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How to Do Thematic Analysis | Step-by-Step Guide & Examples

Published on September 6, 2019 by Jack Caulfield . Revised on June 22, 2023.

Thematic analysis is a method of analyzing qualitative data . It is usually applied to a set of texts, such as an interview or transcripts . The researcher closely examines the data to identify common themes – topics, ideas and patterns of meaning that come up repeatedly.

There are various approaches to conducting thematic analysis, but the most common form follows a six-step process: familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up. Following this process can also help you avoid confirmation bias when formulating your analysis.

This process was originally developed for psychology research by Virginia Braun and Victoria Clarke . However, thematic analysis is a flexible method that can be adapted to many different kinds of research.

Table of contents

When to use thematic analysis, different approaches to thematic analysis, step 1: familiarization, step 2: coding, step 3: generating themes, step 4: reviewing themes, step 5: defining and naming themes, step 6: writing up, other interesting articles.

Thematic analysis is a good approach to research where you’re trying to find out something about people’s views, opinions, knowledge, experiences or values from a set of qualitative data – for example, interview transcripts , social media profiles, or survey responses .

Some types of research questions you might use thematic analysis to answer:

  • How do patients perceive doctors in a hospital setting?
  • What are young women’s experiences on dating sites?
  • What are non-experts’ ideas and opinions about climate change?
  • How is gender constructed in high school history teaching?

To answer any of these questions, you would collect data from a group of relevant participants and then analyze it. Thematic analysis allows you a lot of flexibility in interpreting the data, and allows you to approach large data sets more easily by sorting them into broad themes.

However, it also involves the risk of missing nuances in the data. Thematic analysis is often quite subjective and relies on the researcher’s judgement, so you have to reflect carefully on your own choices and interpretations.

Pay close attention to the data to ensure that you’re not picking up on things that are not there – or obscuring things that are.

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Once you’ve decided to use thematic analysis, there are different approaches to consider.

There’s the distinction between inductive and deductive approaches:

  • An inductive approach involves allowing the data to determine your themes.
  • A deductive approach involves coming to the data with some preconceived themes you expect to find reflected there, based on theory or existing knowledge.

Ask yourself: Does my theoretical framework give me a strong idea of what kind of themes I expect to find in the data (deductive), or am I planning to develop my own framework based on what I find (inductive)?

There’s also the distinction between a semantic and a latent approach:

  • A semantic approach involves analyzing the explicit content of the data.
  • A latent approach involves reading into the subtext and assumptions underlying the data.

Ask yourself: Am I interested in people’s stated opinions (semantic) or in what their statements reveal about their assumptions and social context (latent)?

After you’ve decided thematic analysis is the right method for analyzing your data, and you’ve thought about the approach you’re going to take, you can follow the six steps developed by Braun and Clarke .

The first step is to get to know our data. It’s important to get a thorough overview of all the data we collected before we start analyzing individual items.

This might involve transcribing audio , reading through the text and taking initial notes, and generally looking through the data to get familiar with it.

Next up, we need to code the data. Coding means highlighting sections of our text – usually phrases or sentences – and coming up with shorthand labels or “codes” to describe their content.

Let’s take a short example text. Say we’re researching perceptions of climate change among conservative voters aged 50 and up, and we have collected data through a series of interviews. An extract from one interview looks like this:

In this extract, we’ve highlighted various phrases in different colors corresponding to different codes. Each code describes the idea or feeling expressed in that part of the text.

At this stage, we want to be thorough: we go through the transcript of every interview and highlight everything that jumps out as relevant or potentially interesting. As well as highlighting all the phrases and sentences that match these codes, we can keep adding new codes as we go through the text.

After we’ve been through the text, we collate together all the data into groups identified by code. These codes allow us to gain a a condensed overview of the main points and common meanings that recur throughout the data.

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Next, we look over the codes we’ve created, identify patterns among them, and start coming up with themes.

Themes are generally broader than codes. Most of the time, you’ll combine several codes into a single theme. In our example, we might start combining codes into themes like this:

At this stage, we might decide that some of our codes are too vague or not relevant enough (for example, because they don’t appear very often in the data), so they can be discarded.

Other codes might become themes in their own right. In our example, we decided that the code “uncertainty” made sense as a theme, with some other codes incorporated into it.

Again, what we decide will vary according to what we’re trying to find out. We want to create potential themes that tell us something helpful about the data for our purposes.

Now we have to make sure that our themes are useful and accurate representations of the data. Here, we return to the data set and compare our themes against it. Are we missing anything? Are these themes really present in the data? What can we change to make our themes work better?

If we encounter problems with our themes, we might split them up, combine them, discard them or create new ones: whatever makes them more useful and accurate.

For example, we might decide upon looking through the data that “changing terminology” fits better under the “uncertainty” theme than under “distrust of experts,” since the data labelled with this code involves confusion, not necessarily distrust.

Now that you have a final list of themes, it’s time to name and define each of them.

Defining themes involves formulating exactly what we mean by each theme and figuring out how it helps us understand the data.

Naming themes involves coming up with a succinct and easily understandable name for each theme.

For example, we might look at “distrust of experts” and determine exactly who we mean by “experts” in this theme. We might decide that a better name for the theme is “distrust of authority” or “conspiracy thinking”.

Finally, we’ll write up our analysis of the data. Like all academic texts, writing up a thematic analysis requires an introduction to establish our research question, aims and approach.

We should also include a methodology section, describing how we collected the data (e.g. through semi-structured interviews or open-ended survey questions ) and explaining how we conducted the thematic analysis itself.

The results or findings section usually addresses each theme in turn. We describe how often the themes come up and what they mean, including examples from the data as evidence. Finally, our conclusion explains the main takeaways and shows how the analysis has answered our research question.

In our example, we might argue that conspiracy thinking about climate change is widespread among older conservative voters, point out the uncertainty with which many voters view the issue, and discuss the role of misinformation in respondents’ perceptions.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

  • Normal distribution
  • Measures of central tendency
  • Chi square tests
  • Confidence interval
  • Quartiles & Quantiles
  • Cluster sampling
  • Stratified sampling
  • Discourse analysis
  • Cohort study
  • Peer review
  • Ethnography

Research bias

  • Implicit bias
  • Cognitive bias
  • Conformity bias
  • Hawthorne effect
  • Availability heuristic
  • Attrition bias
  • Social desirability bias

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Chapter 22: Thematic Analysis

Darshini Ayton

Learning outcomes

Upon completion of this chapter, you should be able to:

  • Describe the different approaches to thematic analysis.
  • Understand how to conduct the three types of thematic analysis.
  • Identify the strengths and limitations of each type of thematic analysis.

What is thematic analysis?

Thematic analysis is a common method used in the analysis of qualitative data to identify, analyse and interpret meaning through a systematic process of generating codes (see Chapter 20) that leads to the development of themes. 1 Thematic analysis requires the active engagement of the researcher with the data, in a process of sorting, categorising and interpretation. 1 Thematic analysis is exploratory analysis whereby codes are not predetermined and are data-derived, usually from primary sources of data (e,g, interviews and focus groups). This is in contrast to themes generated through directed or summative content analysis, which is considered confirmatory hypothesis-driven analysis, with predetermined codes typically generated from a hypothesis (see Chapter 21). 2 There are many forms of thematic analysis. Hence, it is important to treat thematic analysis as one of many methods of analysis, and to justify the approach on the basis of the research question and pragmatic considerations such as resources, time and audience. The three main forms of thematic analysis used in health and social care research, discussed in this chapter, are:

Applied thematic analysis

  • Framework analysis
  • Reflexive thematic analysis.

This involves multiple, inductive analytic techniques designed to identify and examine themes from textual data in a way that is transparent and credible, drawing from a broad range of theoretical and methodological perspectives. It focuses on presenting the stories of participants as accurately and comprehensively as possible. Applied thematic analysis mixes a bit of everything: grounded theory, positivism, interpretivism and phenomenology. 2

Applied thematic analysis borrows what we feel are the more useful techniques from each theoretical and methodological camp and adapts them to an applied research context. 2(p16)

Applied thematic analysis involves five elements:

  • Text s egmentation  involves identifying a meaningful segment of text and the boundaries of the segment. Text segmentation is a useful process as a transcript from a 30-minute interview can be many pages long. Hence, segmenting the text provides a manageable section of the data for interrogation of meaning. For example, text segmentation may be a participant’s response to an interview question, a keyword or concept in context, or a complete discourse between participants. The segment of text is more than a short phrase and can be both small and large sections of text. Text segments can also overlap, and a smaller segment may be embedded within a larger segment. 3
  • Creation of the codebook is a critical element of applied thematic analysis. The codebook is created when the segments of text are systematically coded into categories, types and relationships, and the codes are defined by the observed meaning in the text. The codes and their definitions are descriptive in the beginning, and then evolve into explanatory codes as the researcher examines the commonalities, differences and relationships between the codes. The codebook is an iterative document that the researcher builds and refines as they become more immersed and familiar with the data. 3 Table 22.1 outlines the key components of a codebook. 3

Table 22.1. Codebook components and an example

  • Structural coding can be useful if a structured interview guide or focus group guide has been used by the researcher and the researcher stays close to the wording of the question and its prompts. The structured question is the structural code in the codebook, and the text segment should include the participant’s response and any dialogue following the question. Of course, this form of coding can be used even if the researcher does not follow a structured guide, which is often the reality of qualitative data collection. The relevant text segments are coded for the specific structure, as appropriate. 3
  • Content coding is informed by the research question(s) and the questions informing the analysis. The segmented text is grouped in different ways to explore relationships, hierarchies, descriptions and explanations of events, similarities, differences and consequences. The content of the text segment should be read and re-read to identify patterns and meaning, with the generated codes added to the codebook.
  • Themes vary in scope, yet at the core they are phrases or statements that explain the meaning of the text. Researchers need to be aware that themes are considered a higher conceptual level than codes, and therefore should not be comprised of single words or labels. Typically, multiple codes will lead to a theme. Revisiting the research and analysis questions will assist the researcher to identify themes. Through the coding process, the researcher actively searches the data for themes. Examples of how themes may be identified include the repetition of concepts within and across transcripts, the use of metaphors and analogies, key phrases and common phrases used in an unfamiliar way. 3

Framework a nalysis

This method originated in the 1980s in social policy research. Framework analysis is suited to research seeking to answer specific questions about a problem or issue, within a limited time frame and with homogenous data (in topics, concepts and participants); multiple researchers are usually involved in the coding process. 4-6 The process of framework analysis is methodical and suits large data sets, hence is attractive to quantitative researchers and health services researchers. Framework analysis is useful for multidisciplinary teams in which not all members are familiar with qualitative analysis. Framework analysis does not seek to generate theory and is not aligned with any particular epistemological, philosophical or theoretical approach. 5 The output of framework analysis is a matrix with rows (cases), columns (codes) and cells of summarised data that enables researchers to analyse the data case by case and code by code. The case is usually an individual interview, or it can be a defined group or organisation. 5

The process for conducting framework analysis is as follows 5 :

1. Transcription – usually verbatim transcription of the interview.

2. Familiarisation with the interview – reading the transcript and listening to the audio recording (particularly if the researcher doing the analysis did not conduct the interview) can assist in the interpretation of the data. Notes on analytical observations, thoughts and impressions are made in the margins of the transcript during this stage.

3. Coding – completed in a line-by-line method by at least two researchers from different disciplines (or with a patient or public involvement representative), where possible. Coding can be both deductive – (using a theory or specific topics relevant to the project – or inductive, whereby open coding is applied to elements such as behaviours, incidents, values, attitudes, beliefs, emotions and participant reactions. All data is coded.

4. Developing a working analytical framework – codes are collated and organised into categories, to create a structure for summarising or reducing the data.

5. Applying the analytical framework – indexing the remaining transcripts by using the categories and codes of the analytical framework.

6. Charting data into the framework matrix – summarising the data by category and from each transcript into the framework matrix, which is a spreadsheet with numbered cells in which summarised data are entered by codes (columns) and cases (rows). Charting needs to balance the reduction of data to a manageable few lines and retention of the meaning and ‘feel’ of the participant. References to illustrative quotes should be included.

7. Interpreting the data – using the framework matrix and notes taken throughout the analysis process to interpret meaning, in collaboration with team members, including lay and clinical members.

Reflexive thematic analysis

This is the thematic analysis approach developed by Braun and Clarke in 2006 and explained in the highly cited article ‘ Using thematic analysis in psychology ’ . 7 Reflexive thematic analysis recognises the subjectiveness of the analysis process, and that codes and themes are actively generated by the researcher. Hence, themes and codes are influenced by the researcher’s values, skills and experiences. 8 Reflexive thematic analysis ‘exists at the intersection of the researcher, the dataset and the various contexts of interpretation’. 9(line 5-6) In this method, the coding process is less structured and more organic than in applied thematic analysis. Braun and Clarke have been critical of the use of the term ‘emerging themes’, which many researchers use to indicate that the theme was data-driven, as opposed to a deductive approach:

This language suggests that meaning is self evident and somehow ‘within’ the data waiting to be revealed, and that the researcher is a neutral conduit for the revelation of said meaning. In contrast, we conceptualise analysis as a situated and interactive process, reflecting both the data, the positionality of the researcher, and the context of the research itself… it is disingenuous to evoke a process whereby themes simply emerge, instead of being active co-productions on the part of the researcher, the data/participants and context. 10 (p15)

Since 2006, Braun and Clarke have published extensively on reflexive thematic analysis, including a methodological paper comparing reflexive thematic analysis with other approaches to qualitative analysis, 8 and have provided resources on their website to support researchers and students. 9 There are many ways to conduct reflexive thematic analysis, but the six main steps in the method are outlined following. 9 Note that this is not a linear, prescriptive or rule-based process, but rather an approach to guide researchers in systematically and robustly exploring their data.

1.  Familiarisation with data – involves reading and re-reading transcripts so that the researcher is immersed in the data. The researcher makes notes on their initial observations, interpretations and insights for both the individual transcripts and across all the transcripts or data sources.

2.  Coding – the process of applying succinct labels (codes) to the data in a way that captures the meaning and characteristics of the data relevant to the research question. The entire data set is coded in numerous rounds; however, unlike line-by-line coding in grounded theory (Chapter 27), or data segmentation in applied thematic analysis, not all sections of data need to be coded. 8 After a few rounds of coding, the codes are collated and relevant data is extracted.

3.  Generating initial themes – using the collated codes and extracted data, the researcher identifies patterns of meaning (initial or potential themes). The researcher then revisits codes and the data to extract relevant data for the initial themes, to examine the viability of the theme.

4 .  Developing and reviewing themes – checking the initial themes against codes and the entire data set to assess whether it captures the ‘story’ of the data and addresses the research question. During this step, the themes are often reworked by combining, splitting or discarding. For reflexive thematic analysis, a theme is defined as a ‘pattern of shared meaning underpinned by a central concept or idea’. 8 (p 39 )

5.  Refining, defining and naming themes – developing the scope and boundaries of the theme, creating the story of the theme and applying an informative name for the theme.

6.  Writing up – is a key part of the analysis and involves writing the narrative of the themes, embedding the data and providing the contextual basis for the themes in the literature.

Themes versus c odes

As described above, themes are informed by codes, and themes are defined at a conceptually higher level than codes. Themes are broader categorisations that tend to describe or explain the topic or concept. Themes need to extend beyond the code and are typically statements that can stand alone to describe and/or explain the data. Fereday and Muir-Cochrane explain this development from code to theme in Table 22.2. 11

Table 22.2. Corroborating and legitimating coded themes to identify second-order themes

*Note: This table is from an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

When I [the author] first started publishing qualitative research, many of my themes were at the code level. I then got advice that when the themes are the subheadings of the results section of my paper, they should tell the story of the research. The difference in my theme naming can be seen when comparing a paper from my PhD thesis, 12 which explores the challenges of church-based health promotion, with a more recent paper that I published on antimicrobial stewardship 13 (refer to the theme tables in the publications).

Table 22.3. Examples of thematic analysis

Advantages and challenges of thematic analysis.

Thematic analysis is flexible and can be used to analyse small and large data sets with homogenous and heterogenous samples. Thematic analysis can be applied to any type of data source, from interviews and focus groups to diary entries and online discussion forums. 1 Applied thematic analysis and framework analysis are accessible approaches for non-qualitative researchers or beginner researchers. However, the flexibility and accessibility of thematic analysis can lead to limitations and challenges when thematic analysis is misapplied or done poorly. Thematic analysis can be more descriptive than interpretive if not properly anchored in a theoretical framework. 1 For framework analysis, the spreadsheet matrix output can lead to quantitative researchers inappropriately quantifying the qualitative data. Therefore, training and support from a qualitative researcher with the appropriate expertise can help to ensure that the interpretation of the data is meaningful. 5

Thematic analysis is a family of analysis techniques that are flexible and inductive and involve the generation of codes and themes. There are three main types of thematic analysis: applied thematic analysis, framework analysis and reflexive thematic analysis. These approaches span from structured coding to organic and unstructured coding for theme development. The choice of approach should be guided by the research question, the research design and the available resources and skills of the researcher and team.

  • Clarke V, Braun V. Thematic analysis. J Posit Psychol . 2017;12(3):297-298. doi:10.1080/17439760.2016.1262613
  • Guest G, MacQueen KM, Namey EE. Introduction to applied thematic analysis. In: Guest G, MacQueen, K.M., Namey, E.E., ed. Applied thematic analysis . SAGE Publications, Inc.; 2014. Accessed September 18, 2023. https://methods.sagepub.com/book/applied-thematic-analysis
  • Guest G, MacQueen, K.M., Namey, E.E.,. Themes and Codes. In: Guest G, MacQueen, K.M., Namey, E.E., ed. Applied thematic analysis . SAGE Publications, Inc.; 2014. Accessed September 18, 2023. https://methods.sagepub.com/book/applied-thematic-analysis
  • Srivastava A, Thomson SB. Framework analysis: A qualitative methodology for applied policy research. Journal of Administration and Governance . 2009;72(3). Accessed September 14, 2023. https://ssrn.com/abstract=2760705
  • Gale NK, Heath G, Cameron E, Rashid S, Redwood S. Using the framework method for the analysis of qualitative data in multi-disciplinary health research. BMC Med Res Methodol . 2013;13:117. doi:10.1186/1471-2288-13-117
  • Smith J, Firth J. Qualitative data analysis: the framework approach. Nurse Res . 2011;18(2):52-62. doi:10.7748/nr2011.01.18.2.52.c8284
  • Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol . 2006;3(2):77-101. doi:10.1191/1478088706qp063oa
  • Braun V, Clarke V. Can I use TA? Should I use TA? Should I not use TA? Comparing reflexive thematic analysis and other pattern-based qualitative analytic approaches. Couns Psychother Res . 2021;21(1):37-47. doi:10.1002/capr.12360
  • Braun V, Clarke V. Thematic analysis. University of Auckland. Accessed September 18, 2023. https://www.thematicanalysis.net/
  • Braun V, Clarke V. Answers to frequently asked questions about thematic analysis. University of Auckland. Accessed September 18, 2023. https://www.thematicanalysis.net/faqs/
  • Fereday J, Muir-Cochrane E. Demonstrating Rigour Using Thematic Analysis: A Hybrid Approach of Inductive and Deductive Coding and Theme Development. International Journal of Qualitative Methods . 2006;5(1):80-92. doi: 10.1177/160940690600500107
  • Ayton D, Manderson L, Smith BJ. Barriers and challenges affecting the contemporary church’s engagement in health promotion. Health Promot J Austr . 2017;28(1):52-58. doi:10.1071/HE15037
  • Ayton D, Watson E, Betts JM, et al. Implementation of an antimicrobial stewardship program in the Australian private hospital system: qualitative study of attitudes to antimicrobial resistance and antimicrobial stewardship. BMC Health Serv Res . 2022;22(1):1554. doi:10.1186/s12913-022-08938-8
  • McKenna-Plumley PE, Graham-Wisener L, Berry E, Groarke JM. Connection, constraint, and coping: A qualitative study of experiences of loneliness during the COVID-19 lockdown in the UK. PLoS One . 2021;16(10):e0258344. doi:10.1371/journal.pone.0258344
  • Dickinson BL, Gibson K, VanDerKolk K, et al. “It is this very knowledge that makes us doctors”: an applied thematic analysis of how medical students perceive the relevance of biomedical science knowledge to clinical medicine. BMC Med Educ . 2020;20(1):356. doi:10.1186/s12909-020-02251-w
  • Bunzli S, O’Brien P, Ayton D, et al. Misconceptions and the acceptance of evidence-based nonsurgical interventions for knee osteoarthritis. A Qualitative Study. Clin Orthop Relat Res . 2019;477(9):1975-1983. doi:10.1097/CORR.0000000000000784

Qualitative Research – a practical guide for health and social care researchers and practitioners Copyright © 2023 by Darshini Ayton is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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18.4 Thematic analysis

Learning objectives.

Learners will be able to…

  • Explain defining features of thematic analysis as a strategy for qualitative data analysis and identify when it is most effectively used
  • Formulate an initial thematic analysis plan (if appropriate for your research proposal)

What are you trying to accomplish with thematic analysis?

As its name suggests, with thematic analysis we are attempting to identify themes or common ideas across our data. Themes can help us to:

  • Determine shared meaning or significance of an event
  • Povide a more complete understanding of concept or idea by exposing different dimensions of the topic
  • Explore a range of values, beliefs or perceptions on a given topic

Themes help us to identify common ways that people are making sense of their world. Let’s say that you are studying empowerment of older adults in assisted living facilities by interviewing residents in a number of these facilities. As you review your transcripts, you note that a number of participants are talking about the importance of maintaining connection to previous aspects of their life (e.g. their mosque, their Veterans of Foreign Wars (VFW) Post, their Queer book club) and having input into how the facility is run (e.g. representative on the board, community town hall meetings). You might note that these are two emerging themes in your data. After you have deconstructed your data, you will likely end up with a handful (likely three or four) central ideas or take-aways that become the themes or major findings of your research.

Variations in approaches to thematic analysis

There are a variety of ways to approach qualitative data analysis, but even within the broad approach of thematic analysis, there is variation. Some thematic analysis takes on an inductive analysis approach. In this case, we would first deconstruct our data into small segments representing distinct ideas (this is explained further in the section below on coding data). We then go on to see which of these pieces seem to group together around common ideas.

In direct contrast, you might take a deductive analysis approach (like we discussed in Chapter 8 ), in which you start with some idea about what grouping might look like and we see how well our data fits into those pre-identified groupings. These initial deductive groupings (we call these a priori categories) often come from an existing theory related to the topic we are studying. You may also elect to use a combination of deductive and inductive strategies, especially if you find that much of your data is not fitting into deductive categories and you decide to let new categories inductively emerge.

A couple things to note here. If you are using a deductive approach, be clear in specifying where your a priori categories came from. For instance, perhaps you are interested in studying the conceptualization of social work in other cultures. You begin your analysis with prior research conducted by Tracie Mafile’o (2004) that identified the concepts of fekau’aki (connecting) and fakatokilalo (humility) as being central to Tongan social work practice. [1] You decide to use these two concepts as part of your initial deductive framework, because you are interested in studying a population that shares much in common with the Tongan people. When using an inductive approach, you need to plan to use memoing and reflexive journaling to document where the new categories or themes are coming from.

Coding data

Coding is the process of breaking down your data into smaller meaningful units. Just like any story is made up by the bringing together of many smaller ideas, you need to uncover and label these smaller ideas within each piece of your data. After you have reviewed each piece of data you will go back and assign labels to words, phrases, or pieces of data that represent separate ideas that can stand on their own. Identifying and labeling codes can be tricky. When attempting to locate units of data to code, look for pieces of data that seem to represent an idea in-and-of-itself; a unique thought that stands alone. For additional information about coding, check out this brief video from Duke’s Social Science Research Institute on this topic. It offers a nice concise overview of coding and also ties into our previous discussion of memoing to help encourage rigor in your analysis process.

As suggested in the video [2] , when you identify segments of data and are considering what to label them ask yourself:

  • How does this relate to/help to answer my research question?
  • How does this connect with what we know from the existing literature?
  • How does this fit (or contrast) with the rest of my data?

You might do the work of coding in the margins if you are working with hard copies, or you might do this through the use of comments or through copying and pasting if you are working with digital materials (like pasting them into an excel sheet, as in the example below). If you are using a CAQDAS, there will be a function(s) built into the software to accomplish this.

Regardless of which strategy you use, the central task of thematic analysis is to have a way to label discrete segments of your data with a short phrase that reflects what it stands for. As you come across segments that seem to mean the same thing, you will want to use the same code. Make sure to select the words to represent your codes wisely, so that they are clear and memorable. When you are finished, you will likely have hundreds (if not thousands!) of different codes – again, a story is made up of many different ideas and you are bringing together many different stories! A cautionary note, if you are physically manipulating your data in some way, for example copying and pasting, which I frequently do, you need to have a way to trace each code or little segment back to its original home (the artifact that it came from).

When I’m working with interview data, I will assign each interview transcript a code and use continuous line numbering. That way I can label each segment of data or code with a corresponding transcript code and line number so I can find where it came from in case I need to refer back to the original.

