thematic analysis in social work research

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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.

Doctoral Research Methods in Social Work Copyright © by Mavs Open Press. All Rights Reserved.

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General-purpose thematic analysis: a useful qualitative method for anaesthesia research

1 Centre for Medical and Health Sciences Education, School of Medicine, University of Auckland, Auckland, New Zealand

2 Department of Anaesthesia, Auckland City Hospital, Auckland, New Zealand

Learning objectives

By reading this article, you should be able to:

• • Explain when to use thematic analysis.
• • Describe the steps in thematic analysis of interview data.
• • Critique the quality of a study that uses the method of thematic analysis.
• • Thematic analysis is a popular method for systematically analysing qualitative data, such as interview and focus group transcripts.
• • It is one of a cluster of methods that focus on identifying patterns of meaning, or themes, across a data set.
• • It is relevant to many questions in perioperative medicine and a good starting point for those new to qualitative research.
• • Systematic approaches to thematically analysing data exist, with key components to demonstrate rigour, accountability, confirmability and reliability.
• • In one study, a useful six-step approach to analysing data is offered.

Anaesthesia research commonly uses quantitative methods, such as surveys, RCTs or observational studies. Such methods are often concerned with answering what questions and how many questions. Qualitative research is more concerned with why questions that enable us to understand social complexities. ‘Qualitative studies in the anaesthetic setting’, write Shelton and colleagues, ‘have been used to define excellence in anaesthesia, explore the reasons behind drug errors, investigate the acquisition of expertise and examine incentives for hand hygiene in the operating theatre’. 1

General-purpose thematic analysis (termed thematic analysis hereafter) is a qualitative research method commonly used with interview and focus group data to understand people's experiences, ideas and perceptions about a given topic. Thematic analysis is a good starting point for those new to qualitative research and is relevant to many questions in the perioperative context. It can be used to understand the experiences of healthcare professionals and patients and their families. Box 1 gives examples of questions amenable to thematic analysis in anaesthesia research.

Examples of questions amenable to thematic analysis.

• (i) How do operating theatre staff feel about speaking up with their concerns?
• (ii) What are trainee's conceptions of the balance between service and learning?
• (iii) What are patients' experiences of preoperative neurocognitive screening?

Alt-text: Box 1

Thematic analysis involves a process of assigning data to a number of codes, grouping codes into themes and then identifying patterns and interconnections between these themes. 2 Thematic analysis allows for a nuanced understanding of what people say and do within their particular social contexts. Of note, thematic analysis can be used with interviews and focus groups and other sources of data, such as documents or images.

Thematic analysis is not the same as content analysis. Content analysis involves counting the frequency with which words or phrases appear in data. Content analysis is a method used to code and categorise textual information systematically to determine trends, frequency and patterns of words used. 3 Conversely, thematic analysis focuses on the relative importance of ideas and how ideas connect and govern practices. Thematic analysis does not rely on frequency counts to indicate the importance of coded data. Content analysis can be coupled with thematic analysis, where both themes and frequencies of particular statements or words are reported.

Thematic analysis is a research method, not a methodology. A methodology is a method with a philosophical underpinning. If researchers report only on what they did, this is the method. If, in addition, they report on the philosophy that governed what they did, this is methodology. Common methodologies in qualitative research include phenomenology, grounded theory, hermeneutics, narrative enquiry and ethnography. 4 Each of these methodologies has associated methods for data analysis. Thematic analysis can be combined with many different qualitative methodologies.

There are also different types of thematic analysis, such as inductive (including general purpose), applied, deductive or semantic thematic analysis. Inductive analysis involves approaching the data with an open mind, inductively looking for patterns and themes and interpreting these for meaning. 2 , 4 Of note, researchers can never have a truly open mind on their topic of interest, so the process will be influenced by their particular perspectives, which need to be declared. In applied and deductive thematic analysis, the researcher will have a pre-existing framework (which may be informed by theory or philosophy) against which they will attempt to categorise the data. 4 , 5 , 6 For semantic thematic analysis, the data are coded on explicit content, and tend to be descriptive rather than interpretative. 6

In this review, we outline what thematic analysis entails and when to use it. We also list some markers to look for to appraise the quality of a published study.

