this way computer initial coding is very similar to the work we did as a larger committee
to analyze the data in that we each assigned meaning to parts of responses. At first the
meanings, or “tree nodes” were descriptive in nature. For instance, if the respondent said
that they talked with other colleagues at national conferences about teaching methods
within a similar course, we would assign that a tree node of “conversations about
pedagogy”.
We thought back to Gilbert’s (2002) study. She identified a third stage of coding
awareness that she calls a “metacognitive shift” (p. 220). During our analysis and coding
sessions, we found it helpful to include reflection on our own biases—especially in our
choosing of “tree nodes”. While we felt our “node” choices were descriptive, they still
represented our “choices”, seen through our lenses. Significantly, all three of us include
our lens as “education” faculty. As faculty in a college of education, we are part of
teacher preparation programs that strive to cultivate reflective thinking in our graduates.
We would also hope to see the same qualities, such as reflective thinking, modeled by our
faculty. Gilbert cautions novice software users to reflect “about how and why one works
in a particular way” (p. 220). It was important for increasing trustworthiness for us to
acknowledge and reflect upon our biases and the ways that we incorporated our own
situational awareness as part of the college of education into our analysis of themes and
issues. Discussion
Interpretations and Findings
In this section we discuss what we consider to be significant findings for us as
faculty from diverse departments working on a collaborative project. First, we found that
a unique aspect of using CAQDAS vs. hand-coding/analysis was the ability of the
program to create visual representations of datacharts, graphs, trees—that helped us in
our analysis (we elaborate below) and potentially can help readers to more deeply
understand our interpretations. Part of this aspect is the ease and agility with which such
graphics can be made. Second, we discussed how when several of us had coded by hand
in the past, it was often difficult to justify the work involved in changing and re-arranging
codings and categories, or to try out new arrangements, due to the nature of paper and
Linnea L. Rademaker, Elizabeth J. Grace, and Stephen K. Curda 7
penciland the messinessthat such changes often created, as described below. In
CAQDAS, it is easy to move things around and try different coding categories and
groupings.
A third and final issue we discuss here is related to the nature of the software to
increase the ability of three diverse researchersdifferent departments, different teaching
areas, and different learning stylesto be able to work together to come to a group
understanding of the issues found within our data. Steve felt that it would be important
for the three of us to be as diverse as our survey respondents (in his quantitative world of
reliability and validity), as we were also members of the group (college of education)
being studied. That we were as diverse in our areas of expertise as our working
paradigms helped us to represent ourselves and our participants but also caused divergent
thinking about analysis and themes. However, the use of CAQDAS helped us to
understand each other’s viewpoints, and work to represent multiple perspectives in our
analysis. We elaborate on this aspect below.
In addressing the first issue and thinking about the visual aspects of CAQDAS,
we offer this example from our analysis sessions. When we were analyzing Question 2
(“Have you had conversations about teaching within your department or program?”), we
decided to try a few graphs. We created a tree-cluster by word similarity, and the words
“curriculum,” “pedagogy,” and “assessment came up frequently, as we expected based
upon our initial, group hand-coding. However, the tree node allowed us to visualize
subcategories by grouping similar words together under the umbrella words. The visual
offered a striking presentation that illustrated the wealth and depth of the conversations.
Linnea found the visual helped her to think about all of the themes (nodes) as part of a
larger collectivealmost like a family. Grouped together were phrases from participants
that are part of teaching or pedagogy, such as:
Collaboration with colleagues about curriculum
Sharing classroom materials and resources
Conversations about program contexts
Strategies for teaching
Incorporating philosophy and theory into teaching
Blending theory into practice
Using technology in pedagogy
Meeting diverse student learning styles and needs
These phrases, and more, demonstrated the vast array of topics about which our faculty
converse, think about, and reflect upon with each other. While we saw these across the
responses when we hand-coded on several pieces of paper, our intent at that time was
more of the quality of data ordering (Charmaz, as cited in Seidel, 1998, p. 4). However,
as we were able to return to the mass of data responses in a visual way, via the tree graph,
we were able to concentrate on the depth of responses around a single theme or issue—
that of conversations about our teaching or pedagogy. The striking visual display inspired
our thinking about the reflective opportunity we had given faculty through the tool of this
survey. A second issue in coding is that of “trying out” changes in coding categories.
While hand-coding, we were hesitant to make many changes, as the volume of data
8 The Qualitative Report 2012
causes a “chaotic moment”, when the data is re-connected randomly, as in pre-coding.
For Elizabeth the process of creating a graph via the push of a button initially caused a
“fear” response. She asked, “Wait, can you get the other idea back?” When she realized
that we could “try out” endless possibilities of visual connections and representations, she
was excited. We were all impressed with the versatility of the graphics, being able to “try
out” various graphics, yet still have the group’s foundational analysis from which to
work. Thinking back to our original work analyzing the data with the use of a group of
six committee members, we found that most committee members were hesitant to
question the initial analysis of the pairs of the committee members that we set up. When
each committee pair brought back their coding categories to the whole group, the group
accepted them without question, and moved towards a broader picture of the data through
thematic categorization. However, with CAQDAS we were able to try out different
themes and key coding categories, without the worry of losing our initial categories. The
trying out actually is a technique supported by several data analysis theorists, as a means
of rigorous attention to the data as the foundation (our own thinking and experiences)
from which we chose our themes (Corbin & Strauss, 2008; Mertens, 2005; Miles &
Huberman, 1994). CAQDAS allowed us to re-arrange categories, while still keeping our
initial thematic categories. In fact, we were able to look at them side-by-side in a visual
representation. We also created visual representations of all of the codes used in a
question (Figure 1).