The following is an excerpt from a portion of an autobiographical memoir (Wolf, 2010) [3] . Continuous numbers have been added to the transcript to identify line numbers (Figure 18.4). A few preliminary codes have been identified from this data and entered into a data matrix (below) with information to trace back to the raw data (transcript) (Figure 19.1).

  • Mafile'o, T. (2004). Exploring Tongan Social Work: Fekau'aki (Connecting) and Fakatokilalo (Humility). Qualitative Social Work, 3 (3), 239-257. ↵
  • Duke Mod U Social Science Research Institute. (2016, November 11). How to know you are coding correct: Qualitative research methods. [Video]. YouTube. https://www.youtube.com/watch?v=iL7Ww5kpnIM&feature=youtu.be ↵
  • Wolf, H. R. (2010). Growing up in New York City: A generational memoir (1941-1960). American Studies Journal, 54. http://www.asjournal.org/54-2010/growing-up-in-new-york-city/ ↵

Thematic analysis is an approach to qualitative analysis, in which the researcher attempts to identify themes or patterns across their data to better understand the topic being studied.

An approach to data analysis in which we gather our data first and then generate a theory about its meaning through our analysis.

The act of breaking piece of qualitative data apart during the analysis process to discern meaning and ultimately, the results of the study.

Part of the qualitative data analysis process where we begin to interpret and assign meaning to the data.

An approach to data analysis in which the researchers begins their analysis using a theory to see if their data fits within this theoretical framework (tests the theory).

Categories that we use that are determined ahead of time, based on existing literature/knowledge.

A data matrix is a tool used by researchers to track and organize data and findings during qualitative analysis.

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Thematic Analysis in Qualitative Research

The popularity of qualitative methods in social science research is a well-noted and most welcomed fact. Thematic analysis, the often-used methods of qualitative research, provides concise description and interpretation in terms of themes and patterns from a data set. The application of thematic analysis requires trained expertise and should not be used in a prescriptive, linear, and inflexible manner while analyzing data. It should rather be implemented in relation to research question and data availability. To ensure its proper usage, Braun and Clarke have propounded the simplest yet effective six-step method to conduct thematic analysis. In spite of its systematic step-driven process, thematic analysis provides core skills to conduct different other forms of qualitative analysis. Thematic analysis, through its theoretical freedom, flexibility, rich and detailed yet complex analytical account has emerged as the widely used and most effective qualitative research tool in social and organizational context.

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Principled Machine Learning Using the Super Learner: An Application to Predicting Prison Violence

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Introduction

This book investigates the relationship between the quantitative and qualitative research traditions in the social sciences, with a particular focus on political science and sociology. It argues that the two traditions are alternative cultures with distinctive research procedures and practices, each having its own values, beliefs, and norms. The book considers the ways in which the traditions differ in terms of methodology, such as type of research question, mode of data analysis, and method of inference. It suggests that the two traditions draw on alternative mathematical foundations: quantitative research is grounded in inferential statistics (that is, probability and statistical theory), whereas qualitative research is (often implicitly) rooted in logic and set theory. This chapter discusses the book's approach to characterizing and comparing the two cultures of social science research and explains what is distinctive about qualitative research.

Feminist Qualitative Research

Feminist research is described in terms of its purposes of addressing women’s lives, advocacy for women, analysis of gender oppression, working for social justice, and transformation of society. Feminist critiques of social science research are reviewed in relation to the development of methodological and epistemological positions. Feminist research is viewed as contributing to the transformation of science from empiricism to postmodernism. Reflexivity, collaboration, power analysis, and advocacy are discussed as common practices of feminist qualitative research. Several qualitative approaches to research are described in relation to feminist research goals, with illustrations of feminist research included. Validity and voice are identified as particular challenges in the conduct of feminist qualitative research. Intersectionality and double consciousness are reviewed as feminist contributions to the transformation of science. Some emerging and innovative forms of feminist qualitative research are highlighted in relation to potential future directions.

Literature Review

This chapter highlights literature review. Reviewing the published literature is one of the key activities of social science research, as a way to position one’s academic contribution, but also to get a bird’s eye view of what the relevant literature says on a given topic or research question. Many guides have been created to assist academic researchers and students in conducting a literature review, but there is no consensus on the most appropriate method to do so. One of the reasons for this lack of consensus is the plurality of epistemological attitudes that coexist in the social sciences. Before initiating a literature review, the researcher should start by clarifying the need for and the purpose of the review. Once this has been clarified, the actual review protocol, tools, and databases to be used will need to be determined to strike a balance between the scope of the study and the depth of the review.

5. Finding Answers: Theories and How to Apply Them

This chapter shows how to develop an answer to a particular research question. It first considers the requirements and components of an answer to a research question before discussing the role of ‘theory’ in social science research, what a ‘theoretical framework’ is, and what a hypothesis is. It then explores the three components of a hypothesis: an independent variable, a dependent variable, and a proposition (a statement about the relationship between the variables). It also looks at the different types of hypotheses and how they guide various kinds of research. It also explains why conceptual and operational definitions of key terms are important and how they are formulated. Finally, it offers suggestions on how to answer normative questions.

The ride-along: a journey in qualitative research

Purpose The purpose of this paper is to show why and how the “ride-along” can add great value to qualitative research. Design/methodology/approach The paper is primarily based on ethnographic research into food systems that the author carried out in Tanzania and draws on other research experience and existing literature on the “go-along” and “walk-along”. Findings Transport choices are made in all social science research and therefore deserve greater attention in research design. Transport will influence how the researcher is perceived and what they will experience and find. The ride-along, when done well, minimises the risks and adds value to qualitative research. Practical implications Researchers need to be reflexive about transport choices and give them greater consideration in research design and practice. The examples from field experience and the considerations identified in this paper will assist researchers and their supervisors in this process. Originality/value Despite the ubiquity of mobility in social science research, there is surprisingly little literature on the subject, especially related to the use of different modes of transport. The originality is in elaborating the importance of the ride-along and the value is in the clearly identified lessons for qualitative research methodology teaching and practice.

Feminist research is described in terms of its purposes of knowledge about women’s lives, advocacy for women, analysis of gender oppression, and transformation of society. Feminist critiques of social science research are reviewed in relation to the development of methodological and epistemological positions. Feminist research is viewed as contributing to the transformation of science from empiricism to postmodernism. Reflexivity, collaboration, power analysis, and advocacy are discussed as common practices of feminist qualitative research. Several qualitative approaches to research are described in relation to feminist research goals, with illustrations of feminist research included. Validity and voice are identified as particular challenges in the conduct of feminist qualitative research. Intersectionality and double consciousness are reviewed as feminist contributions to transformation of science. Some emerging and innovative forms of feminist qualitative research are highlighted in relation to potential future directions.

A Review of “An Introduction to Qualitative Research Synthesis: Managing the Information Explosion in Social Science Research”

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Practical thematic analysis: a guide for multidisciplinary health services research teams engaging in qualitative analysis

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  • Peer review
  • Catherine H Saunders , scientist and assistant professor 1 2 ,
  • Ailyn Sierpe , research project coordinator 2 ,
  • Christian von Plessen , senior physician 3 ,
  • Alice M Kennedy , research project manager 2 4 ,
  • Laura C Leviton , senior adviser 5 ,
  • Steven L Bernstein , chief research officer 1 ,
  • Jenaya Goldwag , resident physician 1 ,
  • Joel R King , research assistant 2 ,
  • Christine M Marx , patient associate 6 ,
  • Jacqueline A Pogue , research project manager 2 ,
  • Richard K Saunders , staff physician 1 ,
  • Aricca Van Citters , senior research scientist 2 ,
  • Renata W Yen , doctoral student 2 ,
  • Glyn Elwyn , professor 2 ,
  • JoAnna K Leyenaar , associate professor 1 2
  • on behalf of the Coproduction Laboratory
  • 1 Dartmouth Health, Lebanon, NH, USA
  • 2 Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA
  • 3 Center for Primary Care and Public Health (Unisanté), Lausanne, Switzerland
  • 4 Jönköping Academy for Improvement of Health and Welfare, School of Health and Welfare, Jönköping University, Jönköping, Sweden
  • 5 Highland Park, NJ, USA
  • 6 Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St Louis, MO, USA
  • Correspondence to: C H Saunders catherine.hylas.saunders{at}dartmouth.edu
  • Accepted 26 April 2023

Qualitative research methods explore and provide deep contextual understanding of real world issues, including people’s beliefs, perspectives, and experiences. Whether through analysis of interviews, focus groups, structured observation, or multimedia data, qualitative methods offer unique insights in applied health services research that other approaches cannot deliver. However, many clinicians and researchers hesitate to use these methods, or might not use them effectively, which can leave relevant areas of inquiry inadequately explored. Thematic analysis is one of the most common and flexible methods to examine qualitative data collected in health services research. This article offers practical thematic analysis as a step-by-step approach to qualitative analysis for health services researchers, with a focus on accessibility for patients, care partners, clinicians, and others new to thematic analysis. Along with detailed instructions covering three steps of reading, coding, and theming, the article includes additional novel and practical guidance on how to draft effective codes, conduct a thematic analysis session, and develop meaningful themes. This approach aims to improve consistency and rigor in thematic analysis, while also making this method more accessible for multidisciplinary research teams.

Through qualitative methods, researchers can provide deep contextual understanding of real world issues, and generate new knowledge to inform hypotheses, theories, research, and clinical care. Approaches to data collection are varied, including interviews, focus groups, structured observation, and analysis of multimedia data, with qualitative research questions aimed at understanding the how and why of human experience. 1 2 Qualitative methods produce unique insights in applied health services research that other approaches cannot deliver. In particular, researchers acknowledge that thematic analysis is a flexible and powerful method of systematically generating robust qualitative research findings by identifying, analysing, and reporting patterns (themes) within data. 3 4 5 6 Although qualitative methods are increasingly valued for answering clinical research questions, many researchers are unsure how to apply them or consider them too time consuming to be useful in responding to practical challenges 7 or pressing situations such as public health emergencies. 8 Consequently, researchers might hesitate to use them, or use them improperly. 9 10 11

Although much has been written about how to perform thematic analysis, practical guidance for non-specialists is sparse. 3 5 6 12 13 In the multidisciplinary field of health services research, qualitative data analysis can confound experienced researchers and novices alike, which can stoke concerns about rigor, particularly for those more familiar with quantitative approaches. 14 Since qualitative methods are an area of specialisation, support from experts is beneficial. However, because non-specialist perspectives can enhance data interpretation and enrich findings, there is a case for making thematic analysis easier, more rapid, and more efficient, 8 particularly for patients, care partners, clinicians, and other stakeholders. A practical guide to thematic analysis might encourage those on the ground to use these methods in their work, unearthing insights that would otherwise remain undiscovered.

Given the need for more accessible qualitative analysis approaches, we present a simple, rigorous, and efficient three step guide for practical thematic analysis. We include new guidance on the mechanics of thematic analysis, including developing codes, constructing meaningful themes, and hosting a thematic analysis session. We also discuss common pitfalls in thematic analysis and how to avoid them.

Summary points

Qualitative methods are increasingly valued in applied health services research, but multidisciplinary research teams often lack accessible step-by-step guidance and might struggle to use these approaches

A newly developed approach, practical thematic analysis, uses three simple steps: reading, coding, and theming

Based on Braun and Clarke’s reflexive thematic analysis, our streamlined yet rigorous approach is designed for multidisciplinary health services research teams, including patients, care partners, and clinicians

This article also provides companion materials including a slide presentation for teaching practical thematic analysis to research teams, a sample thematic analysis session agenda, a theme coproduction template for use during the session, and guidance on using standardised reporting criteria for qualitative research

In their seminal work, Braun and Clarke developed a six phase approach to reflexive thematic analysis. 4 12 We built on their method to develop practical thematic analysis ( box 1 , fig 1 ), which is a simplified and instructive approach that retains the substantive elements of their six phases. Braun and Clarke’s phase 1 (familiarising yourself with the dataset) is represented in our first step of reading. Phase 2 (coding) remains as our second step of coding. Phases 3 (generating initial themes), 4 (developing and reviewing themes), and 5 (refining, defining, and naming themes) are represented in our third step of theming. Phase 6 (writing up) also occurs during this third step of theming, but after a thematic analysis session. 4 12

Key features and applications of practical thematic analysis

Step 1: reading.

All manuscript authors read the data

All manuscript authors write summary memos

Step 2: Coding

Coders perform both data management and early data analysis

Codes are complete thoughts or sentences, not categories

Step 3: Theming

Researchers host a thematic analysis session and share different perspectives

Themes are complete thoughts or sentences, not categories

Applications

For use by practicing clinicians, patients and care partners, students, interdisciplinary teams, and those new to qualitative research

When important insights from healthcare professionals are inaccessible because they do not have qualitative methods training

When time and resources are limited

Fig 1

Steps in practical thematic analysis

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We present linear steps, but as qualitative research is usually iterative, so too is thematic analysis. 15 Qualitative researchers circle back to earlier work to check whether their interpretations still make sense in the light of additional insights, adapting as necessary. While we focus here on the practical application of thematic analysis in health services research, we recognise our approach exists in the context of the broader literature on thematic analysis and the theoretical underpinnings of qualitative methods as a whole. For a more detailed discussion of these theoretical points, as well as other methods widely used in health services research, we recommend reviewing the sources outlined in supplemental material 1. A strong and nuanced understanding of the context and underlying principles of thematic analysis will allow for higher quality research. 16

Practical thematic analysis is a highly flexible approach that can draw out valuable findings and generate new hypotheses, including in cases with a lack of previous research to build on. The approach can also be used with a variety of data, such as transcripts from interviews or focus groups, patient encounter transcripts, professional publications, observational field notes, and online activity logs. Importantly, successful practical thematic analysis is predicated on having high quality data collected with rigorous methods. We do not describe qualitative research design or data collection here. 11 17

In supplemental material 1, we summarise the foundational methods, concepts, and terminology in qualitative research. Along with our guide below, we include a companion slide presentation for teaching practical thematic analysis to research teams in supplemental material 2. We provide a theme coproduction template for teams to use during thematic analysis sessions in supplemental material 3. Our method aligns with the major qualitative reporting frameworks, including the Consolidated Criteria for Reporting Qualitative Research (COREQ). 18 We indicate the corresponding step in practical thematic analysis for each COREQ item in supplemental material 4.

Familiarisation and memoing

We encourage all manuscript authors to review the full dataset (eg, interview transcripts) to familiarise themselves with it. This task is most critical for those who will later be engaged in the coding and theming steps. Although time consuming, it is the best way to involve team members in the intellectual work of data interpretation, so that they can contribute to the analysis and contextualise the results. If this task is not feasible given time limitations or large quantities of data, the data can be divided across team members. In this case, each piece of data should be read by at least two individuals who ideally represent different professional roles or perspectives.

We recommend that researchers reflect on the data and independently write memos, defined as brief notes on thoughts and questions that arise during reading, and a summary of their impressions of the dataset. 2 19 Memoing is an opportunity to gain insights from varying perspectives, particularly from patients, care partners, clinicians, and others. It also gives researchers the opportunity to begin to scope which elements of and concepts in the dataset are relevant to the research question.

Data saturation

The concept of data saturation ( box 2 ) is a foundation of qualitative research. It is defined as the point in analysis at which new data tend to be redundant of data already collected. 21 Qualitative researchers are expected to report their approach to data saturation. 18 Because thematic analysis is iterative, the team should discuss saturation throughout the entire process, beginning with data collection and continuing through all steps of the analysis. 22 During step 1 (reading), team members might discuss data saturation in the context of summary memos. Conversations about saturation continue during step 2 (coding), with confirmation that saturation has been achieved during step 3 (theming). As a rule of thumb, researchers can often achieve saturation in 9-17 interviews or 4-8 focus groups, but this will vary depending on the specific characteristics of the study. 23

Data saturation in context

Braun and Clarke discourage the use of data saturation to determine sample size (eg, number of interviews), because it assumes that there is an objective truth to be captured in the data (sometimes known as a positivist perspective). 20 Qualitative researchers often try to avoid positivist approaches, arguing that there is no one true way of seeing the world, and will instead aim to gather multiple perspectives. 5 Although this theoretical debate with qualitative methods is important, we recognise that a priori estimates of saturation are often needed, particularly for investigators newer to qualitative research who might want a more pragmatic and applied approach. In addition, saturation based, sample size estimation can be particularly helpful in grant proposals. However, researchers should still follow a priori sample size estimation with a discussion to confirm saturation has been achieved.

Definition of coding

We describe codes as labels for concepts in the data that are directly relevant to the study objective. Historically, the purpose of coding was to distil the large amount of data collected into conceptually similar buckets so that researchers could review it in aggregate and identify key themes. 5 24 We advocate for a more analytical approach than is typical with thematic analysis. With our method, coding is both the foundation for and the beginning of thematic analysis—that is, early data analysis, management, and reduction occur simultaneously rather than as different steps. This approach moves the team more efficiently towards being able to describe themes.

Building the coding team

Coders are the research team members who directly assign codes to the data, reading all material and systematically labelling relevant data with appropriate codes. Ideally, at least two researchers would code every discrete data document, such as one interview transcript. 25 If this task is not possible, individual coders can each code a subset of the data that is carefully selected for key characteristics (sometimes known as purposive selection). 26 When using this approach, we recommend that at least 10% of data be coded by two or more coders to ensure consistency in codebook application. We also recommend coding teams of no more than four to five people, for practical reasons concerning maintaining consistency.

Clinicians, patients, and care partners bring unique perspectives to coding and enrich the analytical process. 27 Therefore, we recommend choosing coders with a mix of relevant experiences so that they can challenge and contextualise each other’s interpretations based on their own perspectives and opinions ( box 3 ). We recommend including both coders who collected the data and those who are naive to it, if possible, given their different perspectives. We also recommend all coders review the summary memos from the reading step so that key concepts identified by those not involved in coding can be integrated into the analytical process. In practice, this review means coding the memos themselves and discussing them during the code development process. This approach ensures that the team considers a diversity of perspectives.

Coding teams in context

The recommendation to use multiple coders is a departure from Braun and Clarke. 28 29 When the views, experiences, and training of each coder (sometimes known as positionality) 30 are carefully considered, having multiple coders can enhance interpretation and enrich findings. When these perspectives are combined in a team setting, researchers can create shared meaning from the data. Along with the practical consideration of distributing the workload, 31 inclusion of these multiple perspectives increases the overall quality of the analysis by mitigating the impact of any one coder’s perspective. 30

Coding tools

Qualitative analysis software facilitates coding and managing large datasets but does not perform the analytical work. The researchers must perform the analysis themselves. Most programs support queries and collaborative coding by multiple users. 32 Important factors to consider when choosing software can include accessibility, cost, interoperability, the look and feel of code reports, and the ease of colour coding and merging codes. Coders can also use low tech solutions, including highlighters, word processors, or spreadsheets.

Drafting effective codes

To draft effective codes, we recommend that the coders review each document line by line. 33 As they progress, they can assign codes to segments of data representing passages of interest. 34 Coders can also assign multiple codes to the same passage. Consensus among coders on what constitutes a minimum or maximum amount of text for assigning a code is helpful. As a general rule, meaningful segments of text for coding are shorter than one paragraph, but longer than a few words. Coders should keep the study objective in mind when determining which data are relevant ( box 4 ).

Code types in context

Similar to Braun and Clarke’s approach, practical thematic analysis does not specify whether codes are based on what is evident from the data (sometimes known as semantic) or whether they are based on what can be inferred at a deeper level from the data (sometimes known as latent). 4 12 35 It also does not specify whether they are derived from the data (sometimes known as inductive) or determined ahead of time (sometimes known as deductive). 11 35 Instead, it should be noted that health services researchers conducting qualitative studies often adopt all these approaches to coding (sometimes known as hybrid analysis). 3

In practical thematic analysis, codes should be more descriptive than general categorical labels that simply group data with shared characteristics. At a minimum, codes should form a complete (or full) thought. An easy way to conceptualise full thought codes is as complete sentences with subjects and verbs ( table 1 ), although full sentence coding is not always necessary. With full thought codes, researchers think about the data more deeply and capture this insight in the codes. This coding facilitates the entire analytical process and is especially valuable when moving from codes to broader themes. Experienced qualitative researchers often intuitively use full thought or sentence codes, but this practice has not been explicitly articulated as a path to higher quality coding elsewhere in the literature. 6

Example transcript with codes used in practical thematic analysis 36

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Depending on the nature of the data, codes might either fall into flat categories or be arranged hierarchically. Flat categories are most common when the data deal with topics on the same conceptual level. In other words, one topic is not a subset of another topic. By contrast, hierarchical codes are more appropriate for concepts that naturally fall above or below each other. Hierarchical coding can also be a useful form of data management and might be necessary when working with a large or complex dataset. 5 Codes grouped into these categories can also make it easier to naturally transition into generating themes from the initial codes. 5 These decisions between flat versus hierarchical coding are part of the work of the coding team. In both cases, coders should ensure that their code structures are guided by their research questions.

Developing the codebook

A codebook is a shared document that lists code labels and comprehensive descriptions for each code, as well as examples observed within the data. Good code descriptions are precise and specific so that coders can consistently assign the same codes to relevant data or articulate why another coder would do so. Codebook development is iterative and involves input from the entire coding team. However, as those closest to the data, coders must resist undue influence, real or perceived, from other team members with conflicting opinions—it is important to mitigate the risk that more senior researchers, like principal investigators, exert undue influence on the coders’ perspectives.

In practical thematic analysis, coders begin codebook development by independently coding a small portion of the data, such as two to three transcripts or other units of analysis. Coders then individually produce their initial codebooks. This task will require them to reflect on, organise, and clarify codes. The coders then meet to reconcile the draft codebooks, which can often be difficult, as some coders tend to lump several concepts together while others will split them into more specific codes. Discussing disagreements and negotiating consensus are necessary parts of early data analysis. Once the codebook is relatively stable, we recommend soliciting input on the codes from all manuscript authors. Yet, coders must ultimately be empowered to finalise the details so that they are comfortable working with the codebook across a large quantity of data.

Assigning codes to the data

After developing the codebook, coders will use it to assign codes to the remaining data. While the codebook’s overall structure should remain constant, coders might continue to add codes corresponding to any new concepts observed in the data. If new codes are added, coders should review the data they have already coded and determine whether the new codes apply. Qualitative data analysis software can be useful for editing or merging codes.

We recommend that coders periodically compare their code occurrences ( box 5 ), with more frequent check-ins if substantial disagreements occur. In the event of large discrepancies in the codes assigned, coders should revise the codebook to ensure that code descriptions are sufficiently clear and comprehensive to support coding alignment going forward. Because coding is an iterative process, the team can adjust the codebook as needed. 5 28 29

Quantitative coding in context

Researchers should generally avoid reporting code counts in thematic analysis. However, counts can be a useful proxy in maintaining alignment between coders on key concepts. 26 In practice, therefore, researchers should make sure that all coders working on the same piece of data assign the same codes with a similar pattern and that their memoing and overall assessment of the data are aligned. 37 However, the frequency of a code alone is not an indicator of its importance. It is more important that coders agree on the most salient points in the data; reviewing and discussing summary memos can be helpful here. 5

Researchers might disagree on whether or not to calculate and report inter-rater reliability. We note that quantitative tests for agreement, such as kappa statistics or intraclass correlation coefficients, can be distracting and might not provide meaningful results in qualitative analyses. Similarly, Braun and Clarke argue that expecting perfect alignment on coding is inconsistent with the goal of co-constructing meaning. 28 29 Overall consensus on codes’ salience and contributions to themes is the most important factor.

Definition of themes

Themes are meta-constructs that rise above codes and unite the dataset ( box 6 , fig 2 ). They should be clearly evident, repeated throughout the dataset, and relevant to the research questions. 38 While codes are often explicit descriptions of the content in the dataset, themes are usually more conceptual and knit the codes together. 39 Some researchers hypothesise that theme development is loosely described in the literature because qualitative researchers simply intuit themes during the analytical process. 39 In practical thematic analysis, we offer a concrete process that should make developing meaningful themes straightforward.

Themes in context

According to Braun and Clarke, a theme “captures something important about the data in relation to the research question and represents some level of patterned response or meaning within the data set.” 4 Similarly, Braun and Clarke advise against themes as domain summaries. While different approaches can draw out themes from codes, the process begins by identifying patterns. 28 35 Like Braun and Clarke and others, we recommend that researchers consider the salience of certain themes, their prevalence in the dataset, and their keyness (ie, how relevant the themes are to the overarching research questions). 4 12 34

Fig 2

Use of themes in practical thematic analysis

Constructing meaningful themes

After coding all the data, each coder should independently reflect on the team’s summary memos (step 1), the codebook (step 2), and the coded data itself to develop draft themes (step 3). It can be illuminating for coders to review all excerpts associated with each code, so that they derive themes directly from the data. Researchers should remain focused on the research question during this step, so that themes have a clear relation with the overall project aim. Use of qualitative analysis software will make it easy to view each segment of data tagged with each code. Themes might neatly correspond to groups of codes. Or—more likely—they will unite codes and data in unexpected ways. A whiteboard or presentation slides might be helpful to organise, craft, and revise themes. We also provide a template for coproducing themes (supplemental material 3). As with codebook justification, team members will ideally produce individual drafts of the themes that they have identified in the data. They can then discuss these with the group and reach alignment or consensus on the final themes.

