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A Practical Guide to Collaborative Qualitative Data Analysis

K. Andrew R. Richards
University of Alabama

Michael A. Hemphill
University of North Carolina at Greensboro

The purpose of this article is to provide an overview of a structured, rigorous approach to collaborative qualitative analysis while
attending to challenges associated with working in team environments. The method is rooted in qualitative data analysis literature
related to thematic analysis, as well as the constant comparative method. It seeks to capitalize on the benefits of coordinating
qualitative data analysis in groups, while controlling for some of the challenges introduced when working with multiple analysts.
The method includes the following six phases: (a) preliminary organization and planning, (b) open and axial coding,
(c) development of a preliminary codebook, (d) pilot testing the codebook, (e) the final coding process, and (f) reviewing
the codebook and finalizing themes. These phases are supported by strategies to enhance trustworthiness, such as (a) peer
debriefing, (b) researcher and data triangulation, (c) an audit trail and researcher journal, and (d) a search for negative cases.

Keywords: multiple analysts, qualitative methods, researcher training, trustworthiness

While qualitative research has been traditionally discussed
as an individual undertaking (Richards, 1999), research reports
have in general become increasingly multi-authored (Cornish,
Gillespie, & Zittoun, 2014; Hall, Long, Bermback, Jordan, &
Patterson, 2005), and the field of physical education is no exception
(Hemphill, Richards, Templin, & Blankenship, 2012; Rhoades,
Woods, Daum, Ellison, & Trendowski, 2016). Proponents of
collaborative data analysis note benefits related to integrating
the perspectives provided by multiple researchers, which is often
viewed as one way to enhance trustworthiness (Patton, 2015).
Collaborative data analysis also allows for researchers to effec-
tively manage large datasets while drawing upon diverse perspec-
tives and counteracting individual biases (Olson, McAllister,
Grinnell, Walters, & Appunn, 2016). Further, collaborative ap-
proaches have been presented as one way to effectively mentor new
and developing qualitative researchers (Cornish et al., 2014).

Despite the potential benefits associated with collaborative
qualitative data analysis, coordination among analysts can be
challenging and time consuming (Miles & Huberman, 1994).
Issues related to the need to plan, negotiate, and manage the
complexity of integrating multiple interpretations while balancing
diverse goals for involvement in research also represent challenges
that need to be managed when working in group environments
(Hall et al., 2005; Richards, 1999). Concerns have also been voiced
about the extent to which qualitative data analysis involving
multiple analysts is truly integrative and collaborative, rather than
reflective of multiple researchers working in relative isolation to
produce different accounts or understandings of the data (Moran-
Ellis et al., 2006).

Challenges associated with collaboration become com-
pounded when also considering the need for transparency in
qualitative data analysis. Analysts need to develop, implement,
and report robust, systematic, and defensible plans for analyzing
qualitative data so to build trustworthiness in both the process and
findings of research (Sin, 2007). Authors, however, often prioritize
results in research manuscripts, which limits space for discussing
methods. This leads to short descriptions of data analysis proce-
dures in which broad methods without an explanation of how they
were implemented (Moravcsik, 2014), and can limit the availability
of exemplar data analysis methods in the published literature.
This has given rise to calls for increased transparency in the
data collection, analysis, and presentation aspects of qualitative
research (e.g., Kapiszewski & Kirilova, 2014). The American
Political Science Association (APSA, 2012), for example, recently
published formal recommendations for higher transparency stan-
dards in qualitative research that call for detailed descriptions of
data analysis procedures and require authors support all assertions
with examples from the dataset.