Designing the data collection

Before embarking on qualitative research, as with quantitative research, it is important to seek ethical review of the proposed study. Ethical considerations include such issues as consent, data security and confidentiality, permission to use quotes, potential for identifying individuals or institutions, risk of psychological harm to participants with studies on sensitive issues (e.g. suicide or sexual harassment), power relationships between interviewer and interviewee or intrusion on other activities (such as teaching time or work commitments). 7

Qualitative research often involves asking people questions during interviews or focus groups. Merriam and Tisdell stated that, ‘The most common form of interview is the person-to-person encounter in which one person elicits information from the other’. 8 Information is elicited through careful and purposeful questioning and listening. 9 Research interviews in anaesthesia are generally purposeful conversations with a structure that allows the researcher to gather information about a participant's ideas, perceptions and experiences concerning a given topic.

A structured interview is when the researcher has already decided on a set of questions to ask. 9 If the researcher will ask a set of questions, but has flexibility to follow up responses with further questions, this is called a semi-structured interview. Semi-structured interviews are commonly used in research involving thematic analysis. The researcher can also use other forms of questioning, such as single-question interview. Semi-structured interviews are commonly used in anaesthesia, such as the studies from our own research group. 10 , 11 , 12

Interviews are usually recorded in audio form and then transcribed. For each interview or focus group, a single transcript is created. The transcripts become the written form of data and the collection of transcripts from the research participants becomes the data set.

Designing productive interview questions

The design of interview questions significantly shapes a participant's response. Interview questions should be designed using ‘sensitising concepts’ to encourage participants to share information that will increase a researcher's understanding of the participants' experiences, views, beliefs and behaviours. 13 ‘Sensitising concepts’ describe words in questions that bring the participants' attention to a concept of research interest. Examples of sensitising concepts include speaking up, teamwork and theoretical concepts (such as Kolb's experiential learning cycle or Foucauldian power theory in relation to trainee learning and operating theatre culture). 14 , 15 Specifically, the questions should be framed in such a way as to encourage participants to make sense of their own experience and in their own words. The researcher should try to minimise the influences of their own biases when they design questions. Using open-ended questions will increase the richness of data. Box 2 gives examples of question design.

How to design an interview question.

Alt-text: Box 2

Bias, positionality and reflexivity

Bias is an inclination or prejudice for or against someone or something, whereas positionality is a person's position in society or their stance towards someone or something. For example, Tanisha once had an inexperienced anaesthetist accidentally rupture one of her veins whilst they were siting an i.v. cannula in an emergency situation. Now, Tanisha has a bias against inexperienced anaesthetists. Tanisha's positionality —a medical anthropologist with no anaesthesia training, but working with many anaesthesia colleagues, including her director—may also inform that bias or the way that Tanisha interacts with anaesthetists. Reflexivity is a process whereby people/researchers proactively reflect on their biases and positionality. Biases shape positionality (i.e. the stance of the researcher in relation to the social, historical and political contexts of the study). In practical research terms, biases and positionality inform the way researchers design and undertake research, and the way they interpret data. It is important in qualitative research to both identify biases and positionality, and to take steps to minimise the impact of these on the research.

Some ways to minimise the influence of bias and positionality on findings include:

(i) Raise awareness amongst the research team of bias and positionality.

(ii) Design research/interview questions that minimise potential for these to distort which data are collected or how they are collected.

(iii) Researchers ask reflexive questions during data analysis, such as, ‘Is my bias about xxx informing my view of these data?’

(iv) Two or more researchers are involved in the analysis process.

(v) Data analysis member check (e.g. checking back with participants if the interpretation of their data is consistent with their experience and with what they said).