The team should ensure that all themes are salient, meaning that they are: supported by the data, relevant to the study objectives, and important. Similar to codes, themes are framed as complete thoughts or sentences, not categories. While codes and themes might appear to be similar to each other, the key distinction is that the themes represent a broader concept. Table 2 shows examples of codes and their corresponding themes from a previously published project that used practical thematic analysis. 36 Identifying three to four key themes that comprise a broader overarching theme is a useful approach. Themes can also have subthemes, if appropriate. 40 41 42 43 44

Example codes with themes in practical thematic analysis 36

Thematic analysis session

After each coder has independently produced draft themes, a carefully selected subset of the manuscript team meets for a thematic analysis session ( table 3 ). The purpose of this session is to discuss and reach alignment or consensus on the final themes. We recommend a session of three to five hours, either in-person or virtually.

Example agenda of thematic analysis session

The composition of the thematic analysis session team is important, as each person’s perspectives will shape the results. This group is usually a small subset of the broader research team, with three to seven individuals. We recommend that primary and senior authors work together to include people with diverse experiences related to the research topic. They should aim for a range of personalities and professional identities, particularly those of clinicians, trainees, patients, and care partners. At a minimum, all coders and primary and senior authors should participate in the thematic analysis session.

The session begins with each coder presenting their draft themes with supporting quotes from the data. 5 Through respectful and collaborative deliberation, the group will develop a shared set of final themes.

One team member facilitates the session. A firm, confident, and consistent facilitation style with good listening skills is critical. For practical reasons, this person is not usually one of the primary coders. Hierarchies in teams cannot be entirely flattened, but acknowledging them and appointing an external facilitator can reduce their impact. The facilitator can ensure that all voices are heard. For example, they might ask for perspectives from patient partners or more junior researchers, and follow up on comments from senior researchers to say, “We have heard your perspective and it is important; we want to make sure all perspectives in the room are equally considered.” Or, “I hear [senior person] is offering [x] idea, I’d like to hear other perspectives in the room.” The role of the facilitator is critical in the thematic analysis session. The facilitator might also privately discuss with more senior researchers, such as principal investigators and senior authors, the importance of being aware of their influence over others and respecting and eliciting the perspectives of more junior researchers, such as patients, care partners, and students.

To our knowledge, this discrete thematic analysis session is a novel contribution of practical thematic analysis. It helps efficiently incorporate diverse perspectives using the session agenda and theme coproduction template (supplemental material 3) and makes the process of constructing themes transparent to the entire research team.

Writing the report

We recommend beginning the results narrative with a summary of all relevant themes emerging from the analysis, followed by a subheading for each theme. Each subsection begins with a brief description of the theme and is illustrated with relevant quotes, which are contextualised and explained. The write-up should not simply be a list, but should contain meaningful analysis and insight from the researchers, including descriptions of how different stakeholders might have experienced a particular situation differently or unexpectedly.

In addition to weaving quotes into the results narrative, quotes can be presented in a table. This strategy is a particularly helpful when submitting to clinical journals with tight word count limitations. Quote tables might also be effective in illustrating areas of agreement and disagreement across stakeholder groups, with columns representing different groups and rows representing each theme or subtheme. Quotes should include an anonymous label for each participant and any relevant characteristics, such as role or gender. The aim is to produce rich descriptions. 5 We recommend against repeating quotations across multiple themes in the report, so as to avoid confusion. The template for coproducing themes (supplemental material 3) allows documentation of quotes supporting each theme, which might also be useful during report writing.

Visual illustrations such as a thematic map or figure of the findings can help communicate themes efficiently. 4 36 42 44 If a figure is not possible, a simple list can suffice. 36 Both must clearly present the main themes with subthemes. Thematic figures can facilitate confirmation that the researchers’ interpretations reflect the study populations’ perspectives (sometimes known as member checking), because authors can invite discussions about the figure and descriptions of findings and supporting quotes. 46 This process can enhance the validity of the results. 46

In supplemental material 4, we provide additional guidance on reporting thematic analysis consistent with COREQ. 18 Commonly used in health services research, COREQ outlines a standardised list of items to be included in qualitative research reports ( box 7 ).

Reporting in context

We note that use of COREQ or any other reporting guidelines does not in itself produce high quality work and should not be used as a substitute for general methodological rigor. Rather, researchers must consider rigor throughout the entire research process. As the issue of how to conceptualise and achieve rigorous qualitative research continues to be debated, 47 48 we encourage researchers to explicitly discuss how they have looked at methodological rigor in their reports. Specifically, we point researchers to Braun and Clarke’s 2021 tool for evaluating thematic analysis manuscripts for publication (“Twenty questions to guide assessment of TA [thematic analysis] research quality”). 16

Avoiding common pitfalls

Awareness of common mistakes can help researchers avoid improper use of qualitative methods. Improper use can, for example, prevent researchers from developing meaningful themes and can risk drawing inappropriate conclusions from the data. Braun and Clarke also warn of poor quality in qualitative research, noting that “coherence and integrity of published research does not always hold.” 16

Weak themes

An important distinction between high and low quality themes is that high quality themes are descriptive and complete thoughts. As such, they often contain subjects and verbs, and can be expressed as full sentences ( table 2 ). Themes that are simply descriptive categories or topics could fail to impart meaningful knowledge beyond categorisation. 16 49 50

Researchers will often move from coding directly to writing up themes, without performing the work of theming or hosting a thematic analysis session. Skipping concerted theming often results in themes that look more like categories than unifying threads across the data.

Unfocused analysis

Because data collection for qualitative research is often semi-structured (eg, interviews, focus groups), not all data will be directly relevant to the research question at hand. To avoid unfocused analysis and a correspondingly unfocused manuscript, we recommend that all team members keep the research objective in front of them at every stage, from reading to coding to theming. During the thematic analysis session, we recommend that the research question be written on a whiteboard so that all team members can refer back to it, and so that the facilitator can ensure that conversations about themes occur in the context of this question. Consistently focusing on the research question can help to ensure that the final report directly answers it, as opposed to the many other interesting insights that might emerge during the qualitative research process. Such insights can be picked up in a secondary analysis if desired.

Inappropriate quantification

Presenting findings quantitatively (eg, “We found 18 instances of participants mentioning safety concerns about the vaccines”) is generally undesirable in practical thematic analysis reporting. 51 Descriptive terms are more appropriate (eg, “participants had substantial concerns about the vaccines,” or “several participants were concerned about this”). This descriptive presentation is critical because qualitative data might not be consistently elicited across participants, meaning that some individuals might share certain information while others do not, simply based on how conversations evolve. Additionally, qualitative research does not aim to draw inferences outside its specific sample. Emphasising numbers in thematic analysis can lead to readers incorrectly generalising the findings. Although peer reviewers unfamiliar with thematic analysis often request this type of quantification, practitioners of practical thematic analysis can confidently defend their decision to avoid it. If quantification is methodologically important, we recommend simultaneously conducting a survey or incorporating standardised interview techniques into the interview guide. 11

Neglecting group dynamics

Researchers should concertedly consider group dynamics in the research team. Particular attention should be paid to power relations and the personality of team members, which can include aspects such as who most often speaks, who defines concepts, and who resolves disagreements that might arise within the group. 52

The perspectives of patient and care partners are particularly important to cultivate. Ideally, patient partners are meaningfully embedded in studies from start to finish, not just for practical thematic analysis. 53 Meaningful engagement can build trust, which makes it easier for patient partners to ask questions, request clarification, and share their perspectives. Professional team members should actively encourage patient partners by emphasising that their expertise is critically important and valued. Noting when a patient partner might be best positioned to offer their perspective can be particularly powerful.

Insufficient time allocation

Researchers must allocate enough time to complete thematic analysis. Working with qualitative data takes time, especially because it is often not a linear process. As the strength of thematic analysis lies in its ability to make use of the rich details and complexities of the data, we recommend careful planning for the time required to read and code each document.

Estimating the necessary time can be challenging. For step 1 (reading), researchers can roughly calculate the time required based on the time needed to read and reflect on one piece of data. For step 2 (coding), the total amount of time needed can be extrapolated from the time needed to code one document during codebook development. We also recommend three to five hours for the thematic analysis session itself, although coders will need to independently develop their draft themes beforehand. Although the time required for practical thematic analysis is variable, teams should be able to estimate their own required effort with these guidelines.

Practical thematic analysis builds on the foundational work of Braun and Clarke. 4 16 We have reframed their six phase process into three condensed steps of reading, coding, and theming. While we have maintained important elements of Braun and Clarke’s reflexive thematic analysis, we believe that practical thematic analysis is conceptually simpler and easier to teach to less experienced researchers and non-researcher stakeholders. For teams with different levels of familiarity with qualitative methods, this approach presents a clear roadmap to the reading, coding, and theming of qualitative data. Our practical thematic analysis approach promotes efficient learning by doing—experiential learning. 12 29 Practical thematic analysis avoids the risk of relying on complex descriptions of methods and theory and places more emphasis on obtaining meaningful insights from those close to real world clinical environments. Although practical thematic analysis can be used to perform intensive theory based analyses, it lends itself more readily to accelerated, pragmatic approaches.

Strengths and limitations

Our approach is designed to smooth the qualitative analysis process and yield high quality themes. Yet, researchers should note that poorly performed analyses will still produce low quality results. Practical thematic analysis is a qualitative analytical approach; it does not look at study design, data collection, or other important elements of qualitative research. It also might not be the right choice for every qualitative research project. We recommend it for applied health services research questions, where diverse perspectives and simplicity might be valuable.

We also urge researchers to improve internal validity through triangulation methods, such as member checking (supplemental material 1). 46 Member checking could include soliciting input on high level themes, theme definitions, and quotations from participants. This approach might increase rigor.

Implications

We hope that by providing clear and simple instructions for practical thematic analysis, a broader range of researchers will be more inclined to use these methods. Increased transparency and familiarity with qualitative approaches can enhance researchers’ ability to both interpret qualitative studies and offer up new findings themselves. In addition, it can have usefulness in training and reporting. A major strength of this approach is to facilitate meaningful inclusion of patient and care partner perspectives, because their lived experiences can be particularly valuable in data interpretation and the resulting findings. 11 30 As clinicians are especially pressed for time, they might also appreciate a practical set of instructions that can be immediately used to leverage their insights and access to patients and clinical settings, and increase the impact of qualitative research through timely results. 8

Practical thematic analysis is a simplified approach to performing thematic analysis in health services research, a field where the experiences of patients, care partners, and clinicians are of inherent interest. We hope that it will be accessible to those individuals new to qualitative methods, including patients, care partners, clinicians, and other health services researchers. We intend to empower multidisciplinary research teams to explore unanswered questions and make new, important, and rigorous contributions to our understanding of important clinical and health systems research.

Acknowledgments

All members of the Coproduction Laboratory provided input that shaped this manuscript during laboratory meetings. We acknowledge advice from Elizabeth Carpenter-Song, an expert in qualitative methods.

Coproduction Laboratory group contributors: Stephanie C Acquilano ( http://orcid.org/0000-0002-1215-5531 ), Julie Doherty ( http://orcid.org/0000-0002-5279-6536 ), Rachel C Forcino ( http://orcid.org/0000-0001-9938-4830 ), Tina Foster ( http://orcid.org/0000-0001-6239-4031 ), Megan Holthoff, Christopher R Jacobs ( http://orcid.org/0000-0001-5324-8657 ), Lisa C Johnson ( http://orcid.org/0000-0001-7448-4931 ), Elaine T Kiriakopoulos, Kathryn Kirkland ( http://orcid.org/0000-0002-9851-926X ), Meredith A MacMartin ( http://orcid.org/0000-0002-6614-6091 ), Emily A Morgan, Eugene Nelson, Elizabeth O’Donnell, Brant Oliver ( http://orcid.org/0000-0002-7399-622X ), Danielle Schubbe ( http://orcid.org/0000-0002-9858-1805 ), Gabrielle Stevens ( http://orcid.org/0000-0001-9001-178X ), Rachael P Thomeer ( http://orcid.org/0000-0002-5974-3840 ).

Contributors: Practical thematic analysis, an approach designed for multidisciplinary health services teams new to qualitative research, was based on CHS’s experiences teaching thematic analysis to clinical teams and students. We have drawn heavily from qualitative methods literature. CHS is the guarantor of the article. CHS, AS, CvP, AMK, JRK, and JAP contributed to drafting the manuscript. AS, JG, CMM, JAP, and RWY provided feedback on their experiences using practical thematic analysis. CvP, LCL, SLB, AVC, GE, and JKL advised on qualitative methods in health services research, given extensive experience. All authors meaningfully edited the manuscript content, including AVC and RKS. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: This manuscript did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Competing interests: All authors have completed the ICMJE uniform disclosure form at https://www.icmje.org/disclosure-of-interest/ and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Provenance and peer review: Not commissioned; externally peer reviewed.

  • Ziebland S ,
  • ↵ A Hybrid Approach to Thematic Analysis in Qualitative Research: Using a Practical Example. 2018. https://methods.sagepub.com/case/hybrid-approach-thematic-analysis-qualitative-research-a-practical-example .
  • Maguire M ,
  • Vindrola-Padros C ,
  • Vindrola-Padros B
  • ↵ Vindrola-Padros C. Rapid Ethnographies: A Practical Guide . Cambridge University Press 2021. https://play.google.com/store/books/details?id=n80HEAAAQBAJ
  • Schroter S ,
  • Merino JG ,
  • Barbeau A ,
  • ↵ Padgett DK. Qualitative and Mixed Methods in Public Health . SAGE Publications 2011. https://play.google.com/store/books/details?id=LcYgAQAAQBAJ
  • Scharp KM ,
  • Korstjens I
  • Barnett-Page E ,
  • ↵ Guest G, Namey EE, Mitchell ML. Collecting Qualitative Data: A Field Manual for Applied Research . SAGE 2013. https://play.google.com/store/books/details?id=-3rmWYKtloC
  • Sainsbury P ,
  • Emerson RM ,
  • Saunders B ,
  • Kingstone T ,
  • Hennink MM ,
  • Kaiser BN ,
  • Hennink M ,
  • O’Connor C ,
  • ↵ Yen RW, Schubbe D, Walling L, et al. Patient engagement in the What Matters Most trial: experiences and future implications for research. Poster presented at International Shared Decision Making conference, Quebec City, Canada. July 2019.
  • ↵ Got questions about Thematic Analysis? We have prepared some answers to common ones. https://www.thematicanalysis.net/faqs/ (accessed 9 Nov 2022).
  • ↵ Braun V, Clarke V. Thematic Analysis. SAGE Publications. 2022. https://uk.sagepub.com/en-gb/eur/thematic-analysis/book248481 .
  • Kalpokas N ,
  • Radivojevic I
  • Campbell KA ,
  • Durepos P ,
  • ↵ Understanding Thematic Analysis. https://www.thematicanalysis.net/understanding-ta/ .
  • Saunders CH ,
  • Stevens G ,
  • CONFIDENT Study Long-Term Care Partners
  • MacQueen K ,
  • Vaismoradi M ,
  • Turunen H ,
  • Schott SL ,
  • Berkowitz J ,
  • Carpenter-Song EA ,
  • Goldwag JL ,
  • Durand MA ,
  • Goldwag J ,
  • Saunders C ,
  • Mishra MK ,
  • Rodriguez HP ,
  • Shortell SM ,
  • Verdinelli S ,
  • Scagnoli NI
  • Campbell C ,
  • Sparkes AC ,
  • McGannon KR
  • Sandelowski M ,
  • Connelly LM ,
  • O’Malley AJ ,

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Home > Books > Global Social Work - Cutting Edge Issues and Critical Reflections

Thematic Analysis in Social Work: A Case Study

Submitted: 14 May 2019 Reviewed: 02 September 2019 Published: 09 December 2019

DOI: 10.5772/intechopen.89464

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The article aims to provide a step-by-step description of how thematic analysis was applied in a study examining why men choose to undertake social work as an area of study. Participants in the study came from the University of Concepción in Chile and the University of Quebec in Abitibi-Témiscamingue in Canada. The six phases of the thematic analysis are described in detail to provide students and novice social work researchers with a guide to this method of analysis. Thematic analysis offers a flexible, yet rigorous approach to subjective experience that is highly applicable to research in social work as a means of promoting social justice and combating inequalities.

  • thematic analysis
  • social work
  • qualitative research

Author Information

Oscar labra *.

  • Department of Human and Social Development, Université du Québec en Abitibi-Témiscamingue, Canada

Carol Castro

  • Université du Québec en Abitibi-Témiscamingue, Canada

Robin Wright

  • School of Social Work, University of Windsor, Canada

Isis Chamblas

  • School of Social Work, Bío-Bío, University of Concepción, Chile

*Address all correspondence to: [email protected]

1. Introduction

There exist few detailed guidelines for thematic analysis, which represents a gap in the scientific literature. This article aims to partially remedy this scarcity by examining thematic analysis methods, drawing on the authors’ experiences as social work researchers, particularly as pertains to a case study. The present study is a six-step guide addressed specifically to students and novice researchers.

Thematic analysis has gained increasing currency in various branches of social work research, such as qualitative analysis [ 1 , 2 , 3 ], aboriginal research [ 4 ], resilience studies [ 5 ], the practice of social work in healthcare [ 6 , 7 , 8 ], and minors [ 9 , 10 ]. Nevertheless, little has been written on the specific adaptations and modulations that thematic analysis requires for use in social work research if it is to reflect the field’s specific preoccupations. It is important to note from the outset that thematic analysis in qualitative research is an empirical inductive approach to collect data.

The particular importance of qualitative research methods, such as thematic analysis, for social work is that these approaches can also serve to promote social justice and combat inequalities. Qualitative methods allow researchers to transmit people’s ideas, perceptions, and opinions by analyzing and disseminating participant discourses. This “speech act” is based on the values that guide social work, namely, respect for personal and collective rights, as well as a recognition of the need to perceive and understand human beings as constituents of an interdependent system that carries the potential for change. In employing qualitative research methods, social work researchers have a responsibility to promote social change and contribute to resolve social problems by analyzing and disseminating collected testimonies, which also serve as a basis from which to formulate future research and intervention paths. No other research methods have the same capacity to give voice to the disenfranchised in order to foster social change.

In order to contextualize the discussion of thematic analysis, the following section will first explore the broader framework of qualitative research. Why is qualitative research well suited to social work? After examining potential answers to this question, the discussion will then proceed to its core subject: thematic analysis and its usefulness in social work research, demonstrated by specific examples from fieldwork. This constitutes the primary aim of the present article.

2. Qualitative research and its relevance for social work

Qualitative methods are an established component of research models in various branches of inquiry, including social work, and have been used by social work researchers studying a range of dimensions, such as the family [ 11 , 12 , 13 , 14 ], women [ 15 , 16 , 17 ], children [ 18 , 19 , 20 , 21 ], and mental health services [ 18 , 22 , 23 , 24 ]. Thus, qualitative research methods have served to develop various domains of social work intervention ( Table 1 ).

Characteristics of qualitative research.

Source: Deslauriers [ 25 ]; Guba [ 26 ]; Hatch [ 27 ]; Hernández Sampieri et al. [ 28 ]; Marshall and Rossman [ 29 ]; Morse and Richards [ 30 ].

Over the past three decades, many authors have proposed varying definitions of qualitative research. Table 2 shows the major components of those definitions, providing clues as to the fundamental elements of the “DNA” of qualitative research and their relevance for social work.

Qualitative research criteria.

3. Applications of qualitative research

The elaboration of a research protocol or project requires asking whether qualitative research is relevant to the study’s methods and goals. The choice to adopt a qualitative approach is generally based on at least one of the criteria presented in Table 3 .

Research questions typology.

These seven elements represent contexts in which qualitative research is apposite. In order to demonstrate the application of these elements in fieldwork, Table 3 presents examples of questions used by the authors in previous qualitative studies.

Qualitative research includes a range of analytical methods applicable in various contexts. Those that appear to be adopted most often include phenomenographic analysis, phenomenological analysis, grounded theory (GT), case studies [ 32 ], narrative analysis [ 31 ], content analysis [ 33 , 34 , 35 ], participatory action research [ 36 , 37 , 38 ], aboriginal research [ 39 , 40 , 41 ], discourse analysis [ 42 , 43 , 44 , 45 ], and systematic analysis [ 46 , 47 ].

4. Defining thematic analysis

The definition of thematic analysis adopted in the present paper is that of a method that allows researchers to identify and organize relevant themes and subthemes, which can then be used as units of analysis [ 48 , 49 ] in subsequent detailed re-readings of a data set [ 50 ], through which researchers increasingly familiarize themselves with the data and explore the meanings associated with the concepts emerging from participant testimonies [ 51 , 52 ]. The central operation of thematic analysis, therefore, is thematization [ 53 ]. It is important to specify that “data set” refers to all materials compiled within the scope of a given study: transcripts of interviews conducted with participants, written testimonies, verbal communications, study objectives, and research questions, as well as all other relevant materials, which can include newspaper articles, annual research reports, and social work intervention reports, among others.

Repeated readings of a data set are necessary for the identification of the most salient significations in the collected materials. It is through these processes that researchers can reveal the affective, cognitive, and symbolic dimensions of the assembled data.

Social work research should seek to address issues of social justice and inequality or, at the very least, should not contribute to deficit constructions of marginalized populations by failing to acknowledge issues of discrimination and oppression.

5. The phases of thematic analysis

Thematic analysis involves six phases (see Figure 1 ). For the purposes of the present discussion, these phases will be described using examples from the authors’ experiences during a previous study, in which one of the main research themes was the reasons why certain men choose professions socially viewed as feminine [ 54 ]. The study involved 26 male participants enrolled in social work university programs: 13 in Chile and 13 in the Canadian province of Québec. The research question was exploratory, since no previous studies had addressed the issue directly; the thematic analysis, therefore, required a high degree of interpretation to fully grasp the significations emerging from participant testimonies. Specifically, the research question sought to discover the motivations, obstacles, and positive reference points, which characterized men’s interest in social work, a profession socially viewed as feminine. The following extensive discussion will refer to examples from the aforementioned study in order to examine in detail the methodological progression of the six phases of thematic analysis.

thematic analysis social research methods

Thematic analysis: Six interactive phases.

thematic analysis social research methods

Presentation of results.

It is essential to note that the six phases presented in Figure 2 overlap and interact: the phases are not exclusively successive, since there is a measure of recursion involved, in what is nevertheless a generally linear process. These characteristics indicate that thematic analysis is a flexible yet rigorous method of data analysis (see Figure 1 ). Three distinct approaches may be applied to thematic analysis: deductive (when themes are defined at the outset, prior to analyses), inductive (when themes emerge in the course of analysis), or, frequently, a deductive-inductive combination.

5.1 Phase 1: Familiarization with collected data

The first phase begins with the task of transcribing audio recordings of individual or group interviews carried out in the course of the study. The next step involves proceeding through initial readings of the transcripts in order to find the most salient significations in the participants’ testimonies. The material must be read thoroughly, attentively and analytically, particularly in order to identify those elements that may at first seem banal, yet frequently crucial to understanding the significations of a participants’ discourse.

Several techniques can help researchers to structure their first readings of the material. For example, an initial coding chart allows for the clear identification of excerpts that appear immediately relevant. As well, researchers familiar with thematic analysis frequently make annotations in the margins of transcripts or highlight in color certain excerpts that appear to be particularly significant.

The following excerpt and the comments cited below illustrate one researcher’s initial observations following a first reading of material collected in the course of a study:

Q: Which factors influenced your career choice?

A: I have a childhood friend who is a SW [social worker] and I went to talk with him. He told me about the main orientations of the profession, and I took the decision to enter this line of work. So it was the advice of a friend that helped me to make my decision to undertake social work, which I had not really thought about when I was in high school. It came from these conversations with this friend about the program, and I feel that I do not regret this decision. (Chilean participant No. 8, page 54)

The meanings identified in the testimony of Participant No. 8 were that: a) the participant had a close relationship with a social worker who influenced his career choice; b) the participant wanted to learn about the profession before deciding to undertake it; c) the profession’s orientations attracted the participant; d) the participant had not chosen a career path upon completing his secondary education; and e) the participant was satisfied with his choice of studies.

The example demonstrates that even a short interview excerpt can be a rich source of information, in this case indicating the various factors that characterized and influenced the participant’s choice to study toward a career in social work.

It is worth noting that qualitative data software, such as Nvivo®, presents additional coding capabilities and is in widespread use. For the purposes of the present study, however, the researchers opted to employ a manual coding technique.

Listening to and transcribing participant interviews.

Before undertaking readings of the material, it is helpful to construct an initial coding chart on which researchers can record their first impressions of the readings; this coding chart may identify the participant’s pseudonym, the specific excerpt in question, the transcript page number, and the signification or observation noted.

It is useful, as well, to keep the study objectives physically visible or close at hand for quick reference; this is especially recommended if the researcher carrying out the thematic analysis did not personally carry out the interviews or did not participate in the elaboration of the research project.

Initial readings of the material should be carried out repetitively, without at first overly focusing on particular details, in order to develop a familiarity with the raw data collected from participants. Examples of questions to keep in my mind during these first readings include:

What is this person trying to say?

Why are they talking about that in this particular way?

How should I interpret what I am reading?