Figure 1. Coding Categories for “What have you changed in your teaching?
Linnea L. Rademaker, Elizabeth J. Grace, and Stephen K. Curda 9
Graphics like that in Figure 1 assisted those of us who tend to be more visual, and helped
us to work at making meaning and understanding within visual learning styles, which
could then be translated back to language understanding such as is represented within this
paper. As mentioned earlier, Gilbert (2002) cautioned CAQDAS users to be wary of the
digital learning curve when using software, and we felt that this particular caveat did not
apply to us. However, a more applicable caveat that is related to the difference in users as
mentioned by Gilbert is that of the difference in learning styleslearning that is
represented by the way we, as researchers, read and understand our data. In writing more
recently about his seminal Multiple Intelligences (Gardner, 1983) theory, Gardner and
Moran (2006) posited that “the intelligences can be grouped together for various
purposes” (p. 228). This is, perhaps, what happened with the use of CAQDAS and the
visual representations—we, as separate and distinct researchers, with unique learning
styles, were able to come together and find understanding of each other and the data, as
well as represent understanding through the software.
A third finding is that of being able to easily utilize multiple researchers in a
single project. The ease with which we were able to share suggestions and coding
categories with each other capitalized on the ability of the software to offer data
presentation in multiple ways. We discovered that we could incorporate all three
researchers within one file, share it electronically (via an electronic server), and continue
coding. However, this was somewhat hampered by the way our university has purchased
the product—we only have 10 licenses for faculty. The remaining licenses are on the
server, and can only be accessed in the university computer labs. While one of us had the
full software on her own work computer, the remaining two researchers had to access the
software via the computer labs, limiting work time to office time. Since we are a
university that serves primarily working adult commuters, we have multiple campuses,
and faculty often teach at multiple locations traveling throughout a metropolitan area, and
in addition to working from home, these reduced our ability to make use of the computer
labs.
Implications
We realized as we began this project that we probably wouldn’t find “different”
themes, and this realization was confirmed as we began to work together. We knew that
the hard work of coding qualitative data still comes from within the mind of the
researcher and is intricately tied to the perspective of each researcher (Coffey &
Atkinson, 1996), which in our case proved to be vast, as we each represented vastly
different learning styles and different perspectives within education. However, we were
pleasantly surprised at how CAQDAS allowed easy storage and easy access to large
amounts of language (and other types of) data. We also appreciated the ease with which
we could manipulate and change around categories, themes and codes. Both of these
points confirmed Ryan’s (2009) findings of the software’s ability to increase the
researcher’s organizational abilities.
Still, using any new software, even for those who commonly work in computer-
mediated environments, requires time. We found it difficult to negotiate enough time to
teach the novice members of our group how to work with the software. It was
enlightening, however, to try different features of CAQDAS and find that these “trial and
10 The Qualitative Report 2012
error” efforts resulted in helping us view the data in new and different ways (i.e., the
graphs mentioned above) and to understand each other as diverse scholars. The “trying
out” resulted in unique moments for each of us that expanded our understanding of what
another member was trying to express related to data analysis or interpretation. Our
suggestion is for researchers to allow sufficient time to “play around” with the software,
and to try to look at their data from multiple perspectives. Additionally, we suggest
diverse scholars can work together through the medium of CAQDAS to provide more
opportunity for collaboration in data analysis and interpretation.
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Author Note
Dr. Linnea L. Rademaker is an assistant professor in the department of
Educational Foundations & Inquiry in the National College of Education (NCE) at
National Louis University (NLU), where she teaches research methods and action
research courses to master's and doctoral students. Linnea's interests include action
research to empower teachers, narrative methods, and case study evaluation. Linnea also
is a founding partner of FuturEd, LLC, a private educational evaluation and consulting
firm located in Indiana. Correspondence regarding this article can be addressed to Dr.
Linnea L. Rademaker, phone: 847-947-5043; e-mail: linnea.rademaker@nl.edu
Dr. Elizabeth J. Grace is an assistant professor with the Diversity in Learning and
Teaching department in NCE at NLU. She teaches foundations, assessment and
mathematics, history, and research at the master's and doctoral levels. Her interest areas
include disability studies in education, community/inclusion, methods and uses of
research, applied theatre, and philosophy. Correspondence regarding this article can also
be addressed to Dr. Elizabeth J. Grace, phone: 847-947-5138; e-mail:
Elizabeth.grace@nl.edu
Dr. Stephen K. Curda is Director of Assessment and Innovative Technology in
NCE at NLU. His responsibilities include planning, developing, and implementing
program assessment and faculty development activities across the College with respect to
the mission and goals of the NCE. He also teaches and advises master's and doctoral
students. His research focus areas include technology integration, Instructional Systems
Design, and assessment and evaluation. Correspondence regarding this article can also be
addressed to Dr. Stephen K. Curda, phone: 847-947-5255; e-mail: Stephen.curda@nl.edu
Copyright 2012: Linnea L. Rademaker, Elizabeth J. Grace, Stephen K. Curda, and
Nova Southeastern University
Article Citation
Rademaker, L. L., Grace, E. J., & Curda, S. K. (2012). Using computer-assisted
qualiative data analysis sofware (CAQDAS) to re-examine traditionally analyzed
data: Expanding our understanding of the data and of ourselves as scholars. The
Qualitative Report, 17(Art. 43), 1-11. Retrieved from
http://www.nova.edu/ssss/QR/QR17/rademaker.pdf
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