Analysing Interview Transcripts of a Phenomenological ...

Analysing Interview Transcripts of

a Phenomenological Study on the

Cultural Immersion Experiences of

Graduate Counselling Students

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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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Page 2 of 13 Analysing Interview Transcripts of a Phenomenological Study on the

Cultural Immersion Experiences of Graduate Counselling Students

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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https://nsuworks.nova.edu/tqr/vol16/iss6/13

http://dx.doi.org/10.1037/hum0000107

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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