Before embarking on the study, researchers should consider their own experiences, knowledge and views; how this influences their own position in relation to the study question; and how this position could potentially introduce bias in how they collect and analyse the data. Taking time to reflect on the impact of the researchers' position is an important step towards being reflective and transparent throughout the research process. When writing up the study, researchers should include statements on bias and positionality. In quantitative research, we aim to eliminate bias. In qualitative research, we acknowledge that bias is inevitable (and sometimes even unconscious), and we take steps to make it explicit and to minimise its effect on study design and data interpretation.

Sampling and saturation

Qualitative research typically uses systematic, non-probability sampling. Unlike quantitative research, the goal of sampling is not to randomly select a representative sample from a population. Instead, researchers identify and select individuals or groups relevant to the research question. Commonly used sampling techniques in anaesthesia qualitative research are homogeneous (group) sampling and maximum variation sampling. In the former, researchers may be concerned with the experiences of participants from a distinct group or who share a certain characteristic (e.g. female anaesthesia trainees), so they recruit selectively from within the group with this shared characteristic to gain a rich, in-depth understanding of their experiences. Conversely, the aim with maximum variation sampling is to recruit participants with diverse characteristics to obtain a broad understanding of the question being studied (e.g. members of different professional groups within operating theatre teams, who have diverse ages, gender and ethnicities).

As with quantitative research, the purpose of sampling is to recruit sufficient numbers of participants to enable identification of patterns or richness in what they say or do to understand or explain the phenomenon of interest, and where collecting more data is unlikely to change this understanding.

In qualitative research, data collection and analysis often occur concurrently. This is because data collection is an iterative process both in recruitment and in questioning. The researchers may identify that more data are needed from a particular demographic group or on a particular theme to reach data saturation, so the next participants may be selected from a particular demographic, or be asked slightly different questions or probes to draw out that theme. Sample size is considered adequate when little or no new information emerges from interviews or focus groups; this is generally termed ‘data saturation’, although some qualitative researchers use the term ‘data sufficiency’. This could also be explained in terms of data reliability (i.e. the researcher is satisfied that collecting more data will not substantially change the results). Data saturation typically occurs with between 12 and 17 participants in a relatively homogeneous sampling, but larger numbers may be required, where the interviewees are from distinct groups or cultures. 16 , 17

Data management

For data sets that involve 10 or more transcripts or lengthy interviews (e.g. 90 min or more), researchers often use software to help them collate and manage the data. The most commonly used qualitative software packages are QSR NVivo, Atlas and Dedoose. 18 , 19 , 20 Many researchers use Microsoft Excel instead, or for small data sets the analysis can be done by hand, with pen, paper and scissors (i.e. researchers cut up printed transcripts and reorder the information according to code and theme). 21 NVivo and Atlas are simply repositories, in which you can input the transcripts and, using your coding scheme, sort the text into codes. They facilitate the task of analysis, rather than doing the analysis for you. Some advantages over coding by hand are that text can be allocated to more than one code, and you can easily identify the source of the segment of text you have coded.

Data analysis

Qualitative data analysis is ‘the classification and interpretation of linguistic (or visual) material to make statements about implicit and explicit dimensions and structures of meaning-making in the material and what is represented in it’. 22

Several social scientists have described this analytical process in depth. 2 , 6 , 22 , 23 , 24 , 25 For inductive studies, we recommend researchers follow Braun and Clarke's practical six-phase approach to thematic analysis. 26 The phases are (i) familiarising the researcher with the data, (ii) generating initial codes, (iii) searching for themes, (iv) reviewing themes, (v) defining and naming themes and (vi) producing the report. These six phases are described next.

Phase 1: familiarising the researcher with the data

In this step, the researchers read the transcripts to become familiar with them and take notes on potential recurring ideas or potential themes. They share and discuss their ideas and, in conjunction with any sensitising concepts, they start thinking about possible codes or themes.

