Qualitative Data Analysis Method for Phenomenological Research

A phenomenological research design serves as an effective means of exploring students’ lived experiences. This is particularly so when the focus is on students with disabilities and school disciplinary practices. The selection of an appropriate data analysis method constitutes an important aspect of this research design: Qualitative Data Analysis Method for Phenomenological Research.

This article will investigate the methodology utilized in the data analysis of a phenomenological study and show an applied coding example of it. Besides, it will also look at the comparative advantages of doing it manually as opposed to software.

Proposed Data Analysis Method and Alignment with Research Design

Braun and Clarke’s Reflexive Thematic Analysis (RTA) is the most appropriate analysis for this phenomenological study. The designer adopts a modern-day approach that aligns well with the aim of the research design. The research design aims to uncover the essence of students with disabilities’ lived experiences about disciplinary practices. Reflexive TA techniques utilize subjectivity as a research resource while denouncing positivist notions of researcher “bias” and recognizing that qualitative analysis is always interpretative by nature (Braun & Clarke, 2022).

The six-phase procedure of reflexive thematic analysis consists of (1) becoming familiar with the data, (2) generating initial codes, (3) searching for themes, (4) reviewing themes, (5) defining and naming themes, and (6) writing the report (Braun & Clarke, 2006). The method emphasizes researcher reflexivity, meaning essentially that the meaning in the data is not fixed but is constructed through the researcher’s inferential engagement (Naeem et al., 2023). RTA is often used in health studies and education to develop themes from qualitative data. It allows researchers to investigate complex experiential phenomena while being methodologically rigorous (Braun & Clarke, 2024).

This analytical approach has various benefits for the proposed study. It sticks with the idea of using subjective experiences to understand why they seem to offset each other. Additionally, the reflexive nature of the approach acknowledges the role of the researcher in the making of meaning, which is particularly important in the case of the sensitive topic of disciplinary experiences.

In addition, focusing on the depth of the interpretation rather than counting the frequency conforms to the phenomenological principle with respect to the emphasis on the meaning and not measure. In addition, a flexible theoretical positioning in reflexive TA allows researchers to work on their data from differing epistemologies with analytical consistency.

However, challenges can be associated with undertaking this reflexive thematic analysis. The interpretative nature of the research process calls for thoughtful attention to methodological coherence. Researchers must ensure that theories, analytical techniques, and quality criteria align with one another.

In addition, focusing on researcher reflexivity requires a continuous questioning of assumptions and positioning—a challenge, especially when studying the experiences of marginalized people. The method requires significant time for extensive data engagement, especially in educational research projects it may present practical limitations.

Coding Example: Application to Conversational Data

To show how RTA works in practice, the following example shows the coding process using a fictitious participant reflection from the proposed study.

“When I got suspended for three days, I felt like everyone was staring at me when I came back. The teacher didn’t even ask why I was upset in class—she just sent me to the office. It made me feel like my disability doesn’t matter, like they see me as just a troublemaker.

I started thinking maybe I don’t belong in regular classes. Now I’m always worried about getting in trouble again, even when I’m just trying to participate.”

Phase 1: Familiarization with Data

The initial reading shows themes of social stigma, institutional misunderstanding of disability-related behavior, trouble with identity, and ongoing anxiety about future education participation.

Phase 2: Generating Initial Codes

• “Social surveillance” (everyone was staring)
• “Lack of inquiry into emotional state” (didn’t ask why upset)
• “Institutional dismissal of disability” (disability doesn’t matter)
• “Pathologizing of student identity” (see me as a troublemaker)
• “Questioning educational placement” (maybe I don’t belong)
• “Anticipatory anxiety” (always worried about getting in trouble)

Phase 3: Searching for Themes

These codes cluster around broader thematic areas: experiences of social alienation following disciplinary action, institutional failure to recognize disability-related needs, negative identity construction through disciplinary encounters, and the creation of ongoing educational anxiety.

Phase 4: Reviewing Themes

The identified approaches are coherent and distinct, and they capture the complexity of the experience. The themes show how ways of disciplining mix with a disability identity to create different educative marginalizations.