In order to maintain familiarity with the raw data, repeated readings must be carried out in close succession, which contributes to a fuller understanding of participant testimonies and their significations. Researchers must keep in mind that they are scrutinizing the data for any and all information that relates to the research question and study objectives.

During these surface readings of the data set, researchers should use the initial coding chart to note any emerging elements that seem unfamiliar, interesting, or specifically related to the study question and objectives.

It is possible that following a few initial readings, researchers will be able to identify certain elements of data as themes (normally, this operation is not carried out until Phase 3). It is advisable in these instances to proceed cautiously, noting all pertinent elements on the coding chart and continuing to progress through the readings while noting elements that appear related to the theme, but refraining from premature definition.

Within a constructivist perspective, in the first phase of thematic analysis, the researcher adopts a subjectivist epistemological approach the reality under study. In the course of this process, researcher and respondent become a mutually constructed unit. The results, therefore, are the products of interactions between their realities ([ 26 ] in [ 55 ]:p. 17). In this process of production, social work researchers must maintain consciously reflexive, in order to minimize the potential effects of their prejudices or opinions, which could otherwise deform or falsify interpretation.

It is always preferable that the researcher carrying out the readings be the same person that carried out interviews with participants; this will place the researcher in a better epistemological position to ensure continuity throughout the thematic analysis process. If someone else is tasked with carrying out the readings, it is imperative that they become highly familiar with all aspects of the research project before beginning their analyses.

5.2 Phase 2: Generating initial codes

In this second phase, the researcher will use information identified as relevant in Phase 1 to generate initial codes. At the outset, researchers begin grouping elements of data according to similarities or perceived patterns: these are initial codes (see Tables 4 and 5 ). This ordering of the data is necessary to develop a comprehensive perspective on the participants’ latent or semantic discourse. An experienced researcher will likely proceed more quickly through this process; indeed, some researchers frequently combine the first two phases of thematic analysis.

Coding chart: Chile students.

Coding chart: Quebec students.

To begin, a code is a type of raw data extracted from interviews and field notes. These include words or phrases that are representative of groups or patterns of data (see Table 4 ). Miles and Huberman [ 56 ] identify three types of codes. The first is descriptive codes, which require very little interpretation. The second is interpretive codes, which represent data that require a certain depth of interpretation in order to be fully understood. The third type is inferential codes, relating to data that are explicative and indicate causal relationships.

Within the classification elaborated by Miles and Huberman [ 56 ], therefore, the examples presented in this article largely correspond to the descriptive type. When identifying descriptive codes, researchers have two options: using words or phrases drawn directly from participant testimonies (Level 1) or, where more appropriate, making reference to concepts drawn from relevant theory. The body of accumulated conceptual knowledge allows social work researchers to contextualize problems under study and more fully understand participants’ subjective reality. Social work researchers must remain conscious, however, of how their hypotheses influence their formulations of research questions, objectives, and resulting methodological choices that necessarily precede their analyses.

In order to systematically classify the information, codes and interview excerpts should be grouped in relation to clearly identify study objectives, as shown in Tables 4 and 5 . Particularly for researchers unfamiliar with thematic analysis, this method is effective in developing a better grasp of the classification processes involved in classifying generated data within the scope of defined study objectives.

Codes are always a combination of the descriptive and interpretive. This is evident in the preliminary codes cited in Tables 4 and 5 .

It is important to note that this method does not require codes to be generated for every line of transcript in the data set. Depending on interview type, a data set typically contains between 7000 and 9000 words, or close to 700 lines. A code can represent two, three, or more lines of transcript. It is always advisable to begin by working with the specific words used by participants (Level 1) and only after repeated readings to begin establishing links with concepts drawn from theory (Level 2), as in Table 6 , for example.

Thematic matrix.

Phase 2 concludes once all the elements of the data set have been coded. It is important to note that there is no minimum or maximum number of codes to be generated from a data set: the number is determined by each researcher’s judgment in assessing what is or is not pertinent, a skill that develops over time, in the course of work with transcripts.

5.3 Phase 3: Searching for themes

In qualitative research, a theme (sometimes also termed “category”) [ 31 ] is an element of data or sequence of words that can serve as a synoptic and accurate representation of the signification that interviewed participants attribute to an object, phenomenon, or situation. A theme, therefore, is composed of coded data grouped together according to similarities or patterns.

The search for themes is open ended, and the number and variety of results will depend on how systematically and thoroughly the first two phases were carried out. The process involves identification, differentiation, recombination, and grouping: certain themes will emerge distinctly from the data, others will be the product of either identifying more than one theme in what at first appeared to be one integral category, while others will emerge from the fusion of two or more themes that initially appeared distinct; themes that are divergent, yet related, may also be grouped into broader categories. With certain data sets, yet another level of classification will map the hierarchical relationships between themes. For Crabtree and Miller [ 57 ], the process of linking themes leads to the discovery of yet other themes and patterns in the data, that is, it generates overarching themes and allows for the identification of broad connections. This process of grouping distinct elements identified within a data set into themes constitutes the core task of thematic analysis.

In the example of the study discussed in the present article, data collected from interviews with Chilean and Québec students 1 were coded according to the study’s primary objective. As Table 7 demonstrates, a primary theme was identified in reference to theory (influence of life trajectory), while three subthemes emerged from the coded data.

Final thematic matrix.

Table 6 demonstrates how a primary theme connects three subthemes generated from seven distinct codes. In this example, the motivations to pursue social work of Chilean and Québec students participating in the study were all grouped in the primary theme “Influence of life trajectory.”

As mentioned above, there are no guidelines dictating minimum or maximum numbers of themes or subthemes to identify in a given study, independent of particular factors, such as number of participants. It is of utmost importance that themes and subthemes be delineated precisely in order to represent accurately and comprehensively the complexity of data collected from study participants. Themes therefore will vary qualitatively, substantively, and quantitatively from one study to another. In the example cited, a single-primary theme proved sufficiently broad to represent the significations derived from the data, enabling the authors to answer the research question and achieve the study objective.

A method useful in Phase 3 is to elaborate a coding sheet on which to classify elements of data that could not be precisely categorized in Phase 2 or that do not appear directly linked with the research question or study objectives. These data can prove highly relevant later, as additional themes are identified.

reading through the coding generated during Phase 2 (see Table 5 ), from right to left, in order to verify the accuracy of the identified elements of data;

assessing the correlation of codes with interview excerpts, as well as their relevance in relation to study objectives;

grouping the coded information in reference to concepts or sequences of words according to similarities or patterns: this is the identification of themes;

reviewing the identified themes in order to further categorize subthemes, overarching themes, or groups of themes, as the case may be; and

reading the material in order to identify hierarchical relationships between the themes.

Throughout this process, it is essential to keep in mind the stated study objectives, as well as to question continually whether the codes, themes, and subthemes are relevant to the research question and study objectives or whether they fall beyond the delineated scope of the study. It is important to point out that the themes and subthemes in which codes are grouped can represent concepts drawn from theory or original categories elaborated by the researcher. The epistemological challenge for researchers is to remain analytical in relation to the data that emerge from this phase of coding and to analyze them with reference to theory.

Phase 3 culminates in the elaboration of a thematic matrix that demonstrates connections between themes, subthemes, and codes (see Table 7 ). The matrix offers a clear overview of the ordered complexity of the relationships identified within the data set. It is useful, as well, to include within the matrix a column listing the study objectives or research question, providing an easily accessible reference with which to verify the relevance of data to the stated research goals.

5.4 Phase 4: Reviewing the themes

A comprehensive description of a given phenomenon requires a systematic review of the themes identified in Phase 3. Although, for the purposes of discussion, Phase 4 is identified as distinct from and subsequent to Phase 3, in practice researchers familiar with thematic analysis will frequently carry out the two phases simultaneously.

Is this a theme, subtheme, or code?

Does the theme accurately represent the data with which it is linked (codes and interview excerpts)?

Is the theme too abstract or difficult to understand or, conversely, is it so specific that it cannot be linked more broadly with data?

Is there a clearly identifiable logic to the hierarchical relationships between themes, subthemes, and codes (i.e., clear distinction between broader categories and more specific elements, as in Table 7 ?

Which data do the theme include and which do these exclude?

Is the theme a good representation of the subthemes? Are the subthemes a good representation of the codes?

Does the thematic matrix contain the information necessary to answer the research question and the study objectives?

These questions allow the researcher to assess the validity of the matrix and the coherence of its components. As in the preceding steps, validating the relevance of each element and the links between them is essential to ensuring the authenticity of results. It is important, however, to nuance the notion of validity. In qualitative research, a result is only considered valid if it is reproducible, that is, if it is not an individual occurrence of a given observation. Validity, moreover, may be internal or external. Internal validity refers to the degree to which valid conclusions can be drawn from a study, based on an assessment of all research parameters. External validity is the degree to which internally valid results may be extrapolated beyond specific study samples and settings, that is, to people and contexts other than those considered in the study.

A range of factors may have an incidence on a study’s internal validity, including participants’ personal histories, maturation and pretest habituation, participant selection, experimental mortality, and instrument bias. External validity is subject to other factors, such as interaction between historical factors and interventions, the effect of reactivity (that is, participants’ awareness of taking part in a study resulting changes in behavior), and researcher bias.

Researchers must also take into account other dimensions of validity relevant to social work research, for example, reflexive practice in collaboration with other researchers [ 58 ], data triangulation [ 59 , 60 ], and iterative research that allows participants to react to interpretations of previous results.

A detailed, comprehensive review of the thematic matrix frequently results in adjustments, including changes to the designations and relative positions of codes and themes, as well as the outright deletion of certain themes and subthemes that are not relevant to the research question (see Table 7 ). As a result of this review process, it is often necessary to rename themes that prove unclear, inaccurate, or disconnected from the identified codes. In such cases, themes are said to have evolved. As with each step of each phase, it is through the practice of these operations that researchers unfamiliar with thematic analysis will develop a better grasp of its techniques.

A comparison between Tables 6 and 7 illustrates this process. In this case, the subthemes initially identified as referring to experiences were adjusted in Table 7 to represent motivations. A second important change consisted in adjusting the code designated in Table 6 as “educational performance,” in order to further specify “ good educational performance” in Table 7 . A final change made to the thematic matrix concerned the position of the “ parental influence ” code, which had been placed in the “professional trajectory” subtheme in Table 7 but, subsequent to review, was placed within the “personal motivations” subtheme in Table 7 . In this example, the other data in the matrix remained unchanged following the Phase 4 review (see Table 7 ).

A valuable method of ensuring that the themes, subthemes, and codes are clearly delineated and appropriately positioned is to submit the thematic matrix to additional review by one or two researchers uninvolved in the study who are familiar with thematic analysis methods. If the reliability analysis process is successful, that is, if the independent reviewers concur that the themes reliably represent the codes derived from the data set to which they are linked within the matrix, the thematic analysis can proceed to Phase 5.

5.5 Phase 5: Defining and naming themes

Phase 5 consists of two major stages. First, the themes and subthemes undergo a definitive revision. Thus, the thematic matrix must once again be analyzed thoroughly in order to assess the validity of hierarchical relationships and verify whether the designations given at both levels are an accurate reflection of the significations represented by the codes. It is essential that names given to the themes be revised repeatedly, until no ambiguities remain as to their accuracy. The second stage of Phase 5 is interpretive and consists in the conceptual definition of the themes and subthemes that will be subject to analysis in Phase 6.

Educational motivations: an individual’s [student’s] capacity to construct short- and long-term objectives [in their educational trajectory], notwithstanding difficulties. It is through motivation that needs are transformed into objectives and projects [ 61 , 62 ].

Personal motivations: the choice, energy, and direction of behavior [ 63 ].

Professional motivations: the set of dynamic factors that determine an individual’s [student’s] interest in succeeding [in the chosen profession] [ 64 ].

In defining themes, it is advisable to refer exclusively to specialized reference works conventionally accepted in relevant fields of study, such as dictionaries or encyclopedias of social work, education, or sociology, depending on the focus of a given study.

It is important to mention that the boundary between Phases 4 and 5 may be difficult to pinpoint, since both involve a revision of the themes. The distinction lies in that the final revision and conclusive assessment of themes in Phase 5 is the culmination of the repeated reviews of designations, categories, and relationships performed in Phase 4. In Phase 5, therefore, the researcher’s principal task is to define and name the themes, in reference to all the operations performed in the previous phases, ensuring that they faithfully represent the significations emerging from the data set.

5.6 Phase 6: Presenting and discussing results

Whether to be included in a book, article, or other form of publication, the crux of the material supporting the results presented and discussed is to be found in notes taken by researchers during interviews with participants and the thematic matrix developed in Phase 3 and revised in Phase 4. For the purposes of the present discussion, it is worthwhile to address the two main components of Phase 6, presentation and discussion, as distinct from one another.

In the presentation of results, researchers must produce a clear and coherent description that makes reference the data outlined in the thematic matrix. The presentation should be accompanied by explanations and clarifications sufficient for readers unfamiliar with the specific area of study to understand the results without room for erroneous interpretation. It is highly advisable to quote interview excerpts that are particularly illustrative of the assertions and conclusions described.

A clear presentation of data outlined in the thematic matrix should reflect the order of the hierarchical relationships between the themes and subthemes. In the study of Chilean and Québec students enrolled in social work programs described in the present article, the primary theme of “ influence of life trajectories ” integrated all subthemes and associated codes. The presentation of results, therefore, began with a description of the primary theme and then proceeded through a descriptive and coherent account, supported by illustrative interview excerpts, that outlined all relevant elements of data, beginning with the most broadly inclusive (primary theme) and proceeding toward the most specific (codes).

This is clearly evident in the following excerpt of the presentation of results in the study involving male social work students in Chile and Québec, which provides valuable examples of thematic analysis methods.

This section will first present the motivations that prompt Chilean and Québec male students’ choices to undertake social work. […] On the personal level, the two primary motivations that emerge from the testimonies of Chilean students are the desire to help others and the appeal of social work as a vocation, followed in the order of importance by the influence of family or social circle members who had studied social work.

The following excerpt from the testimony of one student (1) illustrates the motivation to help others and undertake social work as a vocation: “I went into social work […] to be able to help people. I believe that this is the factor that made me enrol” (René). The testimonies of Québec students, however, suggest that their strong motivations are attributable to good relationships with family, specifically parents [who had worked in the health system], and negative personal experiences in the past, among others. The testimony of one participant typifies this primary motivation of most Québec students participating in the present study: “The fact of having two parents who work in the health system. Since I was little, I have been going to hospitals and I have seen how it all works” (Simon) [ 54 ].

It is important that the presentation of results remains descriptive, as in the example cited above. The logical question to ask at this point is: when does the presentation of results end? The answer, too, is logical: when the relevant elements of the final subtheme have been presented. In the study cited above, therefore, the presentation concludes with a description of the professional motivations subtheme (see Table 7 ). Once the results have been comprehensively presented, they must subsequently be discussed.

In the discussion, researchers must address the presented results within an analytical perspective. As in the example cited below, the discussion makes reference to the broader literature relevant to the phenomenon under study:

The present study offers comparative and complementary views on the various dimensions associated with the motivations of men who engage in social work. Participants’ answers to the question “ What made you choose social work studies ?”, suggest that their motivations are varie, “multifactorial” [ 65 ] and linked with life trajectories. At the level of the sub-category of “educational motivations,” it appears that Chilean men seek cognitive and technical skills with which to achieve their goal of social change. The results suggest that these men aspire to the values of social work (EASSW, 2015 [3]) and a more humanist and just society in which social work occupies a position of importance among social science professions. These motivations originate in two factors. One is the participants’ social engagement prior to enrolling in university studies. The other is their personal orientation towards humanist values. In the case of participants from Québec, their main motivations lie in good results obtained during pre-university social science studies, which inspired them to undertake social work at university. Our results corroborate a number of previous studies [ 54 , 66 , 67 ].

The above excerpt illustrates how the discussion builds on the description of results in order to produce an analytical discourse that compares and contrasts the results and conclusions of the study with those of other studies and authors.

The discussion should follow the same order of themes as in the presentation of results.

It should underscore and further develop those themes that most closely correlate with the stated study objectives; it is not always possible to address all the obtained results within the discussion; therefore, a capacity to synthesize is particularly useful at this last stage of the thematic analysis.

The discussion must be framed analytically; the goal is to go beyond the descriptive, in order to demonstrate why the results are meaningful within the context of previous research.

7. Conclusion

As a qualitative research method that offers a simultaneously flexible and rigorous approach to data, thematic analysis allows social work students and novice social work researchers to approach the discourses, opinions, and visions of respondents both analytically. These qualities make it particularly applicable to social work research. Thematic analysis also represents the intellectual and ethic challenge, for experienced and novice researchers alike, of attempting to reveal and interpret themes and subthemes in the participants’ discourse. The ethical challenge for researchers is to avoid substituting personal objectives for research objectives, since this may impact the interpretation of data collected from participants and, consequently, skew study results.

The other challenge facing social work researchers employing thematic analysis is to keep their subjectivity in check. When describing and categorizing testimonies of human experiences, perspectives, and emotions, whether expressed in words or communicated inadvertently by respondents through behavior during interviews, researchers must remain especially vigilant so that their author’s own personal histories and professional experiences do not contaminate their interpretation of the data, altering the significance of participant testimonies. Indeed, this reflects one of the fundamental principles of social work practice and research methodologies that knowledge and techniques must always be applied methodically and objectively.

From the example that is present in this work and following the six stages of the thematic analysis, the researcher can draw inspiration to use this method of analysis and to apply other research designs. Finally, the qualitative research in Trabajo Social students from the two participating universities allowed us to use thematic analysis to better understand the motivations, difficulties, and anchors that make students from two different realities interest in social work.

Limitations

The thematic analysis approach discussed in the present paper must be interpreted with prudence. The article cited above provides a case example of how thematic analysis was applied in one study examining why men choose to undertake social work as an area of study. An additional limitation is the difficulty for researchers to ignore previous, tacit knowledge, which may have influenced the analysis of results [ 68 ]. Furthermore, the construction of certain themes and subthemes cited in Labra [ 54 ] may have been influenced by social desirability, that is, formulated so as to correspond with researchers’ expectations, given that Nvivo® software was not used to manage qualitative data. Nevertheless, the research design of the case example presented above, in which the interview guide was elaborated in reference to both the specific problem under study and a directly relevant conceptual framework, constitutes a significant element underpinning the validity of the thematic analysis approach.

We would like to thank Normand Brodeur, professor at the School of Social Work of Laval University, Canada, and Hugo Asselin, professor at the School of Aboriginal Studies of the University of Abitibi-Témiscamingue, Canada, for the reading and his valuable recommendations to this article. Similarly, we would like to thank in a very special way Carlos Contreras and Jorge Lara, students of the School of Social Work of the University of Concepción, Chile, who have been testing through research verbatim, the phases of the thematic analysis described in this article.

  • 1. Floersch J, Longhofer JL, Kranke D, Townsend L. Integrating thematic, grounded theory and narrative analysis: A case study of adolescent psychotropic treatment. Qualitative Social Work. 2010; 9 (3):407-425
  • 2. Fook J. Theorizing from practice: Towards an inclusive approach for social work research. Qualitative Social Work. 2002; 1 (1):79-95
  • 3. Padgett DK. Qualitative Methods in Social Work Research. Vol. 36. Sage publications; 2016
  • 4. Sinclair R. Indigenous research in social work: The challenge of operationalizing worldview. Native Social Work Journal. 2003; 5 :117-139
  • 5. Kapoulitsas M, Corcoran T. Compassion fatigue and resilience: A qualitative analysis of social work practice. Qualitative Social Work. 2015; 14 (1):86-101
  • 6. Fox J, Ramon S, Morant N. Exploring the meaning of recovery for carers: Implications for social work practice. British Journal of Social Work. 2015; 45 (Suppl_1):i117-i134
  • 7. Williams CC, Almeida M, Knyahnytska Y. Towards a biopsychosociopolitical frame for recovery in the context of mental illness. British Journal of Social Work. 2015; 45 (Suppl. 1):i9-i26
  • 8. Worsley A, McLaughlin K, Leigh J. A subject of concern: The experiences of social workers referred to the health and care professions council. British Journal of Social Work. 2017; 47 (8):2421-2437
  • 9. Jansen A. ‘It’s so complex!’: Understanding the challenges of child protection work as experienced by newly graduated professionals. The British Journal of Social Work. 2017; 48 (6):1524-1540
  • 10. Steels S, Simpson H. Perceptions of children in residential care homes: A critical review of the literature. British Journal of Social Work. 2017; 47 (6):1704-1722
  • 11. Dubus N. Welcoming refugee families: A qualitative study of 20 professionals’ views of resettlement of Syrian families in Iceland. International Social Work. 2019:1-13. DOI: 10.1177/0020872818820411
  • 12. Jones DW. Families and serious mental illness: Working with loss and ambivalence. British Journal of Social Work. 2004; 34 (7):961-979
  • 13. Ma JL, Lai K, Wan ES. Maltreatment in parent–child relationships of Chinese families with children suffering from attention deficit hyperactivity disorder in Hong Kong: A qualitative study. British Journal of Social Work. 2015; 46 (7):2051-2069
  • 14. Parekh R, Praetorius RT, Nordberg A. Carers’ experiences in families impacted by Huntington’s disease: A qualitative interpretive meta-synthesis. British Journal of Social Work. 2017; 48 (3):675-692
  • 15. Hutchinson AJ. Research evidence to inform strengths-based policy and practice: Mapping the coping strategies of young women in Mozambique. The British Journal of Social Work. 2018; 49 (1):116-134
  • 16. Kreitzer L. Liberian refugee women: A qualitative study of their participation in planning camp programmes. International Social Work. 2002; 45 (1):45-58
  • 17. Lenette C, Cox L, Brough M. Digital storytelling as a social work tool: Learning from ethnographic research with women from refugee backgrounds. The British Journal of Social Work. 2013; 45 (3):988-1005
  • 18. Ballús E, Pérez-Téstor C. The emotional experience of being internationally adopted: A qualitative study with Nepalese children adopted in Spain. International Social Work. 2017; 60 (5):1141-1153
  • 19. Kaltenborn K-F. Children’s and young people’s experiences in various residential arrangements: A longitudinal study to evaluate criteria for custody and residence decision making. British Journal of Social Work. 2001; 31 (1):81-117
  • 20. Orme J, Seipel MM. Survival strategies of street children in Ghana: A qualitative study. International Social Work. 2007; 50 (4):489-499
  • 21. Truter E, Fouché A, Theron L. The resilience of child protection social workers: Are they at risk and if so, how do they adjust? A systematic meta-synthesis. British Journal of Social Work. 2016; 47 (3):846-863
  • 22. Davies K, Gray M. Mental health service Users’ aspirations for recovery: Examining the gaps between what policy promises and practice delivers. British Journal of Social Work. 2015; 45 (Suppl_1):i45-i61
  • 23. McCrae N, Murray J, Huxley P, Evans S. The research potential of mental-health social workers: A qualitative study of the views of senior mental-health service managers. British Journal of Social Work. 2005; 35 (1):55-71
  • 24. Webber M, Robinson K. The meaningful involvement of service users and carers in advanced-level post-qualifying social work education: A qualitative study. British Journal of Social Work. 2011; 42 (7):1256-1274
  • 25. Deslauriers J-P. Recherche Qualitative: Guide Pratique. Montréal: Mc Graw-Hill; 1991
  • 26. Guba EG. The Paradigm Dialog. Indiana: Sage publications; 1990
  • 27. Hatch JA. Doing Qualitative Research in Education Settings. Albany: State University of New York Press; 2002
  • 28. Hernández Sampieri R, Fernández Collado C, Baptista Lucio P. Metodología de la Investigación. Vol. 707. México: McGraw-Hill; 2003
  • 29. Marshall C, Rossman GB. Designing Qualitative Research. Thousand Oaks, CA: Sage publications; 2014
  • 30. Morse J, Richards L. Read me First for a user’s Guide to Qualitative Research. CA, US: Sage Publications Thousand Oaks; 2002
  • 31. Creswell JW, Poth CN. Qualitative Inquiry and Research Design: Choosing among Five Approaches. Thousand Oaks, CA: Sage publications; 2017
  • 32. Fortin MF, Gagnon J. Fondements et étapes du processus de recherche: Méthodes quantitatives et qualitatives. Montréal: Chenelière éducation; 2016
  • 33. Armborst A. Thematic proximity in content analysis. SAGE Open. 2017; 7 (2):1-11. DOI: 10.1177/2158244017707797
  • 34. Berelson B. Content Analysis in Communication Research. New York: Hafner; 1971
  • 35. Hsieh H-F, Shannon SE. Three approaches to qualitative content analysis. Qualitative Health Research. 2005; 15 (9):1277-1288
  • 36. Fine M, Torre ME. Intimate details: Participatory action research in prison. Action Research. 2006; 4 (3):253-269
  • 37. Houh EM, Kalsem K. Theorizing legal participatory action research: Critical race/feminism and participatory action research. Qualitative Inquiry. 2015; 21 (3):262-276
  • 38. Stapleton SR. Teacher participatory action research (TPAR): A methodological framework for political teacher research. Action Research. 2018:1-8. DOI: 10.1177/1476750317751033
  • 39. Andrews S, Gallant D, Humphreys C, Ellis D, Bamblett A, Briggs R, et al. Holistic programme developments and responses to aboriginal men who use violence against women. International Social Work. 2018:1-15. DOI: 10.1177/0020872818807272
  • 40. Bull JR. Research with aboriginal peoples: Authentic relationships as a precursor to ethical research. Journal of Empirical Research on Human Research Ethics. 2010; 5 (4):13-22
  • 41. Sanduliak A. Researching the self: The ethics of auto-ethnography and an aboriginal research methodology. Studies in Religion/Sciences Religieuses. 2016; 45 (3):360-376
  • 42. Brock A. Critical technocultural discourse analysis. New Media & Society. 2018; 20 (3):1012-1030
  • 43. Bucholtz M. Reflexivity and critique in discourse analysis. Critique of Anthropology. 2001; 21 (2):165-183
  • 44. Cheek J. At the margins? Discourse analysis and qualitative research. Qualitative Health Research. 2004; 14 (8):1140-1150
  • 45. Garrity Z. Discourse analysis, Foucault and social work research. Journal of Social Work. 2010; 10 (2):193-210
  • 46. Joffe H. Qualitative research methods in mental health and psychotherapy: A guide for students and practitioners. In: Thematic Analysis. Vol. 1. Oxford: Wiley-Blackwell; 2012. pp. 210-223
  • 47. Oliveira DC d. Análise de conteúdo temático-categorial: uma proposta de sistematização. Revista Enfermagem UERJ. 2008; 16 (4):569-576
  • 48. Braun V, Clarke V. Using thematic analysis in psychology. Qualitative Research in Psychology. 2006; 3 (2):77-101
  • 49. Fereday J, Muir-Cochrane E. Demonstrating rigor using thematic analysis: A hybrid approach of inductive and deductive coding and theme development. International Journal of Qualitative Methods. 2006; 5 (1):80-92
  • 50. Rice PL, Ezzy D. Qualitative Research Methods: A Health Focus. Australia: Melbourne; 1999
  • 51. Attride-Stirling J. Thematic networks: An analytic tool for qualitative research. Qualitative Research. 2001; 1 (3):385-405
  • 52. Mieles Barrera MD, Tonon G, Alvarado Salgado SV. Investigación cualitativa: el análisis temático para el tratamiento de la información desde el enfoque de la fenomenología social. Universitas Humanística. 2012; 74 :195-226
  • 53. Paillé P, Mucchielli A. L’analyse qualitative en sciences humaines et sociales. 4e éd ed. Paris: Armand Colin; 2016
  • 54. Labra O, Chamblas I, Turcotte P. Regards croisés sur l’expérience en tant qu’hommes d’étudiants québécois et chiliens durant leur formation universitaire en travail social [Perspectives on the experience of québécois and Chilean male students in social work studies]. Le Sociographe. 2016; 56 (4):121-131
  • 55. Labra O. Positivismo y Constructivismo: Un análisis para la investigación social. Rumbos TS. Un espacio crítico para la reflexión en Ciencias Sociales. 2013;(7):12-21
  • 56. Miles MB, Huberman AM. Qualitative Data Analysis: An Expanded Sourcebook. Thousand Oaks, CA: Sage; 1994
  • 57. Crabtree B, Miller W. A template approach to text analysis: Developing and using codebooks. In: Doing Qualitative Research. Newbury Park, CA: Sage; 1999. pp. 163-177
  • 58. Mortari L. Reflectivity in research practice: An overview of different perspectives. International Journal of Qualitative Methods. 2015; 14 (5):1-9
  • 59. Humble ÁM. Technique triangulation for validation in directed content analysis. International Journal of Qualitative Methods. 2009; 8 (3):34-51
  • 60. Kern FG. The trials and tribulations of applied triangulation: Weighing different data sources. Journal of Mixed Methods Research. 2018; 12 (2):166-181
  • 61. Doron R, Parot F. Dictionnaire de psychologie. Paris: PUF; 1991
  • 62. Raynal F, Rieunier A. Pédagogie: dictionnaire des concepts clés apprentissages, formation et psychologie cognitive. In: Françaises É s, editor. Paris; 1997
  • 63. McClelland D. Human Motivation. Cambridge: Cambridge University Press; 1988
  • 64. Sillamy N. Dictionnaire de psychologie. Paris: Larousse; 1983
  • 65. Biggerstaff MA. Development and validation of the social work career influence questionnaire. Research on Social Work Practice. 2000; 10 (1):34-54
  • 66. Valenzuela, de Keijzer B. “Identidades masculinas en estudiantes y docentes de la Universidad Central que eligen profesiones asociadas socialmente como femeninas’ [Masculine identities among students and faculty at the Central University of Chile in professions socially viewed as feminine]. Santiago, Chili: V Coloque d’Etudes sur les hommes et sur la masculunite; 2015
  • 67. Whitaker T. Who wants to be a social worker? Career Influences and Timing: NASW Membership Workforce Study. Washington, DC: National Association of Social Workers; 2008
  • 68. Charmaz K. Constructing Grounded Theory. Thousand Oaks, CA: Sage publications; 2014
  • This study was built on the analysis of interviews with twenty-six (n = 26) students: 13 are respondents enrolled in a social work program at the University of Québec in Abitibi-Témiscamingue (UQAT) in Canada and the thirteen others enrolled in a social work program at a university of Concepción. The first participants were recruited by way of e-mail messages sent to male students enrolled at the University of Concepción and of University of Quebec in Abitibi-Témiscamingue on in undergraduate social work studies for the winter semesters of 2014 and 2015. The rest were recruited using the "snowball".