Phase 2: generating initial codes

The first step in Phase 2 is ‘assigning some sort of short-hand designation to various aspects of your data so that you can easily retrieve specific pieces of the data’. 2 The designation might be a word or a short phrase that summarises or captures the essence of a particular piece of text. Coding makes it easier to summarise and compare, which is important because qualitative research is primarily about synthesis and comparison of data. 2 , 25 As the researcher reads through the data, they assign codes. If they are coding a transcript, they might highlight some words, for example, and attach to them a single word that summarises their meaning.

Researchers undertaking thematic analysis should iteratively develop a ‘coding scheme’, which is essentially a list of the codes they create as they read the data, and definitions for each code. 25 , 26 Code definitions are important, as they help the researcher make decisions on whether to assign this code or another one to a segment of data. In Table 1 , we have provided an example of text data in Column 1. TJ analysed these data. To do so, she asked, ‘What are these data about? How does it answer the research question? What is the essence of this statement?’ She underlined keywords and created codes and definitions (Columns 2 and 3). Then, TJ searched the remaining data to see if any more data met each code definition, and if so, coded that (see Table 1 ). As demonstrated in Table 1 , data can be coded to multiple codes.

Table 1

How to code qualitative data: an example

In thematic analysis of interview data, we recommend that code definitions begin with something objective, such as ‘participant describes’. This keeps the researcher's focus on what participants said rather than what the researcher thought or said.

There is no set rule for how many codes to create. 25 However, in our experience, effective manageable coding schemes tend to have between 15 and 50 codes. The coding scheme is iterative. This means that the coding scheme is developed over time, with new codes being created as more data are coded. For example, after a close reading of the first transcript, the researcher might create, say, 10 codes that convey the key points. Then, the researcher reads and codes the next transcript and may, for instance, create additional four codes. As additional transcripts are read and coded, more codes may be created. Not all codes are relevant to all transcripts. The researcher will notice patterns as they code more transcripts. Some codes may be too broad and will need to be refined into two or three smaller codes (and vice versa ). Once the coding scheme is deemed complete and all transcripts have been coded, the researcher should go back to the beginning and recode the first few transcripts to ensure coding rigour.

The second step in Phase 2, once the coding is complete, is to collate all the data relevant to each of these codes.

Phase 3: searching for themes

In this phase, the researchers look across the codes to identify connections between them, with the intention of collating the codes into possible themes. Once these possible themes have been identified, all the data relevant to each possible theme are pulled together under that theme.

Phase 4: reviewing the themes

After the initial collation of the data into themes, the researchers undertake a rigorous process of checking the integrity of these themes, through reading and re-reading their data. This process includes checking to see if the themes ‘fit’ in relation to the coded excerpts (i.e. Do all the data collected under that theme fit within that theme?). Next is checking if the themes fit in relation to the whole data set (i.e. Do the themes adequately reflect the data?) This step may result in the search for additional themes. As a final step in this phase, the researchers create a thematic ‘map’ of the analysis.

When viewed together, the themes should answer the research question and should summarise participant experiences, views or behaviours.

Phase 5: naming the themes

Once researchers have checked the themes and included any additional emerging themes they name the final set of themes identified. Each theme and any subthemes should be listed in turn.

Phase 6: producing the report

The report should summarise the themes and illustrate them by choosing vivid or persuasive extracts from the data. For data arising from interviews, extracts will be quotes from participants. In some studies, researchers also report strong associations between themes, or divide a theme into sub-themes.

Tight word limits on many academic journals can make it difficult to include multiple quotes in the text. 27 One way around a word limit is to provide quotes in a table or a supplementary file, although quotes within the text tend to make for more interesting and compelling reading.

Who should analyse the data?

