Qualitative data analysis

Analysing qualitative data can seem like a mysterious process to new researchers. There is a large body of qualitative health research literature that at times can feel conflicting.

This resource aims to demystify the key elements of qualitative data analysis. This resource is suitable for emerging researchers who have attended the STaRR Emerging Researcher training, or who have knowledge of the following:

  • What qualitative research is
  • The purpose or role of qualitative health research
  • Some common qualitative research methodologies
  • Common qualitative data collection methods

Some of these foundational concepts are presented in the STaRR Research Design Resource topic.

Get started: watch this video resource

If you are looking for some guidance when planning and conducting your qualitative data analysis, first watch this clip by Dr Olivia King: An introduction to Qualitative Data Analysis.

Click here to watch the 25-minute video, which talks through the information below with reference to examples (Passcode: DM8gmKK&).

Thank you to Dr Ella Ottrey and Alesha Sayner for their contributions to this resource.

Qualitative data comes in many forms. The content of this resource relates to the analysis of textual data. That is, data that has been transcribed from interviews or focus groups or gathered via open-ended survey questions. Existing documents may also be analysed, such as health policies, procedures, or guidelines.

Qualitative data analysis is the process of categorising data conceptually to identify patterns of meaning within and across a dataset. By analysing qualitative data, researchers aim to generate new concepts known as themes from the data or identify the alignment between new data and existing theory.

Here are some key terms and their definitions:

  • Theme: interpretative concept that explains a proportion of the data collected
  • Thematic analysis: the general method that is used to identify and describe patterns (themes) within and across a dataset
  • Code (verb): the process of indexing, labelling, or categorising data
  • Code (noun): a label or short phrase that denotes an interesting or relevant concept (smaller components of themes)
  • Inductive coding: the researcher generates themes from the data itself (i.e., from the bottom up)
  • Deductive coding: the researcher identifies a theory, framework, or themes and codes a priori (before the analysis), which are then used to code the data (i.e., from the top down)
  • Abductive coding: the researcher moves between the data and an a priori theory or framework, which is applied to the dataset flexibly, and developed and redeveloped through the coding process
  • Reflexivity: “a set of continuous, collaborative, and multifaceted practices through which researchers self-consciously critique, appraise, and evaluate how their subjectivity and context influence the research processes” (Olmos-Vega et al., 2023, p. 242)
  • Theory: “an abstract description of the relationships between concepts that help us to understand the world” (Varpio et al., 2020, p. 990)
  • Transcript: data that has been converted from spoken into written form

Qualitative data analysis, particularly inductive analysis, is a subjective process. Researchers bring their own experiences, understandings of the world and existing theories as they analyse and make meaning of the data gathered. There are ways to moderate this subjectivity, such as using an established and structured approach to data analysis, having multiple people contribute to the analysis process, and maintaining reflexive awareness (reflexivity).

Recommended reading:

This is a nice, practical paper about reflexivity:

Here is another practical paper about team reflexivity, including useful prompts to guide the process:

This is a useful paper that describes inductive and deductive approaches to data analysis:

There is no one-size-fits-all approach when it comes to qualitative data analysis. However, your approach to qualitative data analysis must align with the aims of the research, the research questions, and the data collection methods. This is called internal coherence.

To ensure the rigour of your qualitative research, it is important to decide upon and follow an established, structured approach to analysis before starting.

To decide on the most appropriate approach, consider these key questions:

  1. What are the aims of your research? For example, is it to describe, summarise or quantify qualitative data, or infer new meaning to develop new theory? Or somewhere in between?
  2. Will you use an existing theory or framework to guide your analysis, or will you develop codes and themes from the data itself?
  3. How much data do you have? Pages and pages of interview transcripts, or 10 short survey responses?
  4. Will you analyse the data alone, or will multiple researchers be involved?
  5. How much time/capacity do you have to dedicate to data analysis?

Recommended reading:

This is a nice, practical paper about internal coherence:

Four common approaches to qualitative data analysis described in the qualitative research methods literature include:

  1. Reflexive thematic analysis
  2. Framework analysis
  3. Template analysis
  4. Content analysis

These are described briefly below with links to articles which contain more details. If you work for a Western Alliance member organisation and can’t access any of these articles via your health organisation’s library, please contact starrsupport@deakin.edu.au and we can assist you.  

Reflexive thematic analysis

Braun and Clarke’s reflexive thematic analysis is a widely cited approach. It is particularly useful for in-depth analysis or more interpretative research. The role of the researcher is centralised in the reflexive thematic analysis process, which means the researcher is given equal status to the research question and the data itself.