This illustration indicates how RTA moves systematically from data collection to meanings with an ongoing connection back to participants’ lived experiences and the researcher’s active and involved role in the construction of themes.

Manual Coding versus Software-Assisted Analysis

The comparison between manual coding and software-assisted analysis using NVivo reveals both similarities and distinctions that influence methodological decision-making in RTA. Both types of thematic analysis require researchers to systematically engage with data, follow a particular methodology framework, and interpret participants’ meanings (Braun & Clarke, 2022).

Similarities between Approaches

Coding, whether done manually or with the help of software, requires knowledge about the data, a consistent application of the procedures, and an ongoing reflexive engagement with the interpretations. Also, both strategies involve a close adherence to methodological concordance and transparency in analysis. The software can allow the researcher to be more creative thanks to efficient data management, thus relieving the researcher of the mechanical support found in regular manual analysis systems.

Distinctions and Advantages

There are many other benefits of NVivo over manual analysis, including better organization of data and easy retrieval. The software helps manage many sources of data and allows for complex queries and visualization tools that show patterns that humans may miss. In addition, the systematic management of data on NVivo guarantees the accuracy of qualitative research and a transparent audit trail (Kiger & Varpio, 2020). When triangulating on interviews and reflective journals, it is useful to have multiple data in one project and have it coded in one software program.

Moreover, not all software packages are conducive to reflexive thematic analysis. Some criticize that using features like automated queries can create a distance between the researcher and the context of the data. This can affect the quality of phenomenological research. Furthermore, too much reliance on software functions may lead to mechanical coding approaches that counter reflexive principles of thematic analysis today.

Preference and Justification

For this phenomenological study, the use of a hybrid approach with the first manual engagement and the second computer engagement is most appropriate. To begin the analytical process, I would undertake manual reading and initial coding in order to become intimately familiar with the lived experiences of participants and to remain phenomenologically sensitive to the contexts of lived experiences. According to NVivo, this would enhance organization, facilitate systematic comparisons across participants, and allow data merging from the triangulation approach.

This preference is due to the awareness that RTA requires both interpretive depth and systematic rigor. Making contact manually ensures that researchers’ interactions with participant voices are primary and reflexive, while software support provides the organizational infrastructure requisite for credible analysis of complex educational data involving vulnerable populations. Building on the interpretive sensitivity necessary for phenomenological research, this approach employs technology to improve analytical transparency and methodological coherence.

Conclusion

Braun and Clarke’s reflexive thematic analysis is ideal for exploring how students with disabilities experience school discipline because it is theoretically underpinned and has systematic yet flexible analytic procedures. The focus of the method on the reflexivity of the researcher and interpretive engagement fits with phenomenology’s commitments and offers methodological rigor. The proposed hybrid method of coding provides the interpretative depth necessary for an understanding of lived experience, along with the organizational benefits of contemporary software for qualitative data analysis, thus satisfying the philosophical demands of phenomenological research and the practical requirements of qualitative research.

References

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa

Braun, V., & Clarke, V. (2022). Thematic analysis: A practical guide. SAGE.

Braun, V., & Clarke, V. (2024). Supporting best practice in reflexive thematic analysis reporting in palliative medicine: A review of published research and introduction to the Reflexive Thematic Analysis Reporting Guidelines (RTARG). Palliative Medicine, 38(6), 608–616. https://doi.org/10.1177/02692163241234800

Kiger, M. E., & Varpio, L. (2020). Thematic analysis of qualitative data: AMEE Guide no. 131. Medical Teacher, 42(8), 846–854. https://www.tandfonline.com/doi/abs/10.1080/0142159X.2020.1755030

Naeem, M., Ozuem, W., Howell, K. E., & Ranfagni, S. (2023). A step-by-step process of thematic analysis to develop a conceptual model in qualitative research. International Journal of Qualitative Methods, 22(1), 1–18. Sagepub. https://doi.org/10.1177/16094069231205789

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EDR 8400: Qualitative Research

Assignment 4: Determine a Method to Analyze Qualitative Data

In the Spotlight on Skills section of this lesson you are provided with an explanation of how to structure a presentation. Remember, slides are visual aids to support what you would say during a presentation. This resource will help you to compose your slides in a way that effectively balances visuals and text.