Ideally, each researcher in the team should be involved in the data analysis. Contrasting researcher viewpoints on the same study subject enhance data quality and validity, and minimise research bias. Independent analysis is time and resource intensive. In clinical research, close independent analysis by each member of the research team may be impractical, and one or two members may undertake the analysis while the rest of the research team read sections of data (e.g. reading two or three transcripts rather than closely analysing the whole data set), thus contributing to Phase 1 and Phase 2 of Braun and Clarke's method. 2

The research team should regularly meet to discuss the analytical process, as described earlier, to workshop and reach agreement on the coding and emergent themes (Phase 4 and Phase 5). The research team members compare their perspectives on the data, analyse divergences and coincidences and reach agreement on codes and emerging themes. Contrasting researcher viewpoints on the same study subject enhance data quality and validity, and minimise research bias.

Judging the quality and rigour of published studies involving thematic analysis

There are a number of indicators of quality when reading and appraising studies. 28 , 29 , 30 , 31 In essence, the authors should clearly state their method of analysis (e.g. thematic analysis) and should reference the literature relevant to their qualitative method, for example Braun and Clarke. 2 This is to indicate that they are following established steps in thematic analysis. The authors should include in the methods a description of the research team, their biases and experience and the efforts made to ensure analytical rigour. Verbatim quotes should be included in the findings to provide evidence to support the themes.

A number of guides have been published to assist readers, researchers and reviewers to evaluate the quality of a qualitative study. 30 , 31 The Joanna Briggs Institute guide to critical appraisal of qualitative studies is a good start. 30 This guide includes a set of 10 criteria, which can be used to rate the study. The criteria are summarised in Box 3 . Within these criteria lie rigorous methodological approaches to how data are collected, analysed and interpreted.

Ten quality appraisal criteria for qualitative literature.31

• (i) Alignment between the stated philosophical perspective and the research methodology
• (ii) Alignment between the research methodology and the research question or objectives
• (iii) Alignment between the research methodology and the methods used to collect data
• (iv) Alignment between the research methodology and the representation and analysis of data
• (v) Alignment between the research methodology and the interpretation of results
• (vi) A statement locating the researcher culturally or theoretically (positionality and bias)
• (vii) The influence of the researcher on the research, and vice versa
• (viii) Adequate representation of participants and their voices
• (ix) Ethical research conduct and evidence of ethical approval by an appropriate body
• (x) Conclusions flow from the analysis, or interpretation, of the data

Alt-text: Box 3

Another approach to quality appraisal comes from Lincoln and Guba, who have published widely on the topic of judging qualitative quality. 28 They look for quality in terms of credibility, transferability, dependability, confirmability and authenticity. There are many qualitative checklists readily accessible online, such as the Standards for Reporting Qualitative Research checklist or the Consolidated Criteria for Reporting Qualitative Research checklist, which researchers can include in their work to demonstrate quality in these areas.

Conclusions

As with quantitative research, qualitative research has requirements for rigour and trustworthiness. Thematic analysis is an accessible qualitative method that can offer researchers insight into the shared experiences, views and behaviours of research participants.

Declaration of interests

The authors declare that they have no conflicts of interest.

The associated MCQs (to support CME/CPD activity) will be accessible at www.bjaed.org/cme/home by subscribers to BJA Education .

Biographies

Tanisha Jowsey PhD BA (Hons) MA PhD is a senior lecturer in the Centre for Medical and Health Sciences Education, School of Medicine, University of Auckland. She has a background in medical anthropology and has expertise as a qualitative researcher.

Carolyn Deng MPH FANZCA is a specialist anaesthetist at Auckland City Hospital. She has a Master of Public Health degree. She is embarking on qualitative research in perioperative medicine and hopes to use it as a tool to complement quantitative research findings in the future.

Jennifer Weller MD MClinEd FANZCA FRCA is head of the Centre for Medical and Health Sciences Education at the University of Auckland. Professor Weller is a specialist anaesthetist at Auckland City Hospital and often uses qualitative methods in her research in clinical education, teamwork and patients' safety.

Matrix codes: 1A01, 2A01, 3A01

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

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.

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

• Related content
• 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

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

• View inline

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

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.

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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: Six interactive phases.

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.