This approach is best suited to:

  • Research questions or aims which require a greater depth of analysis and interpretation of the data (e.g., when developing themes around professional identity)
  • When the researcher has the capacity and capability to immerse themselves in the data and develop themes (i.e., this is resource and labour intensive)


  1. Familiarisation – reading transcripts/listening to audio recordings
  2. Generate initial codes – systematically organising data under the relevant codes
  3. Search for themes – arranging individual codes with shared meaning into potential themes
  4. Review and develop themes – with relevant data and check if it all works/makes sense; develop thematic map
  5. Define and name themes – based on thematic map; clearly define the themes
  6. Produce the report – writing up the “story”

It is important to note:

  • These six steps do not need to be followed strictly from top to bottom. Reflexive thematic analysis is an iterative process and researchers often move back and forth between the steps
  • Reflexivity is at the centre of this type of analysis. Researchers bring their subjectivities and unique interpretations of the data. Attending to and maintaining reflexive awareness is crucial (see notes above on reflexivity).

Recommended reading:

This paper is a longer read, but is useful if conducting reflexive thematic analysis:

This paper provides a neat overview of Braun and Clarke’s reflexive thematic analysis:

Framework analysis

Framework analysis represents a more structured approach to qualitative data analysis in which an analytic framework or “codebook” is produced and then used systematically to code the data.

A new analytic framework can be developed from the data itself (inductive) or a pre-existing (a priori) framework or codebook (deductive). The framework method can also be used to adapt and build on an existing theory or framework through iterative coding and analysis (abductive). Irrespective, the coding framework needs to be detailed, and include definitions of codes and example data.

This approach is best suited to:

  • Research aims and data amenable to varying levels of interpretation
  • Policy and practice-based research
  • Team-based coding and analysis as the framework provides a clear structure and guidance for all analysts to promote consistency

Recommended reading:

This paper provides clear and practical guidance and defines many of the key terms related to qualitative data analysis:

This book chapter provides a detailed description with examples of framework analyses:

  • Ritchie, J. & Spencer, L. (1994). Analysing qualitative data. In A. Bryman & R. Burgess (Eds.), Qualitative Data Analysis for Applied Policy Research (pp. 173–194). London: Routledge

This original research paper includes a very detailed methods section which may be helpful for writing up the methods in a protocol or manuscript:


  1. Transcribe the data
  2. Familiarisation – reading transcripts/listening to audio recordings
  3. Initial coding of a portion of transcripts
  4. Identify an initial thematic framework – develop the coding framework
  5. Index – apply the framework to code all data
  6. Chart – decipher patterns in the data
  7. Mapping and interpretation – consider data in the context of existing literature

Template analysis

Template analysis is a hierarchical approach to analysis with “higher level” and subthemes. It is similar to the framework method; however, a key difference is the greater focus in template analysis, on developing the coding template. This approach provides less guidance around the interpretation of the data.

Template analysis is flexible regarding the style and the format of the template that is produced. There is so much focus on the development and re-development of the template, that this is sometimes mistaken as the output of the process, rather than the synthesis of the data.

This approach is best suited to:

  • Applied health research
  • When a pre-identified or a priori framework or theory is guiding the research process (e.g., the aims, research questions, data collection methods, etc.)
  • Team-based analysis, as all members can contribute to the development of the coding template and to the structured coding processes

Recommended reading:

This practical and clear paper describes the steps and applications of template analysis:

Brooks, J., McCluskey, S., Turley, E., & King, N. (2015). The utility of template analysis in qualitative psychology researchQualitative Research in Psychology12(2), 202-222.


  1. Become familiar with the data – all data in a small study, or a subset in a larger study (i.e., approximately 25% of transcripts or data)
  2. Carry out preliminary coding of the data – highlight any interesting or recurring observations in the data +/- some a priori themes
  3. Organise emerging themes into clusters, and begin to define how they relate to each other within and between these clusters
  4. Define an initial coding template based on a subset of the data
  5. Apply the initial template to further data (i.e., code/organise more data) and modify the template as appropriate
  6. Finalise the template and code the full dataset

Content analysis

Content analysis is another commonly used approach in health research. Content analysis can be an inductive process (conventional content analysis) or a deductive process (directed content analysis). There is a third approach called summative content analysis, which is a quantitative form of analysis and involves counting the number of times a term/word/phrase is used within a dataset. Further “latent analysis” can be conducted to explore the meaning of words or terms in a particular context (e.g., the word “risk” in residential aged care policies and procedures).