To help address the aforementioned challenges, scholars
across a variety of disciplines have published reports on best
practices related to qualitative data analysis (e.g., Braun &
Clarke, 2006; Cornish et al., 2014; Hall et al., 2005). Many of these
approaches are rooted in theories and epistemologies of qualitative
research that guide practice (e.g., Boyatzis, 1998; Glaser & Strauss,
1967; Lincoln & Guba, 1985; Strauss & Corbin, 2015). Braun and
Clarke’s (2006) highly referenced article provides a step-by-step
approach to completing thematic analysis that helps to demystify
the process with practical examples. In another similar vein, Hall
and colleagues (2005) tackle challenges related to collaborative
data analysis and discuss processes related to (a) building an
analysis team, (b) developing reflexivity and theoretical sensitivity,
(c) addressing analytic procedures, and (d) preparing to publish
findings. Cornish and colleagues (2014) further this discussion by
noting several dimensions of collaboration that are beneficial in

Richards is with the Department of Kinesiology, University of Alabama,
Tuscaloosa, AL. Hemphill is with the Department of Kinesiology, University of
North Carolina at Greensboro, Greensboro, NC. Address author correspondence to
K. Andrew R. Richards at [email protected]

225

Journal of Teaching in Physical Education, 2018, 37, 225-231
https://doi.org/10.1123/jtpe.2017-0084
© 2018 Human Kinetics, Inc. RESEARCH NOTE

mailto:[email protected]

mailto:[email protected]

https://doi.org/10.1123/jtpe.2017-0084

qualitative data analysis. The rigor and quality of the methodology
may benefit, for example, when research teams include insider and
outsider perspectives, multiple disciplines, academics and practi-
tioners, international perspectives, or senior and junior faculty
members.

In this paper, we contribute to the growing literature that
seeks to provide practical approaches to qualitative data analysis by
overviewing a six-step approach to conducting collaborative qual-
itative analysis (CQA), which is grounded in qualitative methods
and data analysis literature (e.g., Glaser & Strauss, 1967; Lincoln &
Guba, 1985; Patton, 2015). While some practical guides in the
literature provide an overview of data analysis procedures, such as
thematic analysis (Braun & Clarke, 2006), and others discuss issues
related to collaboration (Hall et al., 2005), we seek to address both
by overviewing a structured, rigorous approach to CQA while
attending to challenges that stem from working in team environ-
ments. We close by making the case that the CQA process can be
employed when working with students, novice researchers, and
scholars new to qualitative inquiry.

Collaborative Qualitative Analysis:
Building Upon the Literature

In our collaborative work, we began employing a CQA process in
response to a need to balance rigor, transparency, and trustworthi-
ness in data analysis while managing the challenges associated
with analyzing qualitative data in research teams. Our goal was to
integrate the existing literature related to qualitative theory, meth-
ods, and data analysis (Glaser & Strauss, 1967; Patton, 2015;
Strauss & Corbin, 2015) to utilize procedures that allowed us to
develop consistency and agreement in the coding process without
quantifying intercoder reliability (Patton, 2015). Drawing from
recommendations presented in other guides for conducting quali-
tative data analysis (Braun & Clarke, 2006; Hall et al., 2005),
researchers adopting CQA work in teams to collaboratively
develop a codebook (Gibbert, Ruigrok, & Wicki, 2008) through
open and axial coding, and subsequently test that codebook against
previously uncoded data before applying it to the entire dataset.
There are steps embedded to capitalize on perspectives offered by
members of the research team (i.e., researcher triangulation;
Lincoln & Guba, 1985), and the process culminates in a set of
themes and subthemes that form the basis for study results. The
CQA process also embraces the tradition of constant comparison
(Glaser & Strauss, 1967) as newly coded data are compared with
existing coding structures and modifications are made to those
structures through the completion of the coding process. This
provides flexibility to modify generative themes1 in light of
challenging or contradictory data.