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Using Thematic Analysis in Social Work Research: Barriers to Recruitment and Issues of Confidentiality

  • By: Luke Cartwright
  • Product: Sage Research Methods Cases Part 1
  • Publisher: SAGE Publications Ltd
  • Publication year: 2020
  • Online pub date: January 06, 2020
  • Discipline: Social Work
  • Methods: Qualitative data collection , Thematic analysis , Confidentiality
  • DOI: https:// doi. org/10.4135/9781529708455
  • Keywords: informed decision making , media use , recruitment , social media , students , trainees Show all Show less
  • Type: Indirect case info Online ISBN: 9781529708455 Copyright: Contact SAGE Publications at http://www.sagepub.com . More information Less information

This case study describes the stages involved in designing a research project including the rationale for the type of data to collect (qualitative), the approach used to gather data (semi-structured interviews), and the method utilized to analyze the data (thematic analysis). This case study then explores the importance of information sheets, participant consent forms, and the challenges researchers can experience when recruiting participants for their research projects. Reflection on action is highlighted as being an important concept for researchers in this section of the case study. Four of the key stages used when utilizing thematic analysis are then described so that new researchers can operationalize this approach in their own projects. The final section considers the significant lessons learnt (from the researcher perspective) having carried out the research described in this case study.

Learning Outcomes

By the end of this case, students should be able to

  • Understand the importance of reflection on action
  • Describe the stages of data analysis used with thematic analysis
  • Understand why confidentiality is important to research participants
  • Understand the importance of considering participants’ needs during a research project

Project Overview and Context

This research project was a small-scale study that aimed to develop an initial understanding about how trainee social workers used social media during their training as a tool for continued professional development while balancing the need to present a professional identity. The reason for my interest in this area was driven by new guidance about the use of social media published by the British Association of Social Work (a professional membership organization) and by the Health Care Professions Council (HCPC), the regulatory body for social work in England. There is an increasing focus on how social workers use social media; therefore, it is important that those involved in training new social workers are equipped to support trainees’ development in this area.

Project Design

As I wanted to develop an understanding of how trainee social workers used social media, a qualitative approach to data collection was considered most suitable. Qualitative approaches are useful when researchers want to understand how individuals (in this case, trainee social workers) make sense of their actions. These types of data enable the researcher to understand thoughts, views, and feelings (Whittaker, 2012).

Having decided that a qualitative approach most suited the project aims, I needed to decide on the approach to use for collecting my data. One of the most common approaches to data gathering in qualitative research is participant interviews (Bryman, 1988). These interviews can be what are often described in textbooks as structured; that is to say that the researcher has a list of questions that the researcher asks research participants (D’Cruz & Jones, 2004). At the other end of the spectrum are unstructured interviews; this approach is used by more skilled and experienced researchers and allows for exploration of topics with participants as they arise during interviews. This approach is particularly useful when investigating something where little is known, as it offers the researcher a high degree of flexibility during the data-gathering phase of the project.

For this study, I used semi-structured interviews. As the name suggests, this approach is somewhere between structured and unstructured interviews. The advantage with this approach is that it provides the researcher a degree of flexibility when interviewing participants. However, it also provides a framework that ensures that all participants are asked a set of central questions but gives the researcher the opportunity to ask follow-up questions to gather deep contextual data (Bryman, 1988). This approach best suited this project because I wanted to enable the research participants the opportunity to provide rich contextual detail but also I needed to contain the interviews within a framework that explored how trainee social workers used social media. “The aim of this research was to develop an understanding about how student social workers use social media during their time at university as a tool for continuing professional development while balancing the need to present a professional persona” (Cartwright, 2017, p. 4).

Having made the decision to collect qualitative data by using semi-structured interviews, I needed to select an appropriate method for data analysis. As this was a small-scale study, I identified thematic analysis as an approach that supported the research aims, and that was a good fit when working with data collected from semi-structured interviews. This approach was used for its strengths in helping to identify, analyze, and report patterns or themes in the collected data. The way I used this approach (thematic analysis) reflects what Virginia Braun and Victoria Clark (2006) describe as theoretical thematic analysis, as the research questions were used to inform the coding stage of the analysis process (more on this later). I designed this research to answer the following questions about the participants’ experiences of using social media (Cartwright, 2017, p. 4):

  • How do student social workers use social media?
  • How has their use of social media changed during their training to account for the need to develop a professional (online) identity?
  • How can universities support student social workers make full use of the opportunities that social media offers while helping them develop as professional workers

The next task was to think about the most suitable participants for my study. As my study aimed to understand how trainee social workers used social media, this was the group I needed to access. Social work training at universities in England is delivered at both undergraduate and postgraduate levels. To maximize my pool of potential participants, I decided to approach trainees at both these levels at a university that trained a large number of social workers in the north of England.

Research Practicalities

An important stage in any research project is gaining ethical consent for the work (Mason, 2018). Most institutions will have predetermined processes to follow and forms for researchers to complete to capture key information. The most significant issue to consider (ethically) during this research was that the participants were trainee social workers and students at the university where I intended to recruit. This created a negative power relationship between the researcher (as the possessor of power) and the research participants.

This meant that I needed to ensure that they understood that the research was separate from their training but that my findings may be used with future students to enhance their learning and development. Having reflected on the information I provided to potential research participants, this is one aspect of the research process that could have been developed further. I will consider this in detail later in this case study.

I also needed to make sure that participants understood that my role, as a researcher, was different from the role of the teaching team at the university (McLaughlin, 2012). I tried to account for this and emphasized the arrangements I would use to protect participant anonymity, including the use of pseudonyms in any articles that would be published that drew on the interviews. Again, I will come back to this, as reassuring participants that I would make sure they could not be identified was a significant challenge during this research.

Information Sheets and Consent Forms

Before I could start to recruit participants, I needed to design an information sheet. Information sheets are used to help make sure that research participants make informed decisions about taking part in research and so must include all the key information about the study. It is an essential part of the research process. As this document helps ensure that participants are able to make informed decisions about whether or not to be involved with a research project, when designing the form, the researcher must develop it with his or her participants in mind. (Example information sheets are easy to find on the Internet and a link to a useful web source is offered at the end of this case.)

At the stage in the research process when I designed my information sheet, I thought I had included all the information necessary for participants to make informed decisions. For example, the information sheet had a section that confirmed I would make sure participants could not be identified in any published work and that participants could withdraw from the study at any time. However, while at the design stage of the project I thought I had been clear and offered all the information needed, on reflection, that was perhaps not the case and as such will be considered in more detail in this case.

Consent forms are also crucial when conducting research (again, a web resource is offered at the end of this case and example consent forms are widely available online). The consent form I designed ensured that participants had received the information sheet, that they explicitly consented to taking part in the research, and that they understood they could withdraw at any time without needing to give a reason for withdrawing. Most universities and colleges ask to see the information sheet and consent form as part of the ethical approval process and offer clear advice and guidance about how these important documents should be set out and what must be included.

Participant Recruitment

I recruited participants from a university in England. During social work training in England, students complete two extended work-based placements. I specifically targeted students who had completed their first placement, as this group was at the halfway point in their training and I felt that they would be able to describe how their use of social media may have changed or been adapted during the early stages of their training. To operationalize this approach to recruitment, I used the university’s Virtual Learning Environment (VLE) to promote my study and provide information that students would need to make an informed decision about taking part. This included the information sheet along with my contact details so that trainee social workers could find out more and ask any questions.

Difficulties With Recruitment

The first challenge was recruiting a sufficient number of trainee social workers. After I first promoted my study on the University’s VLE, only two trainee social workers came forward to be interviewed. Because I got such a low response, I specifically asked the first two participants why they thought so few of their fellow trainees were interested. Through these conversations, it became apparent that confidentiality was a significant concern for potential participants. I had included a section in my information sheet about confidentiality and how I would change names in any published work. However, on reflection, it is apparent that I did not offer sufficient detail. I therefore added more detailed assurances to the VLE about the purpose of the study, why I felt it was an important area of research, and how I would ensure that individuals could not be identified and that the teaching team at the university would not have access to the data (e.g., the interview recordings, transcripts).

I did not expect the level of concern about confidentiality and did not anticipate it to be a barrier to recruitment or to information gathering. However, my additional assurances about anonymity led to more students coming forward, although this problem persisted throughout the data-gathering stage of the study. For example, after more students came forward to be interviewed, one participant asked about halfway through the interview, “Will the lecturers know I have talked to you about this?” with another asking, “Will the lecturers get a copy of this interview?” I had not fully understood how reticent students would be, and although this is not explicitly considered in the published research article, it is something I have reflected on.

Trainee social workers spend much time during their training considering issues of confidentiality and the ethics of sharing information. As the participants were only halfway through their training, they were perhaps very sensitive to these issues but may not have had a fully developed understanding, especially about my ethical responsibilities as a researcher. This may have contributed to the difficulties I had recruiting participants. They may also have been concerned about whether what they said during the interview would impact their training, perhaps fearing that any negative comments would lead to lower marks for their academic work. This highlights how this group of participants may have experienced having less power than the researcher. It also connects to one of the findings included in the published work from this research that the trainee social workers who took part in this study were concerned about how their social media presence may impact negatively their future employment prospects.

In the future, when I carry out research with students, I will be much clearer about how I will protect them as participants and about my ethical responsibilities to them as a researcher. I will also give more consideration to issues of power and how participants may be concerned about the impact of both being involved in research and declining any invitation, as these are both issues that may not have been explained to potential participants in sufficient detail initially during this project. The problems I encountered illustrate the importance of offering clear detailed information to potential research participants. It also highlights the importance of ongoing reflection during the research process.

By reflecting on my approaches to recruitment, I was able to identify potential weaknesses in the strategies I had adopted and possible gaps in the information I had offered. It is important during any research project to reflect on action. Reflection on action means thinking about what you have done and how you can improve. People often do this when something has gone wrong or has not worked as expected (Thompson & Thompson, 2018). However, good research practice requires that researchers reflect on action throughout a project. It is also important at the end of any research project to evaluate what you have done. This means thinking about how you can improve what you do in the future and also what you have learned (Whittaker, 2012). In this case, by reflecting on action, I realized that in the future, I need to offer more detailed relevant information to potential research participants so that the information provided meets the needs of the participant group being researched. My research skills had improved as I had learned through experience how power difference can impact participant recruitment and research findings.

Methods in Action

Interviewing.

Having recruited participants, the next stage was to meet with them, formally gain their consent and then interview the trainee social workers. I met with the participants individually in an office on the university campus. Before starting the formal interview process, I spent time talking about the study and trying to build rapport with them, trying to put them at ease. This is important generally when working with research participants, as it is likely to be an unfamiliar situation for them and so drawing on your interpersonal skills can help the research endeavor and support your participants into a (probably) new experience.

Using semi-structured interviews worked well and enabled me to gather detailed rich contextual data that helped me to understand how trainee social workers used social media and some of the difficulties they experienced.

The main interview questions were as follows:

  • How do you use social media?
  • How has your use of social media changed during your training?
  • How could the university support you make full use of the opportunities that social media offers while helping you develop as professional social workers?

I asked follow-up questions to probe areas of interest and to help students offer fully developed answers. For example, participants would often offer a list of social media sites that they used (e.g., Twitter and Facebook) along with a list of how they used social media. In these instances, I would follow up by asking questions such as “Do you use the sites for different things?” as this helped me to gain a deeper understanding of how they used social media. As highlighted earlier, many participants needed reassurance about how I would maintain their anonymity, but once this was done, they appeared to be very open about how they used social media.

Most interviews lasted approximately 30 min, but two were much longer (40 and 50 min). As outlined earlier, several of the research participants remained concerned about whether lecturers would get a copy of the transcribed interviews or find out that they had been involved in the research project. To offer further assurances (and as a matter of good practice), at the end of each interview, I explicitly asked each participant whether they were still happy to be involved with the project and reminded them of their right to withdraw. This enabled participants to ask further questions if they still had concerns. It also demonstrated to the participants that I was being transparent and was taking their concerns seriously. Having collected my data, I needed to move on to the analysis stage.

Data Analysis

Researchers should consider data analysis all the way through a research project; it is not a separate job to undertake after the interviews (Hardwick & Worsley, 2011). One of the ways I did this was to reflect on action. During the interview process, I thought about and reflected on the quality of the data I was gathering and the depth of the responses I received to the questions I was asking. This helped to make sure that I was gathering data that were rich in contextual detail and would enable me to answer the research questions. It also creates a space for the researcher to think about how their skills as an interviewer are developing and about how they can improve. However, I present the research process here as a linear task. In real life, it is perhaps messier than that, but it is often helpful to learn the stages one at a time.

Stage 1—Immersion in the Data

The first stage I used in this project was to transcribe my interviews. Transcribing is the process of listening to the (digital) recording of the interviews and typing them up into a word-processing document. I decided to transcribe the interviews myself as by listening to the interviews and typing them up, a researcher gets very close to the data. It is possible to pay to have interviews transcribed and this can be helpful for larger projects or if there are time constraints. However, I find that transcribing interview recordings myself helps with the analysis of the data.

There are two main approaches to transcribing interviews: full verbatim and intelligent verbatim. Full verbatim transcribing means including everything that is said by both the researcher and the participant. For example, all the erm s and um s! Intelligent verbatim leaves things like false starts to sentences and all the erm s out. I used the full verbatim approach as intelligent verbatim does not always let the researcher understand when a participant is struggling to answer a question or is perhaps reluctant to respond to a question. This is especially the case if you have paid to have your interviews transcribed and so have not listened to the recordings yourself.

Once typed up, the researcher then reads the transcripts over and over again. By carrying out this first stage, the researcher will become immersed in the data (Braun & Clarke, 2006). While reading, the researcher looks for meanings and patterns in the data, which helps him or her move into the second stage of the analysis process.

Stage 2—The Analytical Matrix

The second stage is to start to generate initial codes. The purpose of coding is to identify features in the data. To support the coding process, I used an analytical matrix (Cartwright, 2017a). To create the matrix, I used Microsoft Excel. I gave each participant a number, listing each in its own row in the left-hand column; then, I allocated initial codes in the row across the top of the spreadsheet. See Figure 1 for an example of the analytical matrix I used. For example, Figure 1 shows that I had an initial code to identify which social media platforms my participants used. This is a very basic code but is a good example of how to start to code data.

Data shown in the image are given in a tabular form below:

Figure 1. Example analytical matrix adapted for publication.

An image in a tabular form shows an example of the analytical matrix with three participants and types of social media, awareness of privacy setting, support learning.

This approach enabled me to develop an initial visual representation of the data. Once I had developed my initial codes, I moved on to the next stage of the analysis process.

Stage 3—Developing Themes

Once all the data are coded, the researcher will be able to start to develop themes. At this point, most researchers will probably have a long list of initial codes. By spending time with the data, though, researchers can start to see how some of the codes are connected. Then, they can collapse the codes into themes (Cartwright, 2017a). As Braun and Clarke (2006) state “[e]ssentially, you are starting to analyse your codes, and consider how different codes may combine to form an overarching theme” (p. 19). I color-coded my analytical matrix to help me visualize how codes were connected.

Listening to the recording of the interviews again can also help researchers to understand how codes may be connected and come together to form a theme. While I used the approach described as verbatim transcribing, listening to the recordings helped to remind me when participants took time to respond to a question suggesting they may be conflicted about how to answer. It also allowed me to recognize times that participants were particularly enthusiastic about something they were telling me.

Stage 4—Review

This is the final stage before writing up the findings. By using the analytical matrix to create a visual representation of my data and by color-coding the matrix into themes, I was able to identify themes that were not fully supported by the data during the review stage. I was also able to identify themes that could be joined together to be one stronger theme. For example, I had identified a theme that was initially labeled as “changing behaviour” and another that was labeled as “changing profiles.” While reviewing these themes, it became apparent that the two were connected and could be joined together to form a much stronger theme. Researchers may find themselves going back and forth between these stages several times until they are satisfied that they have fully analyzed the data.

Practical Lessons Learned

Qualitative research often involves gathering rich deep data (McLaughlin, 2012). If this study had made use of a quantitative method such as a survey rather than semi-structured one-to-one interviews, it is likely that I would have had a much larger number of trainees engaging with the project. This is because the participants could have completed a survey anonymously and issues of confidentiality would have been easier to address. However, using semi-structured interviews allowed me some flexibility when gathering data so that I was able to ask follow-up questions to explore topics in more depth. This is one of the strengths of qualitative approaches generally (Hardwick & Worsley, 2011). Interviewing participants will generate a significant amount of data. Thematic analysis as a method helps researchers to make sense of and manage the data.

However, using interviews for data collection can be problematic. For example, this approach affected my ability to recruit participants. When starting a research project, it is important to spend time thinking about how participants may feel about being involved with the project. Each participant group will have vulnerabilities and it is the researcher’s responsibility to consider this when designing a study. By thinking about participants’ needs, a researcher may also increase the number of people he or she is able to recruit into the study. Recruiting participants can be very challenging, but the more a researcher can do to reduce or remove barriers to participation, the easier recruiting participants will be.

As described earlier in this case study, it is important to reflect on action so that the approaches taken during a research project continue to enable research questions to be addressed and so that any weaknesses that emerge can be dealt with. In this case, the main difficulty related to barriers to recruitment that appear to have been caused by insufficient information being provided to potential research participants. By engaging with the early responders to my call for participants about the difficulties with recruitment, it was possible to adjust the information provided to manage this difficulty. In addition, by explicitly talking to participants during the interview stage, it was possible to offer further reassurances that appeared to support the collection of deep rich data.

The most significant learning, for me as a researcher, was that it is important when promoting a study and recruiting participants to consider, as far as possible, the participants’ needs. I needed to be much more explicit about how I would protect the data I was collecting and that the participants’ training would not be impacted by taking part in the research. In other words, I needed to better account for the power difference between them as students and me as a researcher. I needed to offer more reassurance that their academic grades would not be affected and that the teaching team at the university would not have access to the interview data or details about who had been involved with my research. I had not considered in sufficient depth how being interviewed about social media use potentially made the participants feel vulnerable. (One of the findings from my study was that trainee social workers worried about how their social media presence may impact their future career.) By reflecting on action and evaluating the approaches taken, I was able to identify how my future research projects could be improved, supporting my own learning and development as a researcher.

Once what to research has been decided, a researcher needs to spend time thinking about a number of critical issues. This case study has presented these issues in an order designed to help with this process starting with the most appropriate type of data to collect. (For my study, qualitative data were most useful as I wanted to develop an understanding about how trainee social workers used social media and how their use had changed during their training.) The case study then moved through the stages leading to a detailed description of how to analyze the data.

The method used for data analysis in my study (thematic analysis) is a straightforward and accessible method. I found the approach enabled me to identify important patterns in the data that led to some interesting findings—for example, the concern the trainee social workers had about confidentiality and their worries about how their social media presence could impact on their future employment. By reflecting on the research process and my findings, I can now see that these issues are connected.

The barriers I experienced when recruiting participants reflects this group of trainee social workers’ experiences of how the university they attended had tried to highlight some of the dangers of social media (something I consider in the published research article). This amplified their existing concerns, leading to reluctance to engage in my research project. This highlights why it is important to think about research from the participants’ perspective if possible. Doing this may help to recruit people into a study, while also ensuring that the researcher can demonstrate engagement with his or her institution’s ethical approval process.

Exercises and Discussion Questions

  • 1. Why is it important to reflect on action during the research process?
  • 2. In your own words, describe the stages used in thematic analysis. Then in small groups discuss the challenges you might experience with each step.
  • 3. In small groups, discuss the advantages and disadvantages of semi-structured interviews.
  • 4. Why do you think participants were so concerned about confidentiality? What would you suggest could be done to address their concerns?
  • 5. In small groups, write a short paragraph that could be included in an information sheet that would address the problems with confidentiality identified in this case study.

