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Analysing Interview Transcripts of
a Phenomenological Study on the
Cultural Immersion Experiences of
Graduate Counselling Students
© 2019 SAGE Publications, Ltd. All Rights Reserved.
This PDF has been generated from SAGE Research Methods Datasets.
Analysing Interview Transcripts of
a Phenomenological Study on the
Cultural Immersion Experiences of
Graduate Counselling Students
Student Guide
Introduction
Qualitative research seeks in-depth understanding of social phenomena within
natural setting and relies on direct experiences. It is rich and holistic as well as
helps interpret various viewpoints and stories (Englander, 2018). One approach
to qualitative research is phenomenological study. Phenomenological study is
a qualitative research method that is used to describe how human beings
experience a certain phenomenon and make meaning out of it. For example,
12-day study abroad immersion experience in the current data exemplar is the
phenomenon experienced by the graduate counselling students. In a
phenomenological study, the researcher is able to analyse the perceptions,
perspectives, understandings, and feelings of those people who have actually
experienced or lived the phenomenon (Creswell & Creswell, 2018). Through this
phenomenological process, knowledge is acquired through experience, and the
researcher describes participants’ experiences in a specific context.
The data exemplar comes from a cultural immersion experience where
counsellors in training were given the opportunity to go on a study abroad trip
to India for 12 days. The participants had professional and personal experiences
during their trip that they recorded in a daily journal entry as well as shared in a
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post-trip interview. Authors will discuss about more details on how the data are
drawn for the current paper under data exemplar section. Going to India was new
for most participants, and the cultural immersion experiences they had added to
their understanding and feelings about the Indian culture and the Indian people
(Neal Kimball & Turner, 2018). The in-depth interviews demonstrate a dataset in a
phenomenological study because they can be used to analyse perspectives and
feelings of people who were able to experience being a part of the Indian culture.
Phenomenological Study
According to Creswell and Creswell (2018), there are five basic approaches of
conducting a qualitative research study of which phenomenological approach is
one of the most widely used. This approach is mostly used for conducting variety
of projects because of its methodological ease when compared to ethnography
and grounded theory approaches that involve extensive commitment from the
researcher in terms of time, resources for data collection, as well as analysis
(Creswell & Creswell, 2018). In a phenomenological study, information is gathered
from individuals who have experienced or lived in a situation or phenomenon
using interviews, journals, and/ or observations. Qualitative interviews are
valuable because they allow participants to provide explanations and opinions on
the topic of study (Tracy, 2013). Interviews allow researchers to further explore
complex phenomenon that may be hidden. Structured interviews generally use a
list of questions that are repeated across all participants. Unstructured interviews,
on the other hand, are more flexible. Interview logistics such as access, quiet
space, privacy, comfort, and availability of electronic devices to record the
interview are important things to consider. Interviews require transcription to be
considered usable data. Transcription requires listening repeatedly to participants’
voices, which can assist in early analysis (Tracy, 2013). When transcribing, the
use of appropriate symbols is important, so that another researcher can
understand the data. Additionally, fact checking, meaning listening to records
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while reading over transcripts, can help researchers correct any mistakes made
during transcribing. The next step is coding the transcripts either manually like in
the current data exemplar or by using the computer software like NVivo and Atlas/
ti with the goal of identifying the common themes from the phenomenon studied.
Having an understanding of phenomenological study in terms of interviews,
transcription, and manual coding is important to a student learning about
qualitative research because it provides a foundation on the processes that are
required to produce quality research.
Phenomenological Study Interview Transcripts of Graduate Student
Cultural Immersion Experiences
In this section, authors will discuss the details of how the dataset of two interview
transcripts was chosen for the current paper. As described above,
phenomenological approach facilitates researchers to study the participants’ lived
experiences about a phenomena – in this case, a 12-day cultural immersion
experience of graduate counselling students to a foreign country – India. This
study abroad trip is a part of a 15-week counselling class taught by the primary
author at a South Eastern (Mercer) university from the United States. The
rationale for conducting the original immersion experience study was based on
the research need for multicultural competence skill building through immersion
activities and study abroad programmes across the globe (Prosek & Michel,
2016). Two faculty and eight graduate students participated in this study abroad
trip and after the trip in November 2014, primary author’s graduate research
assistant at that time interviewed the students and transcribed the data after
seeking the University’s Institutional Review Board’s approval. Current authors
randomly selected two of those transcripts for the present paper with the goals of
(1) teaching the concept of phenomenological approach, (2) providing a dataset to
explain/teach qualitative data analysis through phenomenological approach, and
(3) designing a Teaching and Student Guide for data analysis through appropriate
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coding, and identification of themes by using the phenomenological approach.