The CQA process is grounded in thematic analysis, which is
a process for identifying, analyzing, and reporting patterns in
qualitative data (Boyatzis, 1998). Typically, thematic analysis
culminates with a set of themes that describe the most prominent
patterns in the data. These themes can be identified using inductive
approaches, whereby the researcher seeks patterns in the data
themselves and without any preexisting frame of reference, or
through deductive approaches in which a theoretical or conceptual
framework provides a guiding structure (Braun & Clarke, 2006;
Taylor, Bogdan, & DeVault, 2015). Alternatively, thematic analy-
sis can include a combination of inductive and deductive analysis.
In such an approach, the research topic, questions, and methods
may be informed by a particular theory, and that theory may also

guide the initial analysis of data. Researchers are then intentional
in seeking new ideas that challenge or extend the theoretical
perspectives adopted, which makes the process simultaneously
inductive (Patton, 2015). The particular approach adopted by a
research team will relate to the goals of the project, and particularly
the extent to which the research questions and methods are
informed by previous research and theory.

Trustworthiness is at the center of CQA, and methodological
decisions are made during the research design phase to address
Guba’s (1981) four criteria of credibility, confirmability, depend-
ability, and transferability. In particular, we find that triangulation,
peer debriefing, an audit trail, negative case analysis, and thick
description fold into CQA quite naturally. In addition to the afore-
mentioned researcher triangulation, data triangulation is often a
central feature of design decisions as researchers seek to draw from
multiple data sources to enhance dependability (Brewer & Hunter,
1989), and an outside peer debriefer (Shenton, 2004) can be invited
to comment upon ongoing analysis so to add credibility. An audit
trail can be maintained in a collaborative researcher journal to
enhance confirmability (Miles & Huberman, 1994), and a negative
case analysis can highlight data that contradict the main findings
so to enhance credibility (Lincoln & Guba, 1985). Transferability
is addressed by providing a detailed account of the study context
and through rich description in the presentation of results
(Shenton, 2004).

Overview of the Collaborative Constant
Comparative Qualitative Analysis Process

The CQA process includes a series of six progressive steps that
begin following the collection and transcription of qualitative data,
and culminate with the development of themes and subthemes
that summarize the data (see Figure 1). These steps include
(a) preliminary organization and planning, (b) open and axial
coding, (c) the development of a preliminary codebook, (d) pilot
testing the codebook, (e) the final coding process, and (f) review of
the codebook and finalizing the themes. While the process can be
employed with teams of various sizes, we have found teams of two
to four analysts to be most effective because they capitalize on the
integration of multiple perspectives, while also limiting variability
due to inconsistencies in coding (Olson et al., 2016). In larger
teams, some members may serve as peer debriefers.

When considering the initiation of teamwork, we concur with
the recommendations of Hall and colleagues (2005) related to the
development of rapport among team members prior to beginning
analysis. A lack of comfort may lead team members to hold back
critique and dissenting viewpoints that could be important to data
analysis. This is particularly true of faculty members working with
graduate students where the implied power relationship can
discourage students from being completely forthright. As a result,
we recommend that groups engage in initial conversations un-
related to the data analysis so to get to know one another and their
relational preferences. This could include a discussion of com-
munication styles, previous qualitative research experience, and
epistemological views related to qualitative inquiry (Hall et al.,
2005). The team leader may also provide an overview of the CQA
process, particularly when working with team members who have
not used it previously. As part of this process it should be made
clear that all perspectives and voices are valued, and that all team
members have an important contribution to make in the data
analysis process.

JTPE Vol. 37, No. 2, 2018

226 Richards and Hemphill

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Phase One: Preliminary Organization and Planning

Following the collection and transcription of data, the CQA process

begins with an initial team meeting to discuss project logistics and

create an overarching plan for analysis. This includes writing a

brief description of the project, listing all qualitative data sources to
be included, acknowledging any theoretical or conceptual frame-
works utilized, and considering research questions to be addressed.
Members of the data analysis team should also have an initial
discussion of and negotiate through topics, such as the target

Figure 1 — Overview of the six steps involved in collaborative qualitative analysis. Strategies for enhancing trustworthiness underpin the analysis
process.

