6 Qualitative Data Analysis Methods: How to Choose

Last Updated on 1 week ago by Bernard Mugo

There are six main qualitative data analysis methods, and choosing the wrong one for your study is

one of the most common mistakes PhD students make. In this guide, I will walk you through each method — what it is, what makes it distinctive, and when to use it — so you can make a confident, well-reasoned choice for your research.

My name is Bernard Mugo. Over the past three years, I have helped more than 200 PhD students analyze their qualitative data and complete their dissertations. Based on that experience, I have also added a practical recommendation at the end for beginners.

The six methods I will cover are:

• Qualitative content analysis
• Narrative analysis
• Discourse analysis
• Thematic analysis
• Grounded theory
• Interpretive phenomenological analysis (IPA)

Qualitative data analysis is the process of systematically examining non-numerical data — such as interview transcripts, focus group recordings, field notes, or documents — to identify patterns, meanings, and themes that answer your research questions.

Unlike quantitative analysis, which works with numbers and statistics, qualitative analysis works with language, stories, and experiences. The method you choose determines how you will approach that material — which is why selecting the right one matters.

Qualitative content analysis involves systematically examining and interpreting a body of material — such as news articles, social media posts, advertisements, or recorded conversations — to identify views, patterns, assumptions, and derived meanings.

A key feature of content analysis is that the material being analyzed was often not originally created for research purposes. For example, a researcher might analyze twenty years of newspaper coverage of gender roles, or evaluate whether a set of advertisements shows bias towards particular cultural groups.

One widely-used approach within content analysis is summative content analysis, which involves identifying words or phrases and counting their occurrence — or the occurrence of their latent meanings — across the dataset. Units of analysis commonly counted include:

• Words and phrases
• Themes and concepts
• Characters and paragraphs
• Semantic patterns across items

• Your data is documents, media, or text not collected through interviews
• You want to track how a topic has been represented across a large dataset over time
• Your research question involves identifying the frequency or presence of specific ideas

Narrative analysis is a qualitative research approach that focuses on the stories people tell and how they tell them. Rather than breaking data into codes, narrative analysis treats the entire story as the unit of analysis — preserving its structure, sequence, and meaning as a whole.

This makes narrative analysis particularly well-suited to studying human experience through the stories participants share. The data for narrative analysis can come from:

• In-depth interviews
• Field notes and observational records
• Oral histories, letters, and autobiographies
• Blogs and personal diaries

The defining characteristic that separates narrative analysis from other methods is its emphasis on how a story is told, not just what is said. The language used, the way events are sequenced, the cultural context, and the researcher’s own role in shaping the account are all considered part of the analysis.

• Your research explores personal experiences, identity, or life histories
• You want to understand how people make meaning of significant events
• Your research question is about the process of storytelling itself, not just the content
• Preserving chronological sequence and context is important to your analysis

Discourse analysis is a qualitative research approach that investigates language — both written and spoken — as it is used in context. The central assumption is that language is not a neutral tool for transmitting information. Instead, language actively constructs social reality, shapes power relationships, and constitutes ways of understanding the world.

Discourse analysts study not just what is said, but how texts are constructed, the functions they serve in different contexts, and the contradictions and power dynamics running through them. Critical discourse analysis, in particular, examines how language is used by those with power to shape meanings and social practices.

The defining characteristics of discourse analysis include:

• A focus on language use as social practice — not just communication
• Interest in power dynamics, social construction, and who controls the discourse
• Contextual analysis — how cultural, institutional, and social norms shape what is said
• An iterative process of deep immersion in the material across multiple readings

• Your research focuses on language, power, or social meaning
• You are analyzing political speeches, policy documents, media texts, or institutional communications
• Your research question asks how language constructs or reproduces social inequalities
• You have a strong theoretical foundation in linguistics, sociology, or critical theory

Thematic analysis is a flexible and widely used qualitative data analysis approach that focuses on identifying, analyzing, and interpreting patterns of meaning — called themes — within qualitative data. It is widely considered the most accessible starting point for qualitative researchers, which is why I recommend it for most PhD students.

Thematic analysis is not tied to any particular philosophical or theoretical position, and it can be used with both:

• Inductive approaches — where themes emerge directly from the data
• Deductive approaches — where the researcher brings preconceived themes to the data and tests them

The most widely cited framework for thematic analysis is the six-step process developed by Braun and Clarke (2006): data familiarization, generating initial codes, searching for themes, reviewing themes, defining and naming themes, and writing the report.

The defining characteristics of thematic analysis include:

• Systematic yet flexible — adaptable to different research questions and data types
• Works at both semantic (surface) and latent (interpretive) levels of meaning
• Produces themes that represent patterns of shared meaning across the dataset
• Can be used with interviews, focus groups, surveys, field notes, or any text-based data

• You are a beginner — thematic analysis is the most learnable method
• Your research involves interviews or focus groups as the primary data source
• Your research question asks about experiences, perceptions, or patterns in your data
• You want a method that works across different philosophical positions

See my step-by-step guide to thematic analysis in NVivo 15 to see exactly how this method works in practice.

Grounded theory is a systematic inductive approach to qualitative data analysis that aims to develop a new theory directly from the data. Unlike most other methods, grounded theory does not begin with a theoretical framework — the theory itself emerges through the analysis.

 The process is iterative: researchers move back and forth between data collection and analysis, constantly comparing data segments, refining categories, and testing emerging ideas. This continues until theoretical saturation is reached — the point at which new data no longer produces new theoretical insights.