This approach is best suited to:

  • Analysing open text survey data
  • Analysing data collected in line with a predetermined framework (e.g., Theoretical Domains Framework, Theory of Planned Behaviour, etc.)
  • Research aims or questions that call for surface level analysis of textual data
  • Research projects that seek to quantify qualitative data (e.g., the proportion of survey respondents who describe parking availability as a barrier to attending a group health program)

Recommended reading:

This paper provides a detailed overview of the three common types of content analysis (conventional, directed, and summative), with examples:

This paper provides a short overview of two types of content analysis (inductive and deductive):

This paper compares content and reflexive thematic analysis:


Conventional content analysis (inductive)

  1. Immersion in the data – read all data
  2. Open code – identify and highlight key words or concepts
  3. Initial analysis – make notes of impressions and thoughts and re-label codes
  4. Categorise codes – organise and group into meaningful clusters (“themes”) and hierarchies; define codes and themes
  5. Report – write up the analysis process and results

Directed content analysis (deductive)

  1. Read data
  2. Identify and define existing theory or a priori themes and codes
  3. Code data using existing codes or theory; generate new codes for data that does not fit
  4. Report – write up the analysis process and results

There are several tools and data analysis software packages to support qualitative data analysis. Some commonly used qualitative data analysis software products include:

These tools can be useful for researchers to code and organise qualitative data, however they do not automatically code data. Also, researchers may or may not have access to these through their organisations. Researchers affiliated with a university may have access to them, while others may not. Speak with your Research Translation Coordinator if you are unsure.

Many qualitative researchers use tables in Microsoft Word, and Excel to organise and code qualitative data. Others use mind mapping tools. There are no hard and fast rules about what tools should be used, and often it comes down to what researchers have access to, and what they prefer.

Further Resources

As you begin to develop your report, manuscript, or presentation, you might like to read this (short) practical paper which includes helpful guidance on selecting and integrating participant quotes into the report:




Below is a list of all sources referred to and used to develop this resource.

  • Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology3(2), 77-101. https://www.tandfonline.com/doi/abs/10.1191/1478088706QP063OA
  • Braun, V., & Clarke, V. (2023). Toward good practice in thematic analysis: Avoiding common problems and be (com) ing a knowing researcher. International Journal of Transgender Health24(1), 1-6.https://doi.org/10.1080/26895269.2022.2129597
  • Byrne, D. (2022). A worked example of Braun and Clarke’s approach to reflexive thematic analysis. Quality & Quantity56(3), 1391-1412. https://doi.org/10.1007/s11135-021-01182-y
  • Davis, C., King, O. A., Clemans, A., Coles, J., Crampton, P. E., Jacobs, N., … & Rees, C. E. (2020). Student dignity during work-integrated learning: a qualitative study exploring student and supervisors’ perspectives. Advances in Health Sciences Education25(1), 149-172. https://link.springer.com/article/10.1007/s10459-019-09914-4
  • King, N. (2012). Doing template analysis. In: G. Symon & C. Cassell (Eds.), Qualitative Organizational Research: Core Methods and Current Challenges (pp. 426-450) London: Sage
  • King, O. (2021). Two sets of qualitative research reporting guidelines: An analysis of the shortfalls. Research in Nursing & Health44(4), 715-723. https://onlinelibrary.wiley.com/doi/abs/10.1002/nur.22157
  • Lingard, L. (2019). Beyond the default colon: effective use of quotes in qualitative research. Perspectives on Medical Education, 8, 360-364. org/10.1007/s40037-019-00550-7
  • Olmos-Vega, F. M., Stalmeijer, R. E., Varpio, L., & Kahlke, R. (2023). A practical guide to reflexivity in qualitative research: AMEE Guide No. 149. Medical Teacher45(3), 241-251. https://www.tandfonline.com/doi/full/10.1080/0142159X.2022.2057287
  • Palermo, C., Reidlinger, D. P., & Rees, C. E. (2021). Internal coherence matters: lessons for nutrition and dietetics research. Nutrition & Dietetics78(3), 252-267. https://doi.org/10.1111/1747-0080.12680
  • Ritchie, J. & Spencer, L. (1994). Analysing qualitative data. In: A. Bryman & R. Burgess (Eds.), Qualitative Data Analysis for Applied Policy Research (pp. 173–194). London: Routledge
  • Terry, G., & Hayfield, N. (2020). Reflexive thematic analysis. In: Ward, MRM. & Delamont, S. (eds). Handbook of Qualitative Research in Education (pp. 430-441). Cheltenham: Edward Elgar Publishing
  • Vaismoradi, M., Turunen, H., & Bondas, T. (2013). Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study. Nursing & Health Sciences15(3), 398-405. https://onlinelibrary.wiley.com/doi/full/10.1111/nhs.12048
  • Varpio, L., Paradis, E., Uijtdehaage, S., & Young, M. (2020). The distinctions between theory, theoretical framework, and conceptual framework. Academic Medicine95(7), 989-994. https://journals.lww.com/academicmedicine/fulltext/2020/07000/The_Distinctions_Between_Theory,_Theoretical.21.aspx