JTPE Vol. 37, No. 2, 2018

Qualitative Data Analysis 227

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journal, anticipated authorship, and a flexible week-by-week plan
for analysis. The weekly plan includes a reference to the data
analysis phase, coding assignments for each team member, and
space for additional notes and clarification (see Figure 2). Deci-
sions related to the target journal and authorship, as well as the
weekly plan for analysis, will likely evolve over time, but we find it
helpful to begin such conversations early to ensure that all team
members are on the same page.

Phase Two: Open and Axial Coding

To begin the data analysis process we use open coding to identify
discrete concepts and patterns in the data, and axial coding to make
connections between those patterns (Corbin & Strauss, 1990).
While open and axial coding are distinct analytical procedures,

we embrace Strauss and Corbin’s (2015) recommendation that
they can occur simultaneously as researchers identify patterns and
then begins to note how those patterns fit together. Specifically,
each member of the research team reads two to three different data
transcripts (e.g., field notes, interviews, reflection journal entries)
and codes them into generative categories using their preferred
method (e.g., qualitative data analysis software, manual coding).
The goal is to identify patterns common across transcripts, or to
note deviant cases that appear.

Depending on the approach to thematic analysis adopted, a
theoretical framework and research questions could frame this
process. We find it helpful, however, to retain at least some
inductive elements so to remain open to generative themes that
may not fit with theory. Following each round of coding, team
members write memos in a researcher journal, preferably through a

Project Overview and Data Analysis Timeline

Project Overview: To understand how physical education teachers navigate the sociopolitical
realities of the contexts in which they work and derive meaning through interactions with
administrators, colleagues, parents, and students. This work is a qualitative follow-up to a large-
scale survey that was completed by over 400 physical education teachers from the US Midwest.

1. Theoretical Framework: Occupational socialization theory
2. Target Journal:Physical education pedagogy specific journal, such as the Journal of

Teaching in Physical Education or Research Quarterly for Exercise and Sport
3. Anticipated Authorship:Researcher 1, Researcher 2, Researcher 3
4. Data Sources:30 individual interviews, 5 focus group interviews, field notes from

observations of teachers
5. Research Questions:

a. How do physical education teachers perceive that they matter given the
marginalized nature of their subject?

b. How do interactions with administrators, colleagues, parents, and students
influence physical educators’ perceptions of mattering and marginalization?

c. How do physical education teachers’ perceptions of mattering and
marginalization influence feelings of role stress and burnout?

Weekly Plan for Data Analysis:
Week Coding Phase Coding Assignment Notes
July 11, 2016 Initial Meeting rof nalp eht ssucsiD enoN

analysis and review the
data analysis timeline.
Make changes and
adjustments to the plan as
necessary. Discuss the
various phases of analysis
and prepare to begin open
coding.

August 1, 2016 Open Coding 1 Researcher 1: 1001, 1002

Researcher 2: 1003, 1004

Researcher 3: 1005, 1006

Open coding of each
transcript into categories.
Following coding, identify
3-4 generative themes and
write a 1 page memo

August8, 2016 Open Coding 2 Researcher 1: 1022, 1023

Researcher 2: 1024, 1025

Researcher 3: 1007, 1027

Open coding of each
transcript into categories.
Following coding, identify
3-4 generative themes and
write a 1 page memo

Figure 2 — Example of a project overview, code numbers (e.g., 1001) refer to interview transcripts.

JTPE Vol. 37, No. 2, 2018

228 Richards and Hemphill

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shared online platform (e.g., Google Docs), in which they overview
the coding and describe two or three generative themes supported
by data excerpts. During research meetings, team members over-
view their coding in reference to the memos they wrote, and the
team discusses the coding process more generally. Phase two
continues for three to four iterations, or until the research team
feels they have seen and agree upon a variety of generative themes
related to the research questions. The exact number of transcripts
coded depends on the size of the dataset and the level of initial
agreement established amongst the researchers. The team can move
on when all coders feel comfortable with advancing to the devel-
opment of a codebook. In our experience, this usually involves
coding approximately 30% of all transcripts, but could be less when
working with large datasets.