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Thematic analysis is a method of qualitative data analysis that was first described in the 1970s (Joffe, Harper and Thompson (eds), Qualitative Research Methods in Mental Health and Psychotherapy: A Guide for Students and Practitioners, Wiley-Blackwell, 2012) but became more prominent at the end of the 1990s with researchers such as Boyatzis ( 1998 ) and Hayes ( 1997 ) (as cited in Braun, V., & Clarke, V. (2012). Thematic analysis. In H. Cooper (Ed.), APA Handbook of Research Methods in Psychology (vol.2) (pp. 57–71). American Psychological Association.). As qualitfvecome more accepted across social science disciplines and now across health professions education, the need for systematic methods to analyze qualitative sets is more accentuated (Castleberry and Nolen, Currents in Pharmacy Teaching and Learning 10:807–815, 2018). ( Braun, V., & Clarke, V. (2012). Thematic analysis. In H. Cooper (Ed.), APA Handbook of Research Methods in Psychology (vol.2) (pp. 57–71). American Psychological Association.) highlighted that thematic analysis is “an accessible, flexible, and increasingly popular method of qualitative data analysis” (p. 57). Although thematic analysis shares similarities with other methodologies that have systematic processes for analyzing data such as Interpretative Phenomenological Analysis or grounded theory, it does not “require the detailed theoretical and technological knowledge” of these approaches (Braun and Clarke, Qualitative Research in Psychology 3:77–101, 2006). However, (Braun and Clarke, Qualitative Research in Psychology 3:77–101, 2006) emphasized that the theoretical position of the study needs to be made explicit, as there are inherent assumptions regarding the nature of the data that has been analyzed.

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Braun, V., & Clarke, V. (2012). Thematic analysis. In H. Cooper (Ed.), APA handbook of research methods in psychology (vol.2) (pp. 57–71). American Psychological Association.

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Fereday, J., & Muir-Cochrane, E. (2006). Demonstrating rigor using thematic analysis: A hybrid approach of inductive and deductive coding and theme development. International Journal of Qualitative Methods, 5 , 1–11.

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Joffe, H. (2012). Thematic analysis. In D. Harper & A. Thompson (Eds.), Qualitative research methods in mental health and psychotherapy: A guide for students and practitioners (pp. 203–223). Wiley-Blackwell.

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Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11 (4), 589–597. https://doi.org/10.1080/2159676X.2019.1628806

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Vaismoradi, M., Turunen, H., & Bondas, T. (2013). Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study. Nursing & Health Sciences, 15 (3), 398–405. https://doi.org/10.1111/nhs.12048

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4 Thematic Analysis

• Published: December 2015
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This chapter concerns thematic analysis, a technique for analyzing qualitative data that involves looking for patterns of meaning that go beyond counting words or phrases. Underlying themes or issues in data are identified and form the basis for theory. Data are analyzed in a several-step process: (a) data familiarization, (b) initial code generation, (c) searching for themes, (d) reviewing themes, (e) defining and naming themes, and (f) reporting the analysis. The author begins the chapter by placing thematic analysis within the context of qualitative methods in general. She then describes the process of conducting a thematic analysis and illustrates this process using an example of a study of barriers to addressing substance abuse among perpetrators and victims of intimate partner violence in domestic violence court.

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• Knowledge Base
• Methodology
• 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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Published on 10.4.2024 in Vol 26 (2024)

Methodological Frameworks and Dimensions to Be Considered in Digital Health Technology Assessment: Scoping Review and Thematic Analysis

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Four clas faculty researchers secure prestigious early career awards.

Continuing  an upward trend of University of Iowa faculty securing prestigious early-career grants, four investigators from the Departments of Physics and Astronomy and Computer Science have been awarded notable grant awards to advance their careers.

DeRoo, Hoadley advance space instrumentation with Nancy Grace Roman Technology Fellowships in Astrophysics for Early Career Researchers

Casey DeRoo and Keri Hoadley , both assistant professors in the Department of Physics and Astronomy, each received a Nancy Grace Roman Technology Fellowship in Astrophysics for Early Career Researchers. The NASA fellowship provides each researcher with $500,000 over two years to support their research in space-based instrumentation. 