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Towards decolonising higher education: a case study from a UK university

  • Open access
  • Published: 29 December 2023

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  • Nancy Tamimi   ORCID: orcid.org/0000-0001-7812-815X 1 ,
  • Hala Khalawi 2 ,
  • Mariama A. Jallow 1 ,
  • Omar Gabriel Torres Valencia   ORCID: orcid.org/0000-0002-9290-5784 1 &
  • Emediong Jumbo   ORCID: orcid.org/0000-0001-9840-9186 1  

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This article presents initiatives undertaken by the Department of Global Health and Social Medicine (GHSM) at King’s College London (KCL), exploring avenues to decolonise higher education institutions (HEI). HEI must integrate anti-racism agendas, challenge the European-centric academic knowledge domination, and dismantle power asymmetries. During the academic year 2021, GHSM executed (1) a gap analysis of undergraduate modules, (2) a course on decolonising research methods taught by global scholars to 40 Global South and North university students who completed pre- and post-course surveys, and (3) semi-structured interviews with 11 academics, and a focus group with four students exploring decolonising HEI; findings were thematically analysed. (1) Gap analysis revealed a tokenistic use of Black and minority ethnic and women authors across modules’ readings. (2) The post-course survey showed that 68% strongly agreed the course enhanced their decolonisation knowledge. (3) The thematic analysis identified themes: (1) Decolonisation is about challenging colonial legacies, racism, and knowledge production norms. (2) Decolonisation is about care, inclusivity, and compensation. (3) A decolonised curriculum should embed an anti-racism agenda, reflexive pedagogies, and life experiences involving students and communities. (4) HEI are colonial, exclusionary constructs that should shift to transformative and collaborative ways of thinking and knowing. (5) To decolonise research, we must rethink the hierarchy of knowledge production and dissemination and the politics of North-South research collaborations. Decolonising HEI must be placed within a human rights framework. HEI should integrate anti-racism agendas, give prominence to indigenous and marginalised histories and ways of knowing, and create a non-hierarchical educational environment, with students leading the decolonisation process.

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Introduction

The twenty-first century has seen resurgent and insurgent decolonisation at a global level. Movements such as “Why is my curriculum so White” (Peters, 2018 ), “Rhodes must fall” (Chaudhuri, 2016 ), and “Black lives matter” (Francis & Wright-Rigueur, 2021 ) ignited calls for decolonising higher education institutions (HEI) and “intellectual decolonisation” by challenging the colonial legacy within academia including curricula, pedagogies, classrooms, research methods, and knowledge production (Bhambra et al., 2020 ; Gopal, 2021 ; Hlatshwayo, 2021 ; Moosavi, 2020 , p. 332; Peters, 2018 ; Phoenix, 2020 ; Tuck & Yang, 2021 ).

In this article, we seek to contribute to broader decolonisation projects across the globe by discussing our efforts towards a decolonised HEI. We first reflect on the contested term decolonisation, followed by an overview of the decolonisation efforts in HEI in the United Kingdom (UK). We then present a few of our decolonisation initiatives in the Department of Global Health and Social Medicine (GHSM) at King’s College London (KCL) in the UK. Our initiatives include (1) a gap analysis exercise of undergraduate core modules, (2) a pilot course on decolonising research methods, (3) interviews with academics and a focus group with GHSM students on decolonising HEI, (4) a workshop with GHSM students and academics on decolonising curriculum and research methods, and (5) a public symposium on decolonising knowledge production. Due to the limited word count, this article focuses on the first three initiatives. The projects were funded by KCL and conducted by members of the GHSM Anti-racism Steering Group (ARSG), founded in 2020 following students’ request to recognise racism and address the Black and Minority Ethnic (BME ) Footnote 1 student attainment gap.

Positionality statement

This article is written from the position and experience of academics and students affiliated with universities primarily in the Global North, except for HK, an Arab Muslim woman who studied in the USA and UK and currently resides in the occupied Palestinian territory. NT is a Palestinian-British Muslim woman. EJ is a Black British Nigerian woman. MAJ is a Black African woman. OGTV is a multi-ethnic Mexican gay man.

It is vital to highlight that reflexivity has been at the core of our work; we acknowledge that our desire to decolonise risks compliance or maintenance of colonial, neo-colonial, and post-colonial monolithic global discursive asymmetry of power structure (Abdelnour & Abu Moghli, 2021 ; Chakraborty et al., 2017 ). While we feel uncomfortable using the divisive Global North (GN) and South (GS) terminology, we aim to show that we actively reach out to voices missing from our curriculum. We endeavour to challenge our unconscious biases and confront the West colonial past and “how its legacy continues to create inequities and injustices in the world we live in today” (Wong et al., 2020 , p. 3).

Our work is informed by critical race theory (CRT), an analytical framework that addresses racial inequities in societies. CRT’s five tenets are (1) counter-storytelling that “legitimizes the racial and subordinate experiences of marginalized groups”; (2) the permanence of racism, where “racism controls the political, social, and economic realms of society”; (3) whiteness where whites have the right of usage, enjoyment, disposition, and exclusion; (4) interest conversion where whites are the main beneficiaries of civil rights legislations; and (5) the critique of liberalism linked to “colour blindness, the neutrality of the law, and equal opportunity for all” (Hiraldo, 2010 , p. 54). CRT helps to understand the systemic racism that embeds in our societies and “the manner in which supposedly race-neutral institutions, systems, policies, and practices maintain white supremacy” (Crenshaw et al.,  1995 in Kelly et al., 2020 , p. 1372).

The roots of decolonising higher education

“Decolonisation” is a contested term that has a multiplicity of heterogeneous definitions, interpretations, aims, perspectives, and approaches, encapsulating different political, economic, cultural, philosophical, and epistemic dimensions, tracing 500 years of history (Adefila et al., 2022 ; Bhambra et al., 2018 ; Hayes et al., 2021 ; Bhambra et al., 2020 ; Pete, 2018 ; Von Bismarck, 2012 ).

It is essential first to understand colonisation and its legacies, characterisations, forms, and practices (Adefila et al., 2022 ). The Peruvian thinker Aníbal Quijano developed the concepts of “coloniality of power” and “coloniality of knowledge” and defined “Eurocentered colonialism” as the direct colonial domination Europeans practised over the political, social, and cultural dimensions of the conquered across the globe (Quijano, 2007 , p. 168). Quijano described four interrelated domains forming the “colonial matrix of power,” where Eurocentered colonialism controlled (a) colonies’ economy through land appropriation, exploitation of labour, and control of natural resources; (b) authority through institutions and army; (c) gender and sexuality by dominating family and education; and (d) subjectivity and knowledge by dictating epistemology and formation of subjectivity (Mignolo, 2007 ; Quijano, 2000 , 2007 ).

While direct political colonialism diminished, the specific colonial structure of power created systems of social discrimination, where colonisers were ranked at the top of social and economic structures, impeding the cultural production of the dominated by repressing “modes of knowing, of producing knowledge, of producing perspectives, images, and systems of images, symbols, modes of signification, over the resources, patterns, and instruments of formalized and objectivised expression, intellectual or visual” (Quijano, 2007 , p. 169).

There is no consensus on defining decolonisation and what it entails (Gopal, 2021 ; Von Bismarck, 2012 ). Mignolo, for example, argues that “the major and vital move is to delink from the “colonial matrix of power” (Mignolo, 2018 ; Nanibush, 2018 ). While Fanon sees decolonisation as a violent process where the colonised liberate themselves politically and psychically (Fanon, 1971 in Etherington, 2016 ), Adébísí, in turn, asserts the vitality of understanding the contextual-based evolutions of decolonisation’s theories and sees that decolonisation “seeks the abolition of the ongoing and evolving structures of violent exploitation including the epistemologies that keep them in place” ( 2023 , p. 15).

The varied discourses of decolonisation were rooted in colonised countries challenging imperialism during the colonial era (Mignolo, 2011 ; Zembylas, 2018 ). Although “intellectual decolonisation” gained prominence in the GN since 2014/15 (Moosavi, 2020 , p. 332), decolonising movements emerged in the early twentieth century when Black and Asian anti-colonial and liberation scholars in India and Africa called for intellectual resistance for freedom and independence from British rule, to challenge the domination of Euro-centric thoughts (Arday and Mirza, 2018 ). Scholars from across the globe have significantly contributed to decolonial thinking, such as the French West Indian psychiatrist Franz Fanon (1925–1961), the Jamaican British feminist Una Marson (1905–1960), the Malaysian intellectual Syed Hussein Alatas (1928–2007), the Nigerian intellectual Claude Ake (1939–1996), and the Palestinian-American Edward Said (1935–2003).

Decolonising higher education

Neo-liberalised HEI are considered spaces that intensify “the logics and rationalities of coloniality,” built upon a “violent monologue” denying and violating the “knowing-be-ing” of other races (Motta, 2018 , p. 25). Decolonising HEI requires “confronting the white occupation of academic knowledge and unsettling its grip over mundane as well as high stakes decisions” (Zeus Leonardo in Arday and Mirza, 2018 , p. 3), besides the need for “congruent social processes that support human rights and inclusive knowledge generation” (Kennedy et al., 2023 , p.1). Oppression and marginalisation practices, such as sexism, racism, and Islamophobia, are reproduced through educational processes (Osler, 2016 ). Therefore, decolonising HEI must embed human rights principles, including universality, indivisibility, equality and non-discrimination, participation, and accountability (UN Sustainable Development Group, 2023 ). Adébísí ( 2023 , p. 33) asserts that “[D]iversifying the face of coercive power is not the same as dismantling it,” confirming the need to interrogate the “entanglement between knowledge and power, across space–time, as well as the evolution of resistance to this entanglement.” While there is no consensus on how a decolonised HEI looks, GN and GS HEI started movements to decolonise curricula, pedagogic practices, and institutional cultures.

HEI increasingly recognise they are key sites where coloniality occurs and Western knowledge is “produced, consecrated, institutionalised and naturalised” (Bhambra et al., 2018 , p. 5). Discussions around curriculum involve three levels: explicit, hidden, and null curriculum (Le Grange, 2016 ). The explicit curriculum is what is presented to students, such as reading lists, assessments, and modules’ frameworks. The hidden curriculum is the underlying “unspoken or implicit values, behaviors, and norms” that shape the dominant university culture (Alsubaie, 2015 , p. 125). The null curriculum is what is missing from the curriculum (Le Grange, 2016 ). It is essential for decolonising initiatives to address all curriculum levels.

Scholars also highlighted three concepts to consider when designing decolonised curricula and pedagogic practice: (1) epistemic silences that marginalise indigenous/local cultures, (2) negation that excludes non-Western theories and impose Western ideals, and (3) grand erasure that erase the experiences of most people around the world (Gaio et al., 2023 , p. 4). Centring voices of the sufferers of inequity in curricula improves the learning experiences and strengthens the epistemological power of Black, indigenous, and GS students (Ahmed-Landeryou, 2023 , p. 4).

Several UK HEI embarked on efforts to decolonise curricula, for example, SOAS ( 2018 ) “Decolonisation Toolkit,” UCL ( 2018 ) “Inclusive Curriculum Health Check,” Kingston University London ( 2020 ) “Inclusive Curriculum Framework,” and the University of Brighton ( 2019 ) “Decolonising the Curriculum: Teaching and Learning about Race Equality.” Most of these efforts do not explicitly explain how they developed their frameworks from evidence; hence, Ahmed-Landeryou ( 2023 ) conducted a scoping review to guide decolonising curricula. Ahmed-Landeryou introduced an evidence-informed framework highlighting key themes, such as the need to teach and learn about race inequality, introduce innovative assessments, secure leadership commitment and investment, make structural changes, involve students in change-making, and create meaningful outcome measures.

Although decolonisation is not exclusively about diversifying reading lists (i.e., the explicit curriculum), it is often the first step HEI employ. Evaluating reading lists often focuses on who are the dominant voices in disciplines, which voices are (intentionally) excluded, and what counts as legitimate knowledge. For instance, an evaluation by Schucan Bird and Pitman ( 2020 ) found an equal proportion of women and men authors within social science-based reading lists in one UK university. However, over 90% of authors were identified as “non-BME,” and 99% were affiliated with GN institutions. On the science-based reading lists, 70% of authors were men, 65% were identified as White, and 90% were based in GN.

Barriers to diversifying reading lists and decolonising curriculum efforts are contextual and structural, including rigid institutional policies, lack of leadership support, lack of access to resources including knowledge, funding and personnel, difficulty in identifying pure local or indigenous knowledge, academics feeling overwhelmed, overworked, and underpaid, and lack of recognising power dynamics around issues of gender, race, immigration status, and class (Loyola-Hernández & Gosal, 2022 ; Shahjahan et al., 2022 ). Laakso and Hallberg Adu ( 2023 ) presented specific challenges linked to decolonising curricula in Botswana, Ghana, Kenya, and Zimbabwe, including resources scarcity for research, lack of opportunities to publish research, difficulties in finding and producing textbooks with locally relevant perspectives, bureaucratic obstacles to accepting new course content, “the prevailing hegemonic structures of global academia and the subordinate position of African universities” (p. 13). Other scholars further highlighted limitations of “intellectual decolonisation,” such as ignoring GS decolonial theories, “reducing intellectual decolonisation to a simple task; essentialising and appropriating the GS; overlooking the multifaceted nature of marginalisation in academia; nativism; and tokenism” (Moosavi, 2020 , p. 332).

Regarding decolonising research methods, indigenous scholar Smith describes conducting research with indigenous communities, placing their voices and epistemologies at the centre. Scholars argue for (re) gaining control over indigenous ways of knowing and being, critiquing traditional research approaches to indigenous life that historically addressed the concerns and interests of non-indigenous scholars, marginalised, oppressed, and dismissed non-Western knowledge production, and for creating new approaches of equal collaboration and participatory research, where power is located within the indigenous practices (Bishop, 2005 ; Datta, 2018 ; Denzin et al., 2008b ; Keikelame & Swartz, 2019 ; Smith, 2021 ).

Our projects contribute to the literature and ongoing efforts toward decolonising curriculum and research methods. Next, we present our initiatives and findings. We conclude with the discussion and our outlook.

During the academic year 2021, GHSM embarked on several decolonisation initiatives:

A gap analysis exercise of all GHSM undergraduate (UG) core modules for 2020/2021. Footnote 2 We recruited three UG student research assistants who evaluated seven core modules, supervised by NT and another lecturer. The weekly reading list containing both core and recommended readings was exported to an Excel sheet, extracting the following information: the study location, author's ethnicity, gender, and institutional affiliation.

A 3-day intensive hybrid course on decolonising research methods, advertising the course on KCL social media platforms and across our global networks, including social media, and inviting experts on decolonisation from KCL and GN and GS institutions to teach the course. The lecturers were paid £100 honorarium. They came from Cape Town, Latin America, Colombia, The occupied Palestinian territory, Australia, New Zealand, and the UK. The course was led by NT and two research assistants, OGTV and HK. The team developed, implemented, and evaluated the course. A handbook was developed with the invited speakers (Supplementary Material 1 ). The invited speakers engaged in person and virtually with the attendees using different techniques to encourage interaction. Attendees had access to the recorded lectures (by speakers’ authorisation). The funded course was open to GHSM and partner universities’ students. Forty students attended the course: 23 from KCL, 12 from GS universities, five from GN universities, and three from GS non-government organisations. Sixteen participants completed the baseline questionnaire collecting demographic data, baseline knowledge, and course expectations. Twenty-five completed a post-course evaluation.

Semi-structured interviews discussing decolonising HEI with 11 academics and a focus group with four students (sample characteristics, Table 1 ). Interview participants included symposium and course presenters and GHSM academics who were approached via email. Focus group participants included GHSM students who were invited to partake by email and social media platforms. Although we invited all students, only four third year, non-white students joined the focus group. On reflection, we must increase the awareness of the colonisation’s effect on HEI among our students to encourage their engagement. Open-ended questions for interviews and the focus group explored motivations to join the research, the meaning of decolonisation, the vision of a decolonised HEI, decolonising the curriculum and research methods, and barriers to decolonisation (Topic guide, Supplementary Material 2 ). Interviews and the focus group were audio recorded and transcribed by MJ. NT applied thematic analysis, as Braun and Clarke ( 2006 ) described, while HK reviewed the codes and themes to increase validity. To strengthen validity and reflect decolonisation values, we invited participants to provide feedback on this article before submission.

One participant asked to be identified, but we must adhere to the ethical approval requirement of anonymity. Ethical approval was obtained through KCL minimal risk process. We were aware that some participants might have experienced kinds of oppression, such as racism or sexism. Hence, the participation was voluntary and anonymised; participants knew they could stop the interview if they felt uncomfortable. Interviewing the HEI management group would have enriched our findings. However, we were interested in exploring students’ and academic perspectives before conducting a future study with the management.

The findings

The findings of the undergraduate core module gap analysis

To explore the diversity and global representation in our reading materials, we addressed the following questions:

Who are we learning from?

How central are different gender and ethnic perspectives to our curriculum?

Which places are we learning about?

Who constitutes our experts?

Our review highlighted four critical areas of concern. (1) A tokenistic use of BME, women, and declared non-binary authors across the modules’ readings. In the first year modules, there were 599 authors. 39% were women, 59% were men, and 2% declared non-binary. BME authors did not constitute above 30% of the total readings. In the second and third year modules, there were 546 authors. 41% were women, 56% were men, and 3% declared non-binary. (2) Most of the perspectives originated from men and non-BME authors. In the first year modules, 22% of authors were identified as BME and 78% as white. In the second and third year modules, 18% were identified as BME. Women only appeared as first authors in 36% of first year modules and 39% in the second and third years. Most of them were in recommended readings. Similarly, BME authors only appeared as first authors 21% of the time, mainly as authors of recommended readings. (3) Most learnings were about GN or GS from GN perspectives. In the first year modules, 64% of study locations were from North America or Europe, 2% were from Oceania, and 3% from South America. In the second and third years, 68% of study locations were from GS regions, 6% were from Europe, and less than 1% were from Oceania. (4) Authors were mainly from GN. For example, in the first year, 270 were from the USA, 118 were from the UK, 17 were from South Africa, and six were from India. In the second and third years, 220 were from the USA, 187 were from the UK, 37 were from India, and 15 were from South Africa (Figs. 1 and 2 ).

figure 1

First-year core module gap analysis

figure 2

Second- and third-year core module gap analyses

There were challenges while executing the gap exercise. Some decisions might have impacted the findings. For example, when recording the geographical locations of independent scholars versus those with institutional affiliations, we classified independent scholars according to their current geographical location (country and continent). Also, we acknowledge that delving into modules might give a different perspective than the reading list suggests. However, this was beyond the scope of the mapping exercise.

The findings of Decolonising Research Methods in Global Health and Social Medicine course

From 7th–9th of June 2022, 40 participants from diverse ethnic backgrounds Footnote 3 joined the course from 10:00 a.m. to 4:00 p.m. GMT. It aimed to bring various decolonising efforts and ongoing initiatives into the conversation to explore the limits of mainstream Western research methodologies and learn about indigenous research practices and methods. The lecturers presented qualitative and quantitative research case studies. Using the decolonisation lens, they discussed epistemologies of health, ethical research practices, reflexive research, and lessons learned from indigenous practices. OGTV simultaneously translated one lecture from Spanish to English. Four key themes emerged to promote an equitable research process: Recognising power dynamics, fostering collaborative research with indigenous researchers and participants, challenging dominant knowledge paradigms and embracing South-to-South epistemologies, and including indigenous concepts within ethical frameworks.

The pre-course survey compared to the post-course survey : 16 respondents completed the pre-course survey on SURVEY MONKEY. 75% showed unfamiliarity with indigenous or non-Western research methodology. Almost 88% have not received training in decolonising research methods, and their confidence in their knowledge was either not or a little confident. Respondents wanted to enhance their knowledge and skills in decolonising and conducting research more relevant to local communities. Twenty-five respondents completed the post-course survey. All respondents either agreed or strongly agreed the course was well delivered, the topics were comprehensive and clearly presented, and the supporting materials were helpful. 68% strongly agreed, and 28% agreed the course enhanced their decolonising research knowledge. 52% strongly agreed, and 40% agreed their understanding of the subjective experience of research participants increased. 64% strongly agreed, and 36% agreed they developed an awareness of the risks of using Western research methods when researching indigenous communities. All participants strongly agreed or agreed that the course made them aware of potential biases, risks, and oppression practices while conducting research, understand what emotional safety for researchers is, and be more aware of research ethics related to university settings. The majority strongly agreed or agreed that they developed more awareness of participatory and collaborative research, positionality as a researcher, and methodologies to redesign colonial space and create more ethical research.

In the post-course qualitative part of the survey, respondents mentioned the course widened their perspectives and provided new insights. One noted, “It made me more aware of the degree to which current research methodologies and approaches do not reflect the vast different types of communities and cultures.” Participants reflected on current research practices. One respondent said, “It sheds light on many ways research methodologies are colonised/../ and ways to decolonise them. It also highlighted/../ a hierarchy of knowledge, things I used to take for granted but are so unfair and unjust.” Another respondent wrote, “Throughout my undergraduate course, I was taught Western-style pedagogy. I had no idea about the indigenous perspective or ‘the researched’ perspectives. This course, however, helped me to unlearn this aspect”. One respondent mentioned that getting exposed to decolonising research provoked frustration. However, the course provided tools and possible action plans and clarified their position as researchers.

Participants considered the emphasis on diversity and the variable backgrounds of lecturers, participants, and topics a key strength. The course was described as a safe, collaborative, and engaging space that was eye-opening to participants. One respondent wrote: “This course enabled me to learn explicitly about positionality, critical reflexivity, reciprocity, respect and power relation/../. I am thankful to the organisers.” Another respondent wrote, “It was an amazing experience. This diversity makes me think critically. It gives light and confidence to us (GS) that we have also strong resources to do research. We are not dependent on the West at all.”

We faced a few challenges during the course. First, while the hybrid provision enhanced accessibility allowing participation from outside the UK, there were a few technical issues related to the Internet connection. Second, while lecturers endeavoured to encourage interaction, online participants were less interactive than in person. Third, while we intended this introductory pilot course to be intensive, participants preferred a longer duration. Finally, we acknowledge that using English as the primary communication language and enrolling English-fluent students is an exclusionary practice that does not fit the decolonial vision. We recognise that using native languages increases the sense of belonging in the classroom and student success (Wawrzynski & Garton, 2023 ). For future courses, we would foster student communication using indigenous languages, which effectively removes structures that perpetuate inequities (Schreiber & Yu, 2016 ).

The findings of semi-structured interviews and the focus group

We identified the following five themes and subthemes.

Decolonisation is about challenging colonial legacies, racism, and knowledge production norms . Participants were aware of the heterogeneity and ambiguity of the meaning of decolonisation; however, they agreed decolonisation entails challenging colonial legacies, racism, and knowledge production norms. Student B questioned using the term decolonisation as they saw it as a way for “Western countries to enforce their ideologies” to avoid facing racism; the student thinks that “using the term decolonisation/../is steering away from the word racism.” Similarly, interviewee 9 described the language of decolonising as “ambiguous” and questioned how it relates to the anti-racism project, “we have power imbalances too, between junior and senior researchers, we have power imbalances between genders, and sometimes the colonial lens doesn’t help us address that problem/…/the legacies are there, and I see them, and it’s important to trace them through and to make them visible. But then there are a lot of power problems and silencing mechanisms that cannot be captured in the colonial terms that I still want to get at.” For interviewee 6, “There is somehow lines blur between anti-racism and decolonising and I'm not sure to what extent they're separate, overlap or the same.” Participants acknowledged histories of white domination and underscored that decolonisation is about challenging white supremacy through structural changes. Interviewee 2 asserted the need to “subvert the dominance of white culture and power.” Interviewee 8 confirmed the necessity to “dismantle the colonial gaze while simultaneously elevating the local systems, institutions, and processes.” Interviewee 11 emphasised the need for a “structural shift” by acknowledging “historiographical, residual, and also emergent complexities that we have to deal with” and “a strive towards translation and exchange of ideas and be mindful of kind of genealogies of these ideas and comparative relations that they come to generate,” and, as Interviewee 7 mentioned, by striving “towards translation and exchange of ideas.” Interviewee 8 discussed structural changes, such as publishing within the African publishing system to “decentralise knowledge production, [and] move from Eurocentric domains.”

Decolonisation is about care, inclusivity, and compensation . Some participants saw decolonisation as a form of social justice to care for others and be inclusive to all humanity. Interviewee 4 clarified, “It [decolonising] is embedded in a true sense of care.” Participants explained the importance of diversifying knowledge and the inclusion of indigenous voices. Interviewee 1 explained, “Decolonising by default requires us to include the voices of indigenous people and for them to be real people, not just in books and in our curriculum. It requires us to link up with the rest of the world differently.” Inclusivity involves conversation, as interviewee 11 emphasised that “decolonisation is about conversation/../it’s not about silencing; just putting something that has been silenced in the foreground and then silencing what has been in the foreground, putting that in the background, I think that’s a dead end.” For Student D, inclusivity means taking away all forms of “discrimination, racial discrimination, segregation, and division.” For others, decolonisation is about empowering, recompense, compensation, giving back land, and reparation. As Interviewee 6 said, decolonisation is about giving back “land,” “authority,” “sovereignty,” “history,” “artefacts,” and “money” and “paying reparations.” Similarly, Student A said, “I think we’ve gone past the point of giving people a seat at the table, and we would need to build a whole new table and replace the ones that we/../as in colonial institutions and powers have broken and taken away.”