The defining characteristics of grounded theory include:

Theory generation — the final output is a theory grounded in and supported by the data

Constant comparative method — data segments are continuously compared to identify similarities, differences, and patterns

Open, axial, and selective coding — a structured three-stage coding process that builds toward theory

Theoretical sampling — data collection is guided by the emerging theory, not fixed in advance

Memo writing — researchers document their evolving ideas and theoretical reasoning throughout

• Your research aim is to build a new theory rather than describe or interpret existing phenomena
• Little prior theory exists on your topic
• Your research involves complex social processes where understanding the why matters as much as the what
• You have the time and resources for iterative data collection alongside analysis

Interpretive phenomenological analysis is a qualitative research approach that delves into how individuals make sense of their lived experiences, emphasizing the unique perspective of each participant within their specific context.

IPA is characterized by what researchers call a double hermeneutic: the researcher interprets not only the participant’s experience but also the participant’s own interpretation of that experience. There is no claim to an objective truth — the goal is to understand how participants make meaning of events in their own lives.

The defining characteristics of IPA include:

• Focus on lived experience — IPA seeks to understand how individuals experience and make sense of significant life events
• Double hermeneutic — two layers of interpretation: the participant’s and the researcher’s
• Ideographic approach — IPA focuses on the particular, not the generalizable; small samples are the norm
• Semi-structured interviews — the primary data collection method, using open-ended questions
• Reflexivity — researchers actively reflect on their own biases and their influence on the analysis

• Your research explores how individuals make sense of significant or difficult personal experiences
• Your research is in health, psychology, education, or social care
• You are working with a small, purposively selected sample (typically 3–10 participants)
• Your research question focuses on meaning-making at the individual level, not group patterns

Choosing a method comes down to three questions:

1. What is your research question asking? Questions about patterns across a group → thematic analysis or content analysis. Questions about individual lived experience → IPA. Questions about language and power → discourse analysis. Questions about stories → narrative analysis. Questions about building theory → grounded theory.
2. What kind of data do you have? Interview transcripts with multiple participants → thematic analysis. A small number of in-depth personal accounts → IPA or narrative analysis. Media, documents, or text not from interviews → content analysis.
3. What is your experience level? If you are new to qualitative research, start with thematic analysis. It is the most well-documented, the most flexible, and the most forgiving of methodological learning curves.

My consistent advice: if you are a PhD student doing your first piece of qualitative research, use thematic analysis. After you have completed one study using thematic analysis, you will have the grounding to explore more specialized methods.

If you are using NVivo for your analysis, I have step-by-step guides covering the full thematic analysis workflow from coding through to writing your findings.

If you are using MAXQDA and want to understand deductive thematic analysis, I cover that approach in detail as well.

Comparison Table: 6 Qualitative Data Analysis Methods at a Glance

Note: Thematic analysis (highlighted in green) is recommended for most PhD students beginning qualitative research.

Method	Best For	Data Types	Skill Level	Key Feature
Content Analysis	Media, documents, text patterns	Printed text, video, social media	Beginner–Intermediate	Counts patterns & frequencies
Narrative Analysis	Personal stories, life histories	Interviews, autobiographies, blogs	Intermediate	Preserves story structure whole
Discourse Analysis	Language, power, social meaning	Spoken/written language, media	Advanced	Examines how language constructs reality
Thematic Analysis	PhD research, most qualitative studies	Interviews, focus groups, any text	Beginner-friendly ✓	Flexible; inductive or deductive
Grounded Theory	Theory-building studies	Interviews, observations	Advanced	Generates new theory from data
IPA	Lived experience, health/psychology	Semi-structured interviews (small n)	Intermediate–Advanced	Double hermeneutic interpretation

Frequently Asked Questions

What is the easiest qualitative data analysis method for beginners?

Thematic analysis is the most beginner-friendly qualitative data analysis method. It is well-documented, flexible across research questions and philosophical positions, and can be used with the most common data types in PhD research — particularly interview transcripts and focus groups.

Can I use more than one qualitative data analysis method in my thesis?

Yes, but it requires a clear rationale. Researchers occasionally combine methods — for example, using content analysis to examine a large corpus of documents alongside thematic analysis of interview data. However, for most PhD students, one well-applied method is more rigorous than two methods applied superficially.

Is thematic analysis the same as grounded theory?

No. Thematic analysis identifies patterns and themes within data to answer a research question. Grounded theory uses data to build an entirely new theory. Grounded theory involves theoretical sampling, constant comparison, and memo writing in a way that thematic analysis does not. Both involve coding, but for different purposes.

What qualitative data analysis method is best for interview data?

Thematic analysis, narrative analysis, and IPA are all well-suited to interview data, depending on your research question. Thematic analysis works best for identifying patterns across multiple participants. Narrative analysis works best when the story structure matters. IPA works best for in-depth exploration of individual lived experience with a small sample.

Do I need software to do qualitative data analysis?

No — qualitative data analysis can be done manually. However, software such as NVivo, MAXQDA, or ATLAS.ti makes the process significantly faster and more organized, especially for large datasets. I recommend NVivo for most PhD students because it has the most extensive documentation and tutorial support.

Key Takeaways

• There are six main qualitative data analysis methods: content analysis, narrative analysis, discourse analysis, thematic analysis, grounded theory, and IPA
• The right method depends on your research question, your data type, and your experience level
• Thematic analysis is the most flexible and beginner-friendly method — recommended for most PhD students
• Grounded theory and discourse analysis require the most methodological experience
• IPA is best for small samples and studies focused on lived experience
• After mastering thematic analysis, you can branch into more specialized methods as your research develops

Ready to Start Your Qualitative Analysis?

If you are under time pressure or want expert support, I offer a done-for-you thematic analysis service where my team handles your full qualitative analysis — from transcript to finished findings report.

For further reading on qualitative research methods, SAGE Research Methods and Scribbr’s qualitative research guide are both excellent, well-cited resources.