Hoadley’s research is two-pronged. She will design and ultimately prototype a mirror-based vacuum ultraviolet polarizer, which will allow researchers to access polarized light from space below 120-nanometer wavelength. Polarizing light at such a low wavelength is crucial to building optics for NASA’s future Habitable World Observatory (HWO), the agency’s next flagship astrophysics mission after the Nancy Grace Roman Space Telescope. 

“Our vacuum ultraviolet polarizer project is meant to help set up our lab to propose to NASA for one or more follow-up technology programs, including adapting this polarizer for use in vacuum systems, duplicating it and measuring its efficiency to measure additional flavors of polarized UV light, quantifying the polarization effects introduced by UV optical components that may be used on HWO, and building an astronomical instrument to measure the polarization of UV from around massive stars and throughout star-forming regions,” said Hoadley.

In addition, Hoadley and her team will build a facility to align, calibrate, and integrate small space telescopes before flight, using a vacuum chamber and wavelengths of light typically only accessible in space, which could help the university win future small satellite and suborbital missions from NASA. 

DeRoo will work to advance diffraction gratings made with electron beams that pattern structures on a nanometer scale.   Like a prism, diffraction gratings spread out and direct light coming from stars and galaxies, allowing researchers to deduce things like the temperature, density, or composition of an astronomical object.

The fellowship will allow DeRoo to upgrade the university’s Raith

 Voyager tool, a specialized fabrication tool hosted by OVPR’s Materials Analysis, Testing and Fabrication (MATFab) facility.

“These upgrades will let us perform algorithmic patterning, which uses computer code to quickly generate the patterns to be manufactured,” DeRoo said. “This is a major innovation that should enable us to make more complex grating shapes as well as make gratings more quickly.” DeRoo added that the enhancements mean his team may be able to make diffraction gratings that allow space instrument designs that are distinctly different from those launched to date.

“For faculty who develop space-based instruments, the Nancy Grace Roman Technology Fellowship is on par with the prestige of an NSF CAREER or Department of Energy Early Career award,” said Mary Hall Reno, professor and department chair. “Our track record with the program elevates our status as a destination university for astrophysics and space physics missions.”

Uppu pursues building blocks quantum computing with NSF CAREER Award

Ravitej Uppu, assistant professor in the Department of Physics and Astronomy, received a 5-year NSF CAREER award of $550,000 to conduct research aimed at amplifying the power of quantum computing and making its application more practical. 

Uppu and his team will explore the properties of light-matter interactions at the level of a single photon interacting with a single molecule, enabling them to generate efficient and high-quality multiphoton entangled states of light. Multiphoton entangled states, in which photons become inextricably linked, are necessary for photons to serve as practical quantum interconnects, transmitting information between quantum computing units, akin to classical cluster computers. 

“ In our pursuit of secure communication, exploiting quantum properties of light is the final frontier,” said Uppu. “However, unavoidable losses that occur in optical fiber links between users can easily nullify the secure link. Our research on multiphoton entangled states is a key building block for implementing ‘quantum repeaters’ that can overcome this challenge.”

Jiang tackles real-world data issues with NSF CAREER Award

Peng Jiang, assistant professor in the Department of Computer Science, received an NSF CAREER Award that will provide $548,944 over five years to develop tools to support the use of sampling-based algorithms. 

Sampling-based algorithms reduce computing costs by processing only a random selection of a dataset, which has made them increasingly popular, but the method still faces limited efficiency. Jiang will develop a suite of tools that simplify the implementation of sampling-based algorithms and improve their efficacy across wide range of computing and big data applications.

“ A simple example of a real-world application is subgraph matching,” Jiang said. “For example, one might be interested in finding a group of people with certain connections in a social network. The use of sampling-based algorithms can significantly accelerate this process.”

In addition to providing undergraduate students the opportunity to engage with this research, Jiang also plans for the project to enhance projects in computer science courses.