Data Analysis of Current Phenomenological Study
Data analysis in the current phenomenological study is similar to data analysis
in other qualitative approaches where the data can be organized either manually
or through computer software such as NVivo, Atlas/ti, NUD*IST, etc., and then
analysed. Wickham and Woods (2005) highlight the importance of developing a
Computer Aided Qualitative Data Analysis Protocol (CAQDAP) while analysing
the data through these software in order to have a clear purpose, transparency,
and a logical deduction of themes. Although the entire dataset of the original
phenomenological study was analysed using NVivo software, in the current paper,
authors will focus on the process of theme deduction through manual coding
only. Hence, the primary goal of the researchers during this data analysis process
in a phenomenological study is to deduce meaning out of the themes identified
through initial manual coding. This process includes the following six steps that
are vital for any phenomenological approach.
Step 1: Transcriptions
The first step after data collection is transcription of your semi-structured
interviews or journals or other qualitative datasets. The dataset provided with
this paper consists of two of such transcripts from the immersion experience
phenomenological study conducted by the first author.
Step 2: Organizing the Data
After transcription, you need to focus on organizing the data to make sense out of
the volumes of rich text. In other words, you are trying to develop the storyline by
carefully reading and understanding the transcripts/datasets. This process starts
with coding the dataset either manually or through software. A key element to
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keep in mind for understanding the bigger picture is based on the “purpose” of
your study (Saldaña, 2013). In the current paper, authors explain the manual
coding process for phenomenological study as discussed in Step 3.
Step 3: Coding
Coding is a way to systematically organize and understand the data (Tracy, 2013).
Codes are the basic elements of labelling and organizing the dataset. They are
essential to link the data and to synthesize them into categories. Manual coding
can be done using hard or soft copies of the data. Manual coding entails marking
up the transcripts with comments and highlights to identify themes that stand
out. In doing so, researchers can also make connections between themes to
develop first-level and second-level codes. Based on the review of Saldaña’s
(2013) coding manual for qualitative researchers, authors propose the following
guidelines while coding the transcripts manually for any phenomenological study:
• It is recommended that manual coding is done on hard copy printouts first
instead of on the computer monitor as you can see from the Example 1.
• Manual coding can be done by circling, highlighting, bolding, underlining, or
colouring rich or significant quotes (please refer to Example 1).
• Coding can include highlighting cognitive and emotional feelings.
• Coding manages, filters, highlights, and focuses the significant features of
the data. For instance, in Example 1, only the feeling word of “extremely
excited” is highlighted and the rest of the statement is filtered.
• Coder’s primary goal is to find the repetitive patterns of action and
consistencies in the data. As you can notice from Example 2, the authors
circled the words like “great, eye-opening, and like, etc.” in an attempt to
identify the repetitive patterns of action.
• First cycle coding processes can be single word to a full sentence to
an entire page of text. According to Saldaña’s (2013) coding manual for
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qualitative researchers, first cycle coding process starts with initial coding
of the dataset. It is a fairly direct and simple way of manual coding of
the data based on seven subcategories of attributes like grammatical,
elemental, affective, literacy and language, exploratory, procedural, and
theming the data. Below are examples of the first-level coding from the
attached dataset.
Example 1:
In Example 1, the first cycle coding is based on the affective subcategory
described above and is supported by the participant’s use of the terms “extremely
excited, like to.” The first cycle coding in this case is associated with the question
being answered by the student “S.” Further, the student statement about travel
and willing to find out about different cultures falls into the major category of
“thoughts and emotions” which is another example of first cycle coding.
Example 2:
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In Example 2, a second major category or another illustration of first cycle coding,
“self-awareness and personal growth,” can be seen. This also have emerged
based on the participant’s experiences. In addition, the first major category of
“thoughts and emotions” is further supported.
Second cycle coding processes, the portions coded can be the exact same units
as the first cycle coding, and even a reconfiguration of the codes themselves
developed thus far as illustrated below. In other words, that second cycle coding
requires the researcher to use their analytical skills through classifying, prioritizing,
integrating, synthesizing, abstracting, conceptualizing, and theory building
(Saldaña, 2013, p. 45). The six types of second cycle coding according to
Saldaña’s manual are pattern, focussed, axial, theoretical, elaborate, and
longitudinal coding. Below illustration is an example of the pattern type under the
second cycle coding category.
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With the first-level coding being “personal growth and self-awareness,” the
example above also shows second-level coding under this category. Personal
growth and self-awareness emerged as a category based on the participant’s
description of his or her experience. Hence, the five things (patterns) that the
participant states to have learned add a second cycle coding of “benefits from the
experience” or “learning from experience.”