A decolonised curriculum should embed an anti-racism agenda, reflexive pedagogies, and life experiences involving students and communities. When discussing decolonising the curriculum, five subthemes were recognised:

Embed anti-racism agenda

Participants highlighted the importance of having an anti-racism lens embedded within the curriculum. Interviewee 2 stated that decolonising curriculum should be about “consciousness-raising” and thinking about power structures. Interviewee 6 believed that “anti-racist agenda/../feeds into the decolonising agenda.” Interviewee 1 wanted to allow students to critique authors’ writings “from a racialised perspective,” acknowledging the source of our knowledge and emphasising that one way “in which colonisation exists is through the presentation of knowledge from the GS as if it has been derived solely from the GN.” Student A wanted the teaching about race to be compulsory, highlighting a lack of white students’ presence in modules addressing racism, “we only have one white person in our class now, so it's good because it’s a safe space, but it's bad because it’s just preaching to the choir, the people who should be learning about all of the history and context behind racism and especially its intersections with health are not there.”

Diversify perspectives

Participants confirmed the necessity to include diverse knowledge and perspectives when teaching the curriculum, emphasising that decolonisation efforts should not only focus on the tokenistic act of diversifying the reading list but also reflect on the content and history of knowledge. Interviewee 7 confirmed that “geographical diversity of reading material is a cop-out answer.” Interviewee 3 highlighted that when designing a curriculum, we need to think “about who writes it and whose perspective is it” and clarify to students the rationale of having a specific reading list. Student D believed that decolonisation should look at “what kind of knowledge is shared” and learn about the colonial past to be “more truthful, more accurate, and more representative of the history.” Student C confirmed the need for “diverse readings that are, for example, by authors from more diverse backgrounds and academics who are probably not from like the West.” Interviewee 11 asserted that all knowledge is valuable and warned against demolishing the current curriculum: “Just because something of political or historical context has been overlooked and oppressed doesn't mean that paper is suddenly not valuable.”

Embrace reflexivity

Participants confirmed that reflexivity must be at the core of curriculum decolonisation, where students and staff reflect on their positionalities, biases, and histories, paving the way for including all kinds of knowledge. Interviewee 7 called for “commitment to critically reflect on how colonialism continues to pervade the global health field,” including our disciplines, research, teaching, and higher education. Interviewee 9 believed that reflexivity through “pointing out your own limitations and constraints and failings is actually the road of creating spaces for others to speak.” Interviewee 4 suggested reflecting on the reasons for teaching particular subjects, their relevance to the local community, and responsiveness to multicultural students. Interviewee 6 reflected on extracting indigenous methods to decolonise curriculum: “To what extent are we just extracting methods that really fit very nicely our sort of modernisation of the curriculum? They just fit very nicely into what we're trying to do anyway. How practical that we can now take it again from the GS and extract and apply it here in order to improve our own sort of pedagogical practice.” Positionality was a key concept to participants, as Interviewee 6 confirmed the need to “acknowledge where we are from and the privileges that this brings [and] the limitations.” Interviewee 5 asserted that students and staff must embrace reflexivity “to think about their own positionality, their own power, their own practice.” Interviewee 10 said, “I always say at the beginning of my class, it’s uncomfortable for me /../ We don’t have someone to teach you this who is Black, who has that experience. So, I have a choice of not teaching you/../ or doing so as a white man.”

Incorporate life experiences and communities

Participants highlighted that the curriculum should link to life experiences and communities (Interviewees 2, 3, 4, 5). Interviewee 2 pointed to including pedagogies with a “real-world element to it,” leading to an action embedded within communities and mutual learning between students and academics. Interviewee 3 highlighted the usefulness of connecting to indigenous movements to guide the decolonising process. Interviewee 5 mentioned that students must speak to communities and indigenous people and learn from them, “It could be really amazing to have people who are much more embodied and community practice-oriented talking about decolonising /../ breaking the structures we’re in, the patriarchal colonial imperial structure as they understand it and reconfiguring ourselves as humans.” Interviewee 4 asserted that community should be at the heart of a decolonised HEI where community members sit on university boards to understand “what an ordinary man/../ understands about decolonising.”

Engage students

Participants confirmed students’ role is vital in decolonising the curriculum as they should reflect on the curriculum and their learning experience and be active agents of change. Interviewee 8 wanted students to reflect on the colonial legacies and “be able /../ to name our own intersections, our position, where we come from.” Interviewee 6 asserted the need to teach the history of our disciplines “to make students fully aware that they are entering into a colonial system/../ they are becoming part of it /../ it is their responsibility how they carry that knowledge.” Interviewee 9 encourages students to reflect and be proactive: “I would invite the students to actually create spaces where they can challenge my way of knowing, where they can bring their own interests and priorities to say what we should be talking about.” Interviewee 6 highlighted the need to learn from GS students about their countries, histories, challenges, social structures, and norms.

Higher education institutions are colonial, exclusionary constructs that should shift to transformative and collaborative ways of thinking and knowing

When discussing barriers and vision to decolonise HEI, we identified five subthemes:

HEI are colonial, exclusionary constructs

Participants agreed that the most significant barrier to decolonising HEI is their colonial structures and rigid ways of knowing dominated by white Eurocentric thoughts. Interviewee 1 described the education system as a “capitalist model,” part of the “racism agenda,” that adopts “masculinist norms” building on “patriarchal controlling structures /../ with a very narrowly defined concept of knowing/../ which doesn't create that many opportunities for new ideas.” Interviewee 7 said most university disciplines “have their foundations in very white Eurocentric scholarly traditions.” Student C also said that “these institutions being formed on colonial bases is something that really hinders the [decolonisation] process.” Interviewee 2 believed that some HEI structures are “oppressive/../rooted in colonising practices/.. and linked to prioritising certain people’s knowledge” while excluding others. Interviewee 2 also mentioned barriers such as “universities being a business,” “the university hierarchy,” and “the race pay gap.” Interviewee 6 talked about the nationalistic nature of HEI and the “bureaucratic political hurdles” that hinder decolonisation and enmeshment with GS institutions, “We work in highly structured institutions that have been developed during colonial times, and we’re only able to function the way they functioned because of these sorts of colonial ties, right? This is how we gained global knowledge. This is how disciplines like anthropology emerged/../. it’s along these colonial trajectories. This is how we came to know the world. Of course, there’s history before that, but still. And so, I think our institutions don’t really permit enmeshment because they’re very nationalistic.”

Attitudes towards decolonisation

Several attitudes were identified as barriers, such as shallow engagement with the decolonisation discourse, fear of change, and lack of time and funds. Student A highlighted the need to move from creating “a buzzword” or, as the Notetaker Student said, “a tick box exercise” where universities measure their diversity by the number of BME-hired people. Others talked about the apprehension and resistance to change, which Student C described as “a major hindrance.” Interviewee 1 talked about resistance to change: “We’re at the mercy of people at the political level who have a vested interest in maintaining the hegemony that has been established because if we were to get radical transformation, it would also mean that the people in power are no longer in power.” Interviewee 2 talked about some academics working on decolonisation to build careers rather than making a change. Interviewee 7 mentioned “the lack of rewarding.” Interviewee 1 pointed to insufficient time available to staff to work on decolonisation, the lack of funds, and “defaulting on the racialized people to do the hard craft,” primarily women, as Interviewee 5 indicated.

Collaboration with global south

Participants discussed the unequal nature of working with GS institutions; they envisioned a decolonised HEI with equal collaborative opportunities. Interviewee 11 said that we are an “elite institution” in a “powerful country” and must engage with GS institutions “on a much more equal ground.” Interviewee 9 talked about practising the concept of “mutuality” in partnerships and institutionalising humility by creating a “space of mutuality /../ where you have equity and equitable relationships at the bottom of everything/../ the fact that you’re not the expert while being fully aware that you have the resources, the funding, the building, the branding power.” Interviewee 6 wanted institutions to provide fellowships and exchange with GS scholars “but according to the terms of those ones needing and wanting to extract whatever it is that they want to extract. That has to be financed by us as a form of reparation and trying to make good in terms of what we have done so horribly wrong in the past.” Interviewee 5 wanted to see more GN and GS staff exchanges to learn from others.

Academics’ diversity

Academics’ diversity and making space for talented people of colour were mentioned as essential steps towards decolonising HEI. Interviewee 7 explained that the high fee is a barrier for BME students to join universities, hence the lack of BME scholars. Interviewee 6 highlighted, “Right now, we’re looking like the snowy white tops/../it becomes very white on top/../ that needs to change.” For Interviewee 10, a decolonised HEI “would have more Black and minority ethnic people in positions of power and authority.” Students also recognised the lack of diversity in HEI. Student B questioned, “Where do most of our lecturers come from? Where do they get their degrees, who can get into those institutions, and what hinders other people from getting into those institutions or getting to that level of education? Is it socioeconomic factors? Is it their location?” Similarly, student A also commented on the lack of academic diversity, “we’re going to an institution with a large colonial history, a very capitalist outlook on the world, and the professors, especially the older ones, have the same outlook so obviously they’re going to have their biases as they teach which will anyone would, but it would be nice to have a diverse amount of biases when teaching rather than the status quo.”

Fostering decolonisation

Participants acknowledged the importance of forwarding the decolonisation agenda within universities. They suggested creating informal spaces for debate and discussions. Interviewee 1 suggested having “sufficient pockets” in every university focusing on decolonisation and “to incrementally reduce some of the fear [of change].” Interviewee 4 confirmed the difficulty in changing existing attitudes and behaviours but recommended that a debate on decolonising should be fostered everywhere, “We need to talk about it more and exchange ideas and understand ourselves.” Similarly, Interviewee 8 envisioned conversations about decoloniality to be open and taken seriously by HEI. Interviewee 9 talked about creating safe spaces to challenge systems of oppression: “brave spaces/../where it’s about how can I safely challenge what’s around me. So how can I find the courage to call out the things that need to be called out?” Interviewee 2 called for free access to learning opportunities on decolonisation. Finally, participants praised some efforts on decolonisation in UK HEI, such as courses, students’ movements, collaborative projects, and inviting GS guest lecturers.

To decolonise research, we must rethink the hierarchy of knowledge production and dissemination and the politics of North-South research collaborations

When discussing decolonising research, we identified the following seven subthemes:

Research as a colonial construct

Participants shared their views on the colonial roots of current research practices and criticised the assumption that global health should focus on GS. They acknowledged the colonial history of disciplines like Anthropology. Two interviewees (5,7) shared their concerns as white researchers conducting research in GS and wanted to see GS researchers doing this kind of research. Interviewee 7 said, “There is something inherently problematic about me as a white researcher based in a UK-based university, to go to places like Uganda trying to understand people and practices in these contexts/../ I would wish that there were Ugandan researchers who, if they think these are important projects or important things to look at, would be the ones doing that kind of research.” Others viewed research as a collaborative endeavour. Interviewee 6 believed that research is “a global convention” and a collaborative process where researchers across the globe have contributed to and shaped over time through “engaging in a global discourse by refining them [research techniques].” Interviewee 11 said that “there are things that should be dismantled and things that should be left to be improved” and explained that indigenous approaches and “modern scientific tradition” should be “genuinely collaborative.”

The knowledge we value

Participants highlighted the lack of value research communities hold for indigenous knowledge and the need to address the knowledge hierarchy. Interviewee 1 highlighted the link between racism and sickness, explaining that while for many years people of colour indicated racism made them sick, it was not until researchers from GN confirmed this link, “We now kind of place more value on it/../we need to/../be open to reviewing what we consider to be the hierarchy in the gold standard and why and actually what is excluded in that process.” Interviewee 4 called for the “need to look at different methods and how we incorporate those African or indigenous methods in our research.” She criticised the Eurocentric review mechanism for not valuing non-Western methods and mentioned “storytelling” as an indigenous method that could be used in research. Interviewee 10 called to rethink what “valuable knowledge is” and criticised “the dominance of English in academia.” Interviewee 4 highlighted issues with journal reviewers regarding the marginalisation of non-English languages and the domination of the English language: “Why should it only be English, if a person writes in Arabic that can be translated in English and then put the other one you know in the bracket so that the person who understands that can read that in context and understand what the person was saying.” Interviewee 7 highlighted authorship problems and the need to support and fund GS researchers, mentioning that 80% of research articles are produced in GN.

New ways of dissemination

Participants acknowledged the limitations of current research dissemination practices and suggested new ways to communicate research to communities. Interviewee 2 suggested that “decolonised research should be outside journals because a journal does not feel like an accessible way to get knowledge.” He wanted to see research that is not only written in English and that “happens outside of the universities, in the context of community organisations /../ through a collaboration with other parts of the world.” Similarly, Interviewee 3 saw new ways of dissemination outside universities, like exhibitions, where researchers present “findings to the public, inviting local people” and showing the research benefits to society. Interviewee 4 discussed using community channels to disseminate research findings, such as “community radio stations,” drama, music, stories and arts. Interviewee 8 wanted participants to present research and tell their stories.

Reflexivity

Participants placed reflexivity at the core of decolonising research practices, promoting reflection on who is doing the research, their biases, motivations, and backgrounds. Interviewee 1 talked about identifying the “hegemony” when conducting research and the impact of who is doing the research, “what do we privilege, what do we study, who do we have as researchers and to actually understand the conscious and unconscious processes that occur in the research practice.” Interviewee 7 confirmed the need to “critically reflect on our own knowledge production and how we know what we know and allow ourselves to be challenged.” Interviewee 11 believed that “there is value in multiplicity” and that the inherited research practices are not necessarily perpetuating coloniality; the interviewee emphasised that when conducting empirical research, “the question to ask is what potentially could be colonial through this research and what should be avoided to become decolonised.”

Equitable South-North collaboration

Participants confirmed that equal South-North collaboration is key to decolonising research with emphasis on recognising that funders influence and limit models of collecting information. Interviewee 3 highlighted that Western funders would keep dominating the research agenda unless the GS governments have the financial independence to do research. “The finance one is a huge one/../ [it] has to change because a lot of these studies are funded by the United States. So, it’s very difficult. So, unless the governments themselves are going to be, I want to run my study, I want to do this by myself. But then you have to have the financial independence to do that.” Interviewee 6 spoke about the imbalance of power dynamics due to funding “often comes with strings attached” where GN researchers lead research, money is not handed directly to GS researchers, research reflects GN interests, and is not co-designed with GS researchers. Interviewee 6 continued, “If these power dynamics are not changed and /../dismantled, we cannot have any form of equitable research.” Interviewee 8 also discussed global power discourses regarding knowledge production and spoke about the importance of equal pay, equal relationships between GN and GS researchers, and the need to support GS researchers. Interviewee 10 believed big institutions in wealthy, predominantly white countries should support researchers in GS countries rather than doing research with them or on them. Interviewee 6 also talked about researchers’ exchange: “I feel increasingly uncomfortable that researchers from the GN think that they should be the ones exploring life in the GS. Although I would have to say I would find it quite interesting if more researchers from the GS would come to the GN and actually study us and hold a mirror up. That hasn’t happened to the same degree. So, there is a big knowledge gap.”

Partnership with the researched

Participants highlighted the importance of shifting control over data and partnering with the researched. Interviewee 3 confirmed the need to partner with people “by investigating critical issues that indigenous people identify and giving them more control over the data/../ who has access to the data and what they choose to do with it.” Similarly, Interviewee 4 called for involving people in research “so that people must understand the research is/../ about working together /../ to address their issue of concern, not the issue of the researcher.” Interviewee 5 spoke about the need to be “humble” and “critical of our research” and “what you owe to people when you do research.” Interviewee 6 also asserted that researchers need to engage with communities “to understand how it is that they generate knowledge /../ and the question is also who has the right to extract that knowledge and for what purpose.” For Interviewee 8, a decolonised research “speaks to local systems and processes. It is research that responds to realities, and I understand that makes it difficult because we are obsessed with coming up with standards and systems and processes/../But I think when you are doing research in spaces that are different in terms of culture, ethnicity and geography, everything. I think what should define your research is the expectations of that culture, that system or that process and how things are done in that space.”

New approach to ethics

Participants wanted to see less rigid, Western-informed ethical procedures and more reflexive, collaborative, ethical processes responsive to communities’ needs and norms. Interviewee 4 wanted to have community members on the ethics review boards. Interviewee 5 envisioned a less formal ethical process with constant reflexivity and collective conversation among researchers. Interviewee 8 called for rethinking the ethical research norms by reflecting on ethical dilemmas, such as compensating research participants for their time by paying them, as he believed that “taking them away from their income [is] unethical in itself.” Interviewee 5 highlighted the importance of engaging with the researched and exploring “whether they see any of those processes within their own frameworks.” Interviewee 6 discussed co-producing research knowledge and ownership: “Ownership of the data needs to be shared, moving away from the extraction model.” While acknowledging the need to avoid harm, she, for example, questioned the value of anonymising participants: “If you anonymise everything and your data sets and then aggregate and disaggregate, the individual’s story gets lost. So, it’s difficult for the person to know what part of that study they actually owned. So, there is something to be said about anonymity.”

Decolonising initiatives must question how Euro-centric knowledge is embedded in HEI and its hegemony over knowledge production compared to non-Western indigenous knowledge systems. This article presented our GHSM decolonising initiatives: (1) a gap analysis exercise of UG core modules, (2) a pilot course on decolonising research methods, and (3) interviews with academics and a focus group with students on decolonising HEI. Our initiatives contribute to the knowledge and efforts to decolonise HEI (Chaudhuri, 2016 ; Francis & Wright-Rigueur, 2021 ; Peters, 2018 ).

First, like Schucan Bird and Pitman’s ( 2020 ), our gap analysis exercise exposed our mainly white, biased curriculum towards GN scholars and research. Second, the pilot course expanded the horizons of our participants’ knowledge of decolonisation and indigenous research approaches. Finally, we identified the following themes by analysing our interviews and focus group: (1) Decolonisation is about challenging colonial legacies, racism, and knowledge production norms. (2) Decolonisation is about care, inclusivity, and compensation. (3) A decolonised curriculum should embrace anti-racisms agenda, reflexive pedagogies, and life experiences involving students and communities. (4) Higher education institutions are colonial, exclusionary constructs and should shift to transformative and collaborative ways of thinking and knowing. (5) To decolonise research, we must rethink the hierarchy of knowledge production and dissemination and the politics of North–South research collaborations.

Our research identified contextual and structural barriers to decolonise the curriculum: the colonial, exclusionary nature of the university’s construct, the colonial root of some disciplines; the apprehension and fear of, or resistance to change exhibited by some university staff; the lack of mechanisms to face power asymmetry and create equal collaboration opportunities between GN and GS institutions, the lack of academics’ diversity, and shortage of fund, time, space, and avenues to foster decolonisation in universities. These findings agree with other studies (Loyola-Hernández & Gosal, 2022 ; Shahjahan et al., 2022 ).

To decolonise the curriculum, our participants highlighted that an anti-racist agenda must be at the core of the HEI vision. This includes changing structures and policies to ensure diversity of staff; creating equal opportunities for people from different backgrounds; addressing the power imbalance at all levels, i.e., staff to staff, staff to students, students to students, and GN to GS; adopting reflexive pedagogies where colonial legacies are problematised and challenged; creating spaces for silenced voices to be heard and for indigenous and non-Western knowledge to be acknowledged and incorporated in learning; connecting learning to diverse communities and histories and ways of knowledge; and empowering students to lead the HEI decolonisation. Our findings reflect the themes proposed by Ahmed-Landeryou’s scoping review ( 2023 ) to guide decolonising curricula. Participants talked about explicitly diversifying reading lists. They criticised a university culture shaped by colonial practices. They pointed to missing voices, marginalisation of groups, domination of Western ideals and structures, and the grand erasure of non-Western experiences. Hence, reflecting the three curriculum levels (Le Grange, 2016 ) and Gaio et al.’s ( 2023 ) concepts in designing curriculum.

To decolonise research, our participants expressed a struggle with the inherited disciplinary practices rooted in colonial systems. They suggested steps to shift to a decolonised research through (1) rethinking the knowledge production and dissemination hierarchy, such as supporting publishing in non-Western journals in native languages; (2) changing the politics of North-South research collaborations by creating funded opportunities for GS researchers to conduct research in the GN; (3) partnership with people, so more participatory research and power shifting to the researched rather than the researchers and funding bodies; and (4) be reflexive of positionality and what Western ethical bodies consider ethical research practices versus what is relevant to different cultures and communities. These findings agree with the decolonising research literature (Bishop, 2005 ; Datta, 2018 ; Denzin et al., 2008a , b ; Keikelame & Swartz, 2019 ; Smith, 2021 ).

Our findings concur with scholars calling for a decolonised HEI to confront the domination of white academic knowledge and power asymmetry (Adébísí, 2023 ; Zeus Leonardo in Arday and Mirza, 2018 ), placing the process within a human rights framework (Kennedy et al., 2023 ; Osler, 2016 ). Our findings speak to the tenets of CRT (Hiraldo, 2010 ): our participants envision a decolonised HEI where (1) experiences of marginalised groups are foregrounded, (2) an anti-racist agenda is adopted across all levels, (3) exclusionary white practices are challenged, (4) legislations are changed to benefit all people from all backgrounds, and (5) spaces are created to challenge colonial legacies.

Moving forward, we endeavour to be guided by our findings and others in the field. Addressing the identified limitations of “intellectual decolonisation” (Moosavi, 2020 , p. 332), we plan to (1) incorporate decolonial theories and ways of knowing from the GS in our curricula. This is achieved through funding we gained from KCL to recruit a GHSM student to assist in creating a publicly accessible online archive. We have embarked on building the archive with GS collaborators and input from students and educators to help global health scholars in their decolonising efforts; (2) move away from a ticking box exercise to a process of continuous evaluation and reflection on curriculum, pedagogies, research practices, and positionality; this is achieved through an open dialogue with students and staff and one to one support to educators and researchers; (3) address marginalisation in academia through our efforts in the ARSG in collaboration with the Equality Diversity Inclusion Committee and other initiatives at KCL to foster spaces for conversation about race and inequity among staff and students, across all levels; and (4) actively seek funding to equally collaborate with GS researchers.

We agree with scholars that “Decolonization is not a metonym for social justice” (Tuck & Yang, 2021 , p. 21) and “certainly not a substitute for material reparations” (Gopal, 2021 , p. 880). We, however, believe that to understand race-based hierarchies embedded within HEI and to achieve social justice and racial equity in HEI, we must reveal the harm created by constructing white as a supreme race and document the “intellectual decolonisation” efforts to dismantle the power asymmetry and challenge the domination of Euro-centric thoughts. Hence, we document our efforts on decolonisation. We do not claim to be experts in decolonising HEI. However, by writing about our initiatives, we seek to build our knowledge and provide examples for others on the same journey. In the pursuit of decolonisation, it is essential that future curricula illuminate historical injustices where indigenous people are generators of knowledge and not just subjects for research and foster a more inclusive, ethically informed and just education. We acknowledge that systematic, structural colonial legacies are ingrained in our societies and that the journey to make changes is difficult. We also know our limitations in securing funding to continue our work and gain the institution’s full support. However, we believe collective efforts and determination to evolve would improve race inequities among future generations.

HEI must adopt strategies that are transformative, representative of, and responsive to the needs of their students’ cohorts and communities. Decolonising HEI must be placed within a human rights framework. A decolonised HEI would integrate anti-racism agendas; dismantle power asymmetries; challenge the European-centric academic knowledge domination; give prominence to indigenous and marginalised histories, theories, worldviews, and ways of knowing; foster multi-directional learning; create a non-hierarchical educational environment; and, most importantly, equip and allow students to lead the decolonisation process.

Data availability

The online archive that we mentioned in the conclusion has been designed and launched: https://globalhealtharchive2.wordpress.com/

We follow King’s College London guidelines which define ethnicity through the category of ‘BME’, encompassing six ethnic categories (White, Chinese, Black, Asian, other and mixed, unknown).

The modules that were analysed: Introduction to Social Medicine, Introduction to Global Health, Foundations in Social Science Theory, Research Practice and Design Studio. Key Concepts in Social Medicine, Key Concepts in Global Health, and Contemporary Crisis in Global Health and Social Medicine.

The countries represented at the course (in alphabetical order) were Argentina, Australia, Bangladesh, Belgium, Bermuda, Brazil, Canada, China, Colombia, Czech Republic, Ethiopia, Ghana, Greece, India, Indonesia, Lebanon, Malaysia, Mexico, Nepal, Netherlands, New Zealand, the occupied Palestinian territory, Philippines, Rwanda, South Africa, Spain, Tanzania, United Kingdom, United States, and Zimbabwe.