Phase Three: Development of a Preliminary
Codebook

After the completion of phase two, one team member reviews the
memos and develops a preliminary codebook (Richards,
Gaudreault, Starck, & Woods, in press). An example codebook
is included in Figure 3, and typically includes first- and second-order
themes, definitions for all themes, and space to code quotations from
the transcripts. Theme definitions provide the criteria against which
quotations are judged for inclusion in the codebook, and thus should
be clear and specific. We code by copy/pasting excerpts from the
transcript files into the codebook and flagging each with the
participant’s code number, the line numbers in the transcript file,
and a reference to the data source (e.g., Interview 1001, 102–105).

This allows for reference back to the data source to gain additional
context for quotations as needed. We always include a “General
(Uncoded)” category where researchers can place quotations that are
relevant, but do not fit anywhere in the existing coding structure.
These quotations can then be discussed during team meetings. Once
compiled, the draft codebook is circulated to the research team for
review and discussed during a subsequent team meeting. Changes
are made based on the team discussion, and a preliminary codebook
is finalized. At this stage we enlist the assistance of a researcher who
is familiar with the project, but not involved in the data analysis, to
serve as a peer debriefer (Lincoln & Guba, 1985). This individual
reviews and comments on the initial codebook, and appropriate
adjustments are made before proceeding.

Phase Four: Pilot Testing the Codebook

After the initial codebook has been developed, it is tested against
previously uncoded data. During this step, the researchers all code
the same two to three transcripts, and make notes in the researcher
journal related to interesting trends or problems with the codebook.
Weekly research team meetings provide a platform for researchers
to overview and compare their coding and discrepancies are
discussed until consensus is reached. Entries in the researcher
journal are also discussed. These discussions lead to the develop-
ment of coding conventions, which function as rules that guide
subsequent coding decisions. Conventions may be created for
double coding excerpts into two generative themes in rare instances
when both capture the content of a single quotation, and that
quotation cannot be divided in a meaningful way.

Perceived Mattering Codebook

stpircsnarT morf selpmaxE snoitinifeD semehtbuS semehT

Subject Marginalization Lack of
communication

Teacher believes physical education does
not matter due to lack of communication
about issues that affect thephysical
education environment.

“My stressful day, um probably when things pop up that are
not…A lot of my stresses get raised from being an activities
director. If the school calls me and says now they have to—
they have kids who are not coming, they change times, or I
have a different schedule. My stuff is very organized and if
it’s not where I think it’s supposed to be and I need it, that’s
very stressful for me” (1019, 210–217, individual interview)

dna emit fo kcaL
resources

Teacher believes physical education does
not matter due to lack of teaching contact
time and resources such as materials,
equipment for PE, or teaching facilities.

“It’s kind of rough because I don’t have my own classroom. I
don’t have my own computer up there. I don’t have a room
that I can make into a welcoming environment so that’s kind
of rough” (1018, 110–112, individual interview)

“Right now that class is more just like babysitting. It’s just a
study hall, kind of boring. I don’t have a classroom I’m in the
gym balcony where the bleachers are at. I don’t have space
the kids complain” (1018, 120–122, focus group)

eileb rehcaeT troppus fo kcaL ves physical education does not
matter due to situations in which the
physical educator does not feel support for
ideas or initiatives.

“I think the colleagues, it wouldn’t matter either way outside
of the P.E. teachers, and I think the administration wouldn’t
care either way.” (1018, 348–350, individual interview)

“At the elementary level that would be a big issue. As they
get a little older, you know middle school, high school it’s not
as much probably fun. They don’t see it in their eyes as much
fun. The students themselves probably wouldn’t care, there’d
be a handful.” (1019, 307–309, focus group)

Figure 3 — Example codebook including themes, subthemes, definitions of subthemes, and quotations from the dataset.