Sometimes, coding may not be right the first time. Hence, recoding can occur with
a more adjusted perspective. Using memos helps the researcher to track their
own thoughts and decision-making process while organizing the data. Below is an
example of using memos.
In the above example, the researcher analysing the data writes out “rare
opportunity or benefits?” as a memo or note to self because he or she sees a
possible theme emerging out of the highlighted statement. As researchers are
manually coding, they use memos to write out their thought process to aid them in
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their analysis.
Finally, codes can lump or split the data into categories and subcategories as
illustrated in the above two examples.
Step 4: Deducing Categories
We learnt above that coding allows you to link an idea to the data. Once the codes
are identified, you need to start organizing them into categories (Saldaña, 2013).
Sometimes we code and categorize data by what participants talk about and also
based on the memos that you create while reading through the transcript/dataset.
You may group things together not just because they are alike but also because
they might have something in common. For example, the below quotes from
transcript0089 can be classified into the category of “Personal Growth through
Self- Awareness” because both participants are expressing the similar views
regarding their study abroad experience for more than once in the interview.
Above example illustrates how the major categories of “thoughts and emotions,”
“personal growth and self-awareness” emerged.
Step 5: Identifying Common Themes and Making Interpretations
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This is a critical step of Phenomenological data analysis process as a researcher
deduces interpretations based on the common patterns or themes identified and
linking them to the bigger picture or purpose of the study. In other words,
researcher develops the storyline from the common themes identified from the
dataset and links it to deduce the meaning that participants attach through
experiencing the phenomena – in this case, the cultural immersion experience.
“culture shock” is an example of one of such common themes identified by
the researchers from their cultural immersion phenomenological study. Both
participants from the dataset expressed that they experienced culture shock
through the immersion experience which they felt was “eye-opening” and
facilitated “personal growth through self-awareness.”
Step 6: Maintaining a Reflective Journal
This is an important step in the qualitative data analysis process especially
while conducting a phenomenological study, since it addresses the researcher
bias and also establishes appropriate transferability and credibility measures.
Chenail (2011) recommends maintaining a reflective journal or diary which not
only examines the researcher’s choices and decision-making process throughout
the qualitative study process but also helps to re-examine the choices made or
themes identified for reconsideration. Journaling in fact serves as an audit trail
for the researchers to keep the themes and choices transparent, coherent, and
simple. Hence, it plays a crucial role in the qualitative data analysis process.
Summary
Phenomenological approach provides an opportunity for the participants to
experience a particular phenomenon and derive meaning out of it. By using
this qualitative approach, authors explored the cultural immersion experiences
of graduate counselling students after a 12-day study abroad trip to India. The
dataset of post trip interview transcripts is provided to the readers along with
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the details of how to analyse the data through manual coding process. Specific
illustrations of how to identify codes, memos, categories, and subsequent themes
are presented.
Reflective Questions
1. How does this dataset demonstrate the phenomenological approach
of qualitative research?
2. What are some common themes that can be found in the dataset?
3. How do the interview questions guide the analysis?
4. Journal your reflections with the data analysis and coding process with
a minimum of five entries.
Further Readings
Chenail, R. J. (2011). Ten steps for conceptualizing and conducting qualitative
research studies in a pragmatically curious manner. The Qualitative Report, 16(6),
1715–1732. Retrieved from https://nsuworks.nova.edu/tqr/vol16/iss6/13
Creswell, J, W., & Creswell, J. D. (2018). Research design: Qualitative,
quantitative, and mixed methods approaches (5th ed.). Los Angeles, CA: SAGE.
Englander, M. (2018). General knowledge claims in qualitative research. The
Humanistic Psychologist. Published ahead of print. Retrieved from:
http://dx.doi.org/10.1037/hum0000107
Neal Kimball, C., & Turner, S. (2018). Nurturing the apprentice: An immersion
training in qualitative research. Qualitative Psychology, 5(2), 290–299.
Prosek, E. A., & Michel, R. E. (2016). Transformative learning for counselor
trainees: Implementation of the multicultural immersion experience model.
International Journal for the Advancement of Counselling, 38(1), 61–76.
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Saldaña, J. (2013). The coding manual for qualitative researchers. Los Angeles,
CA: SAGE.
Steffen, E. (2014). Qualitative research methods in psychology: Combining core
approaches. Existential Analysis, 25(1), 176.
Tracy, Sarah J. (2013). Qualitative research methods: Collecting evidence,
crafting analysis, communicating impact. Hoboken, NJ: Wiley-Blackwell.
Wickham, M., & Woods, M. (2005). Reflecting on the strategic use of CAQDAS
to manage and report on the qualitative research process. The Qualitative Report,
10(4), 687–702.
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