Abdelnour, S., & Abu Moghli, M. (2021). Researching violent contexts: A call for political reflexivity.  Organization , 13505084211030646. https://doi.org/10.1177/13505084211030

Adébísí, F. (2023). Theories of decolonisation; or, to break all the tables and create the world necessary for us all to survive.  Decolonisation and Legal Knowledge: Reflections on Power and Possibility , 14. https://doi.org/10.51952/9781529219401.ch001

Adefila, A., Teixeira, R. V., Morini, L., Garcia, M. L. T., Delboni, T. M. Z. G. F., Spolander, G., & Khalil-Babatunde, M. (2022). Higher education decolonisation: Whose voices and their geographical locations? Globalisation, Societies and Education, 20 (3), 262–276. https://doi.org/10.1080/14767724.2021.1887724

Article   Google Scholar  

Ahmed-Landeryou, M. (2023). Developing an evidence-Informed decolonising curriculum wheel—A reflective piece.  Equity in Education & Society , 27526461231154014. https://doi.org/10.1177/27526461231154014

Alsubaie, M. A. (2015). Hidden curriculum as one of current issue of curriculum. Journal of Education and Practice, 6 (33), 125–128.

Google Scholar  

Arday, J., & Mirza, H. S. (Eds.). (2018).  Dismantling race in higher education: Racism, whiteness and decolonising the academy . London: Palgrave Macmillan. https://doi.org/10.1007/978-3-319-60261-5

Bhambra, G. K., Nişancıoğlu, K., & Gebrial, D. (2020). Decolonising the university in 2020. Identities, 27 (4), 509–516. https://doi.org/10.1080/1070289X.2020.1753415

Bhambra, G. K., Gebrial, D., & Nişancıoğlu, K. (2018). Decolinising the university (p. 5). PlutoPress.

Bishop, R. (2005). Freeing ourselves from neo-colonial domination in research: A Kaupapa Māori approach to creating knowledge. In N. K. Denzin, ed. & Y. S. Lincoln (Eds.), The SAGE handbook of Qualitative Research (3rd ed., pp. 109–138). Thousand Oaks, CA: Sage.

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

Chakraborty, K., Saha, B., & Jammulamadaka, N. (2017). Where silence speaks-insights from Third World NGOs. Critical Perspectives on International Business, 13 (1), 38–53. https://doi.org/10.1108/cpoib-03-2015-0012

Chaudhuri, A. (2016). The real meaning of Rhodes must fall. The Guardian, 16 , 16.

Crenshaw, K., Gotanda, N., & Peller, G. (1995). Critical race theory: The key writings that formed the movement . The New Press.

Datta, R. (2018). Decolonizing both researcher and research and its effectiveness in Indigenous research. Research Ethics, 14 (2), 1–24. https://doi.org/10.1177/1747016117733296

Denzin, N. K., Lincoln, Y. S., & Smith, L. T. (Eds.). (2008a).  Handbook of critical and indigenous methodologies . Sage. https://doi.org/10.4135/9781483385686

Denzin, N. K., Lincoln, Y. S., & Smith, L. T. (2008b). Introduction: Critical methodologies and indigenous inquiry. Handbook of critical and indigenous methodologies , 1–20. https://doi.org/10.4135/9781483385686

Etherington, B. (2016). An answer to the question: What is decolonization? Frantz Fanon’s the wretched of the earth and Jean-Paul Sartre’s critique of dialectical reason. Modern Intellectual History, 13 (1), 151–178. https://doi.org/10.1017/S1479244314000523

Fanon, F. (1971). The Wretched of the Earth [1961], trans. Constance Farrington .

Francis, M. M., & Wright-Rigueur, L. (2021). Black Lives Matter in historical perspective. Annual Review of Law and Social Science, 17 , 441–458. https://doi.org/10.1146/annurev-lawsocsci-122120-100052

Gaio, A., Joffe, A., Hernández-Acosta, J. J., & Dragićević Šešić, M. (2023). Decolonising the cultural policy and management curriculum–reflections from practice.  Cultural Trends , 1–18. https://doi.org/10.1080/09548963.2023.2168515

Gopal, P. (2021). On decolonisation and the university. Textual Practice, 35 (6), 873–899. https://doi.org/10.1080/0950236X.2021.1929561

Hayes, A., Luckett, K., & Misiaszek, G. (2021). Possibilities and complexities of decolonising higher education: Critical perspectives on praxis. Teaching in Higher Education, 26 (7–8), 887–901. https://doi.org/10.1080/13562517.2021.1971384

Hiraldo, P. (2010). The role of critical race theory in higher education. The vermont connection , 31 (1), 7. https://scholarworks.uvm.edu/tvc/vol31/iss1/7 . Accessed 20 May 2022

Hlatshwayo, M. N. (2021). The ruptures in our rainbow: Reflections on teaching and learning during# RhodesMustFall. Critical Studies in Teaching and Learning (CriSTaL), 9 (2), 1–18. https://doi.org/10.14426/cristal.v9i2.492

Keikelame, M. J., & Swartz, L. (2019). Decolonising research methodologies: Lessons from a qualitative research project, cape town, South Africa. Global Health Action, 12 (1), 1561175. https://doi.org/10.1080/16549716.2018.1561175

Kelly, S., Jérémie-Brink, G., Chambers, A. L., & Smith-Bynum, M. A. (2020). The Black Lives Matter movement: A call to action for couple and family therapists. Family Process, 59 (4), 1374–1388. https://doi.org/10.1111/famp.12614

Kennedy, A., McGowan, K., & El-Hussein, M. (2023). Indigenous elders’ wisdom and dominionization in higher education: Barriers and facilitators to decolonisation and reconciliation. International Journal of Inclusive Education, 27 (1), 89–106.

Kingston University London. (2020). Equality, diversity and inclusion. Inclusive Curriculum Framework , UK. Kingston University London. Available at. https://www.kingston.ac.uk/aboutkingstonuniversity/equality-diversity-and-inclusion/our-inclusive-curriculum/inclusive-curriculum-framework/ . Accessed 10 Oct 2023.

Laakso, L., & Hallberg Adu, K. (2023). ‘The unofficial curriculum is where the real teaching takes place’: Faculty experiences of decolonising the curriculum in Africa.  Higher Education , 1–16. https://doi.org/10.1007/s10734-023-01000-4

Le Grange, L. (2016). Decolonising the university curriculum. South African Journal of Higher Education, 30 (2), 1–12. https://doi.org/10.20853/30-2-709

Loyola-Hernández, L., & Gosal, A. (2022)  Impact of decolonising initiatives and practices in the Faculty of Environment.  Report. University of Leeds. https://doi.org/10.48785/100/103

Mignolo, W. (2007). Introduction: Coloniality of power and de-colonial thinking. Cultural Studies, 21 (2/3), 155–167. https://doi.org/10.1080/09502380601162498

Mignolo, W. D. (2011). The Global South and world dis/order. Journal of Anthropological Research, 67 (2), 165–188.

Mignolo, W. (2018). What does it mean to decolonize? In C. E. Walsh, & W. D. Mignolo (Eds.), On decoloniality: Concepts, analytics, praxis (1st ed., pp. 105–134). Duke University Press.

Moosavi, L. (2020). The decolonial bandwagon and the dangers of intellectual decolonisation. International Review of Sociology, 30 (2), 332–354. https://doi.org/10.1080/03906701.2020.1776919

Motta, S. C. (2018). Feminizing and decolonizing higher education: Pedagogies of dignity in Colombia and Australia. In S. de Jong, R. Icaza, & O. U. Rutazibwa (Eds.), In  Decolonization and feminisms in global teaching and learning  (1st ed., pp. 25–42). Routledge.

Nanibush, W. (2018). Thinking and engaging with the decolonial: A conversation between Walter D. Mignolo and Wanda Nanibush. Afterall Journal, Retrieved April 2, 2023, from. https://www.afterall.org/article/thinking-and-engaging-with-the-decolonial-a-conversation-between-walterd-mignolo-and-wanda-nanibush . Accessed 2 Apr 2023

Osler, A. (2016). Human rights and schooling: An ethical framework for teaching for social justice . Teachers College Press.

Pete, S. (2018). Meschachakanis, a coyote narrative: Decolonising higher education. In G. K. Bhambra, D. Gebrial, & K. Nişancıoğlu (Eds.), Decolonising the university , (1st ed., pp. 173–189). Pluto Press.

Peters, M. A. (2018). Why is my curriculum white? A brief genealogy of resistance. In J. Arday, & H. S. Mirza (Eds.), Dismantling race in higher education, (1st ed., pp. 253–270.). Palgrave Macmillan.

Phoenix, A. (2020). ‘When black lives matter all lives will matter’ — A teacher and three students discuss the BLM movement. London Review of Education, 18 (3), 519–523. https://doi.org/10.14324/LRE.18.3.14

Quijano, A. (2007). Coloniality and modernity/rationality. Cultural Studies, 21 (2/3), 168–178. https://doi.org/10.1080/09502380601164353

Quijano, A. (2000). Power, eurocentrism, and Latin America.  Nepantla: Views from South ,  1 (3), 533–580. https://doi.org/10.1177/0268580900015002005

Schreiber, B., & Yu, D. (2016). Exploring student engagement practices at a South African university: Student engagement as reliable predictor of academic performance. South African Journal of Higher Education, 30 (5), 157–175. https://doi.org/10.20853/30-5-593

Schucan Bird, K., & Pitman, L. (2020). How diverse is your reading list? Exploring issues of representation and decolonisation in the UK. Higher Education, 79 (5), 903–920. https://doi.org/10.1007/s10734-019-00446-9

Shahjahan, R. A., Estera, A. L., Surla, K. L., & Edwards, K. T. (2022). “Decolonizing” curriculum and pedagogy: A comparative review across disciplines and global higher education contexts. Review of Educational Research, 92 (1), 73–113. https://doi.org/10.3102/00346543211042423

Smith, L. T. (2021).  Decolonizing methodologies: Research and indigenous peoples . Bloomsbury Publishing.

SOAS. (2018). Decolonising SOAS learning and teaching toolkit for programme and module convenors . London: SOAS. Available at:  https://blogs.soas.ac.uk/decolonisingsoas/files/2018/10/Decolonising-SOAS-Learning-and-Teaching-Toolkit-AB.pdf . Accessed 12 Oct 2023

Tuck, E., & Yang, K. W. (2021). Decolonization is not a metaphor. Tabula Rasa , (38), 61–111. https://doi.org/10.25058/20112742.n38.04

UCL. (2018).  Inclusive curriculum healthcheck . London, UK: UCL. Available at:  https://www.ucl.ac.uk/teaching-learning/sites/teaching_learning/files/ucl_inclusive_curriculum_healthcheck_2018.pdf . Accessed 12 Oct 2023

UN Sustainable Development Group. (2023). Human rights-based approach. universal values principle one: Human rights-based approach . Available at: https://unsdg.un.org/2030-agenda/universal-values/human-rights-based-approach#:~:text=HRBA%20requires%20human%20rights%20principles,holders'%20to%20claim%20their%20rights . Accessed 9 Oct 2023

University of Brighton. (2019). Decolonising the curriculum: Teaching and learning about race equality. Available at: https://research.brighton.ac.uk/en/publications/decolonising-the-curriculum-teaching-and-learning-about-race-equa . Accessed 9 Oct 2023

Von Bismarck, H. (2012). Defining decolonization. The British Scholar Society , 27 - 28. https://www.helenevonbismarck.com/wp-content/uploads/2017/12/Defining-Decolonization.pdf

Wawrzynski, M. R., & Garton, P. (2023). Language and the cocurriculum: The need for decolonizing out-of-classroom experiences.  Higher Education , 1–17. https://doi.org/10.1007/s10734-023-01016-w

Wong, S., Plowman, T., Nwibe, I., Hope, C., & Puri, D. (2020) decolonising-the-medical-curriculum-reading-list-2020. Retrieved May 3, 2023. From. https://decolonisingthemedicalcurriculum.wordpress.com/dtmc-reading-list/ . Accessed 20 May 2022

Zembylas, M. (2018). Affect, race, and white discomfort in schooling: Decolonial strategies for ‘pedagogies of discomfort.’ Ethics and Education, 13 (1), 86–104. https://doi.org/10.1080/17449642.2018.1428714

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Acknowledgements

The authors would like to thank the course participants for sharing their insights, experience, and valuable feedback on the course. We thank scholars who taught on the course; the research assistants who worked on the gap analysis, the pilot course, the symposium and the online Archive, all our interviewees, Prof. Hanna Kienzler for guiding our initiatives and her feedback on the article, and our Head of Department, prof. Anne Pollock, for supporting our initiatives.

Funding was received from King’s College London via

1. The Global Health and Social Medicine Department to conduct the gap analysis

2. The Faculty Education Fund 2022 for piloting a certificate course on ‘Decolonising Research Methods in Global Health and Social Medicine’.

3. Race Equity and Inclusive Education Fund (REIEF) Education and Students 2022 to conduct the gap analysis, the interviews, and focus group.

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Tamimi, N., Khalawi, H., Jallow, M.A. et al. Towards decolonising higher education: a case study from a UK university. High Educ (2023). https://doi.org/10.1007/s10734-023-01144-3

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  • How to Do Thematic Analysis | Guide & Examples

How to Do Thematic Analysis | Guide & Examples

Published on 5 May 2022 by Jack Caulfield .

Thematic analysis is a method of analysing qualitative data . It is usually applied to a set of texts, such as an interview or transcripts . The researcher closely examines the data to identify common themes, topics, ideas and patterns of meaning that come up repeatedly.

There are various approaches to conducting thematic analysis, but the most common form follows a six-step process:

  • Familiarisation
  • Generating themes
  • Reviewing themes
  • Defining and naming themes

This process was originally developed for psychology research by Virginia Braun and Victoria Clarke . However, thematic analysis is a flexible method that can be adapted to many different kinds of research.

Table of contents

When to use thematic analysis, different approaches to thematic analysis, step 1: familiarisation, step 2: coding, step 3: generating themes, step 4: reviewing themes, step 5: defining and naming themes, step 6: writing up.

Thematic analysis is a good approach to research where you’re trying to find out something about people’s views, opinions, knowledge, experiences, or values from a set of qualitative data – for example, interview transcripts , social media profiles, or survey responses .

Some types of research questions you might use thematic analysis to answer:

  • How do patients perceive doctors in a hospital setting?
  • What are young women’s experiences on dating sites?
  • What are non-experts’ ideas and opinions about climate change?
  • How is gender constructed in secondary school history teaching?

To answer any of these questions, you would collect data from a group of relevant participants and then analyse it. Thematic analysis allows you a lot of flexibility in interpreting the data, and allows you to approach large datasets more easily by sorting them into broad themes.

However, it also involves the risk of missing nuances in the data. Thematic analysis is often quite subjective and relies on the researcher’s judgement, so you have to reflect carefully on your own choices and interpretations.

Pay close attention to the data to ensure that you’re not picking up on things that are not there – or obscuring things that are.

Prevent plagiarism, run a free check.

Once you’ve decided to use thematic analysis, there are different approaches to consider.

There’s the distinction between inductive and deductive approaches:

  • An inductive approach involves allowing the data to determine your themes.
  • A deductive approach involves coming to the data with some preconceived themes you expect to find reflected there, based on theory or existing knowledge.

There’s also the distinction between a semantic and a latent approach:

  • A semantic approach involves analysing the explicit content of the data.
  • A latent approach involves reading into the subtext and assumptions underlying the data.

After you’ve decided thematic analysis is the right method for analysing your data, and you’ve thought about the approach you’re going to take, you can follow the six steps developed by Braun and Clarke .

The first step is to get to know our data. It’s important to get a thorough overview of all the data we collected before we start analysing individual items.

This might involve transcribing audio , reading through the text and taking initial notes, and generally looking through the data to get familiar with it.

Next up, we need to code the data. Coding means highlighting sections of our text – usually phrases or sentences – and coming up with shorthand labels or ‘codes’ to describe their content.

Let’s take a short example text. Say we’re researching perceptions of climate change among conservative voters aged 50 and up, and we have collected data through a series of interviews. An extract from one interview looks like this:

In this extract, we’ve highlighted various phrases in different colours corresponding to different codes. Each code describes the idea or feeling expressed in that part of the text.

At this stage, we want to be thorough: we go through the transcript of every interview and highlight everything that jumps out as relevant or potentially interesting. As well as highlighting all the phrases and sentences that match these codes, we can keep adding new codes as we go through the text.

After we’ve been through the text, we collate together all the data into groups identified by code. These codes allow us to gain a condensed overview of the main points and common meanings that recur throughout the data.

Next, we look over the codes we’ve created, identify patterns among them, and start coming up with themes.

Themes are generally broader than codes. Most of the time, you’ll combine several codes into a single theme. In our example, we might start combining codes into themes like this:

At this stage, we might decide that some of our codes are too vague or not relevant enough (for example, because they don’t appear very often in the data), so they can be discarded.

Other codes might become themes in their own right. In our example, we decided that the code ‘uncertainty’ made sense as a theme, with some other codes incorporated into it.

Again, what we decide will vary according to what we’re trying to find out. We want to create potential themes that tell us something helpful about the data for our purposes.

Now we have to make sure that our themes are useful and accurate representations of the data. Here, we return to the dataset and compare our themes against it. Are we missing anything? Are these themes really present in the data? What can we change to make our themes work better?

If we encounter problems with our themes, we might split them up, combine them, discard them, or create new ones: whatever makes them more useful and accurate.

For example, we might decide upon looking through the data that ‘changing terminology’ fits better under the ‘uncertainty’ theme than under ‘distrust of experts’, since the data labelled with this code involves confusion, not necessarily distrust.

Now that you have a final list of themes, it’s time to name and define each of them.

Defining themes involves formulating exactly what we mean by each theme and figuring out how it helps us understand the data.

Naming themes involves coming up with a succinct and easily understandable name for each theme.

For example, we might look at ‘distrust of experts’ and determine exactly who we mean by ‘experts’ in this theme. We might decide that a better name for the theme is ‘distrust of authority’ or ‘conspiracy thinking’.

Finally, we’ll write up our analysis of the data. Like all academic texts, writing up a thematic analysis requires an introduction to establish our research question, aims, and approach.

We should also include a methodology section, describing how we collected the data (e.g., through semi-structured interviews or open-ended survey questions ) and explaining how we conducted the thematic analysis itself.

The results or findings section usually addresses each theme in turn. We describe how often the themes come up and what they mean, including examples from the data as evidence. Finally, our conclusion explains the main takeaways and shows how the analysis has answered our research question.

In our example, we might argue that conspiracy thinking about climate change is widespread among older conservative voters, point out the uncertainty with which many voters view the issue, and discuss the role of misinformation in respondents’ perceptions.

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COMMENTS

  1. A Step-by-Step Process of Thematic Analysis to Develop a Conceptual

    Thematic analysis is a research method used to identify and interpret patterns or themes in a data set; it often leads to new insights and understanding ( Boyatzis, 1998; Elliott, 2018; Thomas, 2006 ).

  2. How to Do Thematic Analysis

    Thematic analysis is a method of analyzing qualitative data. It is usually applied to a set of texts, such as an interview or transcripts. The researcher closely examines the data to identify common themes - topics, ideas and patterns of meaning that come up repeatedly.

  3. Thematic Analysis: Striving to Meet the Trustworthiness Criteria

    Thematic analysis is an apt qualitative method that can be used when working in research teams and analyzing large qualitative data sets. Our step-by-step approach provides a detailed description and pragmatic approach to conduct a thematic analysis.

  4. Thematic analysis: A practical guide

    Thematic analysis. In Liamputtong P. (Ed.), Handbook of research methods in health social sciences (pp. 843-860). Springer. https://doi.org/10.1007/978-981-10-5251-4_103 Google Scholar Kidder L. H., Fine M. (1987). Qualitative and quantitative methods: When stories converge.

  5. Chapter 22: Thematic Analysis

    Thematic analysis is a common method used in the analysis of qualitative data to identify, analyse and interpret meaning through a systematic process of generating codes (see Chapter 20) that leads to the development of themes. 1 Thematic analysis requires the active engagement of the researcher with the data, in a process of sorting, categorisi...

  6. Essentials of Thematic Analysis

    She also has a second strand of research exploring perimenopause and menopause. Dr. Hayfield uses qualitative methods of data collection and analysis, in particular thematic analysis, and she has written about qualitative research methods, including thematic analysis, insider/outsider research, and story completion tasks.

  7. Thematic analysis

    Thematic analysis is often understood as a method or technique in contrast to most other qualitative analytic approaches - such as grounded theory, discourse analysis, narrative analysis and interpretative phenomenological analysis - which can be described as methodologies or theoretically informed frameworks for research (they specify guiding t...

  8. Thematic Analysis

    1 Introduction Thematic analysis (TA) is often misconceptualized as a single qualitative analytic approach. It is better understood as an umbrella term, designating sometimes quite different approaches aimed at identifying patterns ("themes") across qualitative datasets.

  9. Thematic Analysis

    Thematic analysis (TA) is a set of methods for developing and interpreting patterns of meaning across qualitative data. Description In the social and health sciences, TA has been extensively used for analyzing qualitative data, but until the last decade or so, there had been little discussion of TA as a method or guidance provided for its use ...

  10. Thematic Analysis

    Thematic analysis is a method of qualitative data analysis that was first described in the 1970s (Joffe, 2012) but became more prominent at the end of the 1990s with researchers such as Boyatzis ( 1998) and Hayes ( 1997) As qualitative research approaches become more accepted across social science disciplines and now across health professions ed...

  11. Thematic analysis: A practical guide

    Book Review: Deborah K. Padgett, Qualitative Methods in Social Work Research, 2nd edn. Thousand Oaks, CA: SAGE, 2008. 281 pp. ISBN 978 1412951920 (hbk); 978141295937 (pbk) Show details Hide details

  12. Sage Research Methods

    The authors introduce and outline applied thematic analysis, an inductive approach that draws on established and innovative theme-based techniques suited to the applied research context. Chapters follow the sequence of activities in the analysis process and also include discussions of mixed methods, choosing the most appropriate software, and ...

  13. Thematic Analysis

    Braun and Clarke's thematic analysis method is an iterative process consisting of six steps: (1) becoming familiar with the data, (2) generating codes, (3) generating themes, (4) reviewing themes, (5) defining and naming themes, and (6) locating exemplars.

  14. 18.4 Thematic analysis

    18.4 Thematic analysis - Doctoral Research Methods in Social Work 18.4 Thematic analysis Learning Objectives Learners will be able to… Explain defining features of thematic analysis as a strategy for qualitative data analysis and identify when it is most effectively used

  15. Thematic Analysis in Qualitative Research

    The popularity of qualitative methods in social science research is a well-noted and most welcomed fact. Thematic analysis, the often-used methods of qualitative research, provides concise description and interpretation in terms of themes and patterns from a data set.

  16. PDF Virginia Braun, Victoria Clarke, Nikki Hayfield, and Gareth Terry

    thematic analysis · Semantic · Thematic map · Theme 1 Introduction Thematic analysis (TA) is often misconceptualized as a single qualitative analytic approach. It is better understood as an umbrella term, designating sometimes quite different approaches aimed at identifying patterns ("themes") across qualitative datasets.

  17. Thematic Analysis in Social Work: A Case Study

    Thematic analysis offers a flexible, yet rigorous approach to subjective experience that is highly applicable to research in social work as a means of promoting social justice and...

  18. Practical thematic analysis: a guide for multidisciplinary health

    In particular, researchers acknowledge that thematic analysis is a flexible and powerful method of systematically generating robust qualitative research findings by identifying, analysing, and reporting patterns (themes) within data. 3 4 5 6 Although qualitative methods are increasingly valued for answering clinical research questions, many rese...

  19. Thematic Analysis in Social Work: A Case Study

    The particular importance of qualitative research methods, such as thematic analysis, for social work is that these approaches can also serve to promote social justice and combat inequalities. Qualitative methods allow researchers to transmit people's ideas, perceptions, and opinions by analyzing and disseminating participant discourses.

  20. Thematic Analysis in Qualitative Research

    Thematic Analysis in Qualitative Research Authors: Anindita Majumdar Request full-text Abstract The popularity of qualitative methods in social science research is a well-noted and most...

  21. Increasing rigor and reducing bias in qualitative research: A document

    Examples of such frameworks include the hybrid approach to thematic analysis used by Fereday and Muir-Cochrane (2006), which incorporates both an inductive data-driven approach and a deductive approach using a template of codes developed a priori from the theoretical framework addressed by the research question; the range of analytic procedures ...

  22. Sage Research Methods Cases Part 1

    Abstract. This case study describes the stages involved in designing a research project including the rationale for the type of data to collect (qualitative), the approach used to gather data (semi-structured interviews), and the method utilized to analyze the data (thematic analysis).

  23. Towards decolonising higher education: a case study from a UK

    During the academic year 2021, GHSM executed (1) a gap analysis of undergraduate modules, (2) a course on decolonising research methods taught by global scholars to 40 Global South and North university students who completed pre- and post-course surveys, and (3) semi-structured interviews with 11 academics, and a focus group with four students ...

  24. Social Media Posts About Distal Radius Fracture: A Cross-Sectional

    Recent research has demonstrated a clear connection between patient-reported satisfaction and improved postinterventional outcomes following treatment for DRF. 3,4 One study found a significant positive association between patient satisfaction and 30-day readmission rates and postinterventional complications. 5 These studies rely heavily on standardized patient reported outcomes (PROs), and ...

  25. How to Do Thematic Analysis

    Thematic analysis is a good approach to research where you're trying to find out something about people's views, opinions, knowledge, experiences, or values from a set of qualitative data - for example, interview transcripts, social media profiles, or survey responses. Some types of research questions you might use thematic analysis to answer: