Setting out to do a qualitative research project can be challenging for early career researchers due to the bewildering multiplicity of approaches existing in published literature. Everyone seems to have their own interpretation of how to go about doing qualitative research, probably because of the sentient nature of the paradigm to subtleties of the researcher, the researched, and the context. These may shape the way each researcher develops and articulates on how to do qualitative research. Thematic analysis of transcripts from interviews or group discussions is a common approach a beginner often resorts to when attempting qualitive research. In this situation, it is useful to develop a clear understanding on (a) sample size, (b) data collection, and (c) reflexive thematic analysis.

Before elaborating on the aforementioned concepts, there is a need to consider reflexivity. This pertains to the subjectivity of the researcher and its impact on the study. Communicating on reflexivity requires reporting on the position and values of the researcher, the relationship of the researcher with the participants, and its impact on conceptualizing the study, data collection, analysis, and reporting as a dynamic, continuous process.

Regarding sampling, “saturation” is often the advocated end point for data collection in qualitative researchers. A systematic review by Vasileiou et al in 2018 reported saturation and pragmatic considerations as the two main types of sample size justification reported in 214 qualitative research articles they reviewed.¹ The authors of the review point to several issues when saturation is reported as the justification. Saturation was often conceptualized in different ways (eg, coding saturation, theoretical saturation) and the type and parameters of saturation were not clearly defined. Sample size was also often mentioned as a limitation in the reviewed articles, reflecting a nomothetic statistical tendency to generalize findings rather than an ideographic basis for number of participants studied. Based on the review findings, the authors recommended using Erickson’s criteria for deciding on sample size. Researchers need to have sufficient data broad enough to have variety in evidence including evidence that is divergent or discrepant and deep enough for case-oriented analysis. Additionally, the topic, the skill, and experience of the researcher also matter when deciding upon a feasible sample size. Considerations for participant selection need to be informed by first defining what constitutes compelling evidence to answer specific research questions. Homogeneity may be preferable in some studies, while maximum variation or extreme cases may be required in others. A useful approach may be to give justification for an initial sample for analysis and then clarifying the elements that would form the basis for stopping data collection or increasing the sample size.

Qualitative interviews are in essence storytelling and interviewers need to be able to ask the right questions to elicit the right stories.² Literature review and piloting as close to the targeted set of participants to get insider perspective may help to attain this. A “grant tour” question (like describing a typical day in the participant’s life) can help start the storytelling process, followed by a set of easy questions leading on to more difficult ones. An interviewer needs to have good listening skills and use appropriate nonverbal cues during the interview. Good interviews will be dynamic and new ideas emerge during the conversation. Thus, the interview guidelines may develop further during the data collection process, and some participants may have to be contacted again for talking further on emergent probes, if needed. Interview protocols need to be prepared not just as a guide for questions but also as a procedural guide, clearly scripting the introduction, the consenting process, the questions and probes, and the concluding statements including the possibility of being contacted again. The importance of maintaining ethical standards during the whole process cannot be understated.

A ubiquitous feature of qualitative data analysis is coding, a process of making sense of dense data by tagging or indexing portions of data to “codes.” The coding plan should cover details like what type of information to code and how to name codes while being clear on the particular purpose for which coding is done—to answer the research question at hand. The next step for thematic analysis is to condense codes further into categories, which are central ideas formed from aggregates of codes. Categories are usually condensed further or reflectively interpreted into themes. Negotiating the assortment of approaches used for categorizing and preparation of themes and choosing one that is su itable  for one’s research can be daunting for a less experienced researcher.

The thematic analysis subtypes as recommended by Braun and Clarke may be of use here to label ones approach.³ Analytical approaches that are extensively inductive are labeled Big Q while those that are largely deductive, using codes determined a priori are called Small Q. Obviously, many studies may use a technique that does not strictly fall in either of these approaches. These approaches can be considered as being at two ends of a spectrum, and many analytical approaches can be labeled as intermediate Q.⁴ A useful approach for novice researchers may be to start off with an inductive approach for a few initial transcripts. The initial codes may be condensed into categories that can be used as a framework matrix. The remaining transcripts can be coded deductively into the matrix derived from the first stage of analysis.

Standard reporting guidelines like the Standards for Reporting Qualitative Research may be followed for reporting findings of qualitative research.⁵ A qualitative research exercise can be a challenging activity for those with limited experience in this approach, but it is a journey worth embarking on. If done with due diligence, it will be an exercise that makes one look at oneself as a researcher, reflect on ones values, and how these shapes the representation of the information one receives. Such an experience is very important for a researcher if they are to judge whether the representations they make in their work constitute addition to knowledge in the field of their engagement as part of their research.

Acknowledgments

I thank Dr Aravind Chandru B, MPH scholar, Achutha Menon Centre for Health Science Studies, for reading the initial draft from a student’s perspective and giving suggestions to improve the write-up.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author received no financial support for the research, authorship, and/or publication of this article.