JTPE Vol. 37, No. 2, 2018

Qualitative Data Analysis 229

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Conventions can also specify priority in the use of generative
themes. In Figure 3, for example, there are generative themes for
both “lack of support” and “lack of communication” related to
subject marginalization. Lack of communication could be consid-
ered a way in which support is limited, but because there is a
specific category for lack of communication, it would receive
priority when coding. Modifications are made to the codebook
as needed during these meetings, and an updated codebook is
produced to guide subsequent analysis. The pilot testing continues
for three to four rounds of coding, or until the research team feels
confident in the codebook. Once the team feels ready to move on,
they have a final discussion of the codebook in light of the pilot
testing and make adjustments. The peer debriefer (Lincoln & Guba,
1985) then reviews the evolving codebook and recommends
changes prior to the final coding process.

Phase Five: Final Coding Process

In the final phase of coding the adjusted codebook is applied to
all project data, including that which had been previously coded
during the formative phases of codebook development. While the
researcher triangulation involved when using multiple coders can
increase “validity2” in qualitative research, some have argued that
it has the potential to reduce “reliability” because of inconsisten-
cies in coding across analysts (Olson et al., 2016). As a result, some
qualitative researchers have introduced measures of inter-coder
reliability in an attempt to quantify agreement between coders
(Neuendorf, 2017). While acknowledging these perspectives, we
struggle with efforts to apply the quantitative principles of reliabil-
ity and validity to qualitative data analysis (Patton, 2015). We
prefer to approach the issue of coder agreement, and the broader
notions of trustworthiness and credibility, by establishing a clear
protocol and codebook (Gibbert et al., 2008) through previous steps
of CQA, and then dialogue through and reach consensus
on coded data. This is done either through consensus coding or
split coding. Regardless of the strategy chosen, coding conventions
developed during previous phases are applied to the coding process.
Analysts continue to make notes in the researcher journal related to
problems with the generative themes, or interesting patterns in the
data, and issues are discussed during weekly research meetings.
We continue to apply the constant comparative method (Strauss &
Corbin, 2015) at this stage as modifications are made to the code-
book to reflect ongoing insights developed in the coding process.

Consensus coding is the more rigorous, but more time-
consuming form of final coding. It is likely the more effective
approach when working in larger groups where coding consistency
concerns are more abundant (Olson et al., 2016). During each
iteration of coding, team members code the same two to three
transcripts into the codebook. Then, during research team meet-
ings, each coded statement is compared across members of the
research team. Disagreements are discussed until the group reaches
consensus. Split Coding relies more heavily on the establishment of
clarity through the preliminary coding phases and the coding
conventions that have been developed (Gibbert et al., 2008). While
less rigorous than consensus coding, split coding is also less time
consuming and manageable within smaller teams. During each
iteration of coding, team members code two to three different
transcripts. As a result, only one member of the team will code each
transcript. Then, during research meetings, questions or concerns
related to particular excerpts are discussed. Split coding culminates
with each team member reviewing all coded excerpts in the
codebook, and disagreements are discussed to consensus.

Phase Six: Review the Codebook and Finalize the
Themes

After all of the transcripts have been coded using consensus
coding or split coding, the research team meets one final time to
review the codebook. During the meeting, the codebook is
developed into a thematic structure comprised of themes and
associated subthemes that describe participants’ perspectives.
The thematic structure is reviewed and approved by all members
of the research team, and the final agreed upon structure forms the
basis for the result that will be presented as part of the manuscript.
Importantly, through the earlier stages of CQA, all members of
the research team have had a hand in shaping and agree upon the
themes that are presented. This process, therefore, capitalizes on
the enhanced trustworthiness provided by multiple analysts,
while minimizing issues related to coder variability, without
attempting to quantify the qualitative data analysis process
(Patton, 2015).

Conclusions and Final Thoughts

The purpose of this article is to provide an overview of a structured,
rigorous approach to CQA while attending to challenges that stem
from working in team environments. While this article has …

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