You have covered a lot of material these past 4 lessons.  Now, you have an opportunity to demonstrate your knowledge of qualitative methodology and designs.

For this lesson’s assignment, you will consider a data analysis method that aligns with the qualitative research design for a potential study that you proposed in Lesson 2. Address each of the following three prompts as an essay or presentation:

• Explain your proposed method of data analysis in detail, and how this method aligns with your chosen research design. Discuss what you see as the advantages and potential challenges of the proposed method.
• Create an example to illustrate how you will code your data. You may use any text data available to you that is conversational. Examples include an entry from your own journal, text taken from the internet, or a mock conversation that you creatively write.
  The text you choose to analyze should be one to two paragraphs in length. Note that if you take material from the internet, be sure to cite it properly.
• Review one or more of the tutorials on NVivo from the ASC listed in the resources section for this lesson. Whether you choose to engage in a manual coding process to analyze your research data or to use NVivo (the data analysis software that is available to you through the University), there are some features regarding coding and analysis that are common to both approaches. Explain in what ways you think manual coding and software coding will be similar and/or different? State your preference, explaining the reasons for your choice.
  

  Qualitative Data Analysis Method for Phenomenological Research

Length: If you choose to write an essay, this assignment will be at least 2 pages.

Resources:

Required Resources

• Completing Your Qualitative Dissertation by Linda Dale Bloomberg
  ISBN: 9781071869819
  Publication Date: 2023-04-26
  Bloomberg, L. D. (2023). Completing your qualitative dissertation: A road map from beginning to end (5th ed). Sage.
  Read Chapter 9, Analyzing Data and Reporting Findings.
  This chapter addresses and explains the processes of qualitative data analysis, and identifies the specific methods involved in analyzing qualitative data. Attention is focused on deciding on an analytic process based on the specific research design that is chosen.Access your Redshelf book by clicking on the link in the Getting Started module or the Bookshelf link at the top of your course.
• Qualitative Data Analysis Resources Types of Data and Their Analysis
  Flick, U. (Ed.). (2014). Part IV: Types of data and their analysis. In The SAGE handbook of qualitative data analysis (pp. 295-296). SAGE. https://go.openathens.net/redirector/nu.edu?url=https://methods.sagepub.com/book/the-sage-handbook-of-qualitative-data-analysis/d326.xml
  In this section introduction, the editor introduces the chapters that align data analysis methods with specific types of data collected. Click on the chapter that aligns with your preferred data collection method for further reading.
• Analysis Techniques to Identify Themes in Qualitative Data
  Bloomberg, L. (2019). Analysis techniques to identify themes in qualitative data [Webinar].Northcentral University/Center for Teaching and Learning.
  This webinar lays out the different stages of the qualitative analysis process, explaining key analytic concepts. Useful tips and strategies are included.
• Data Analysis and Interpretation
  Byrne, D. (2017). Project planner: Data analysis and interpretation. SAGE Research Methods. https://go.openathens.net/redirector/nu.edu?url=https://methods.sagepub.com/project-planner/data-analysis-and-interpretation
  The author provides considerations for selecting a qualitative data analysis method. He offers an introduction to common techniques for content and thematic analysis as well as specific techniques for narrative research, phenomenology, and grounded theory.
• Qualitative Data Analysis in Education
  Bradley-Levine, J. (Academic). (2015). Qualitative data analysis in education [Video file]. SAGE Video.
  In this video (6:17), the researcher discusses qualitative data analysis in the context of educational research and illustrates how data analysis can inform the educational setting.
• Qualitative Data Analysis
  Maxwell, J. (2018). Qualitative data analysis. In B. B. Frey (Ed.), The SAGE encyclopedia of educational research, measurement, and evaluation. SAGE. https://go.openathens.net/redirector/nu.edu?url=https://methods.sagepub.com/reference/the-sage-encyclopedia-of-educational-research-measurement-and-evaluation/i16972.xml
  In this chapter, the author provides an overview of qualitative data analysis including the history and traditions of qualitative data analysis and summarizes contemporary approaches to qualitative data analysis.