two granddaughters when I get the chance!! I enjoy most
music except for Rap! I keep fit by jogging, walking, and bicycling(at least three times a week). I have travelled to many places and RVD the South-West U.S., but I would now like to find that special travel partner to do more travel to warm and interesting countries. I now feel it’s time to meet a nice, kind, honest woman who has some of the same interests as I do; to share the happy times, quiet times and adventures together.
Profile No. | Data Item | Initial Codes |
---|---|---|
2 | I enjoy photography, lapidary & seeking collectables in the form of classic movies & 33 1/3, 45 & 78 RPM recordings from the 1920s, ’30s & ’40s. I am retired & looking forward to travelling to Canada, the USA, the UK & Europe, China. I am unique since I do not judge a book by its cover. I accept people for who they are. I will not demand or request perfection from anyone until I am perfect, so I guess that means everyone is safe. My musical tastes range from Classical, big band era, early jazz, classic ’50s & 60’s rock & roll & country since its inception. | HobbiesFuture plans Travel Unique Values Humour Music |
At this stage, you have to make the themes. These themes should be categorised based on the codes. All the codes which have previously been generated should be turned into themes. Moreover, with the help of the codes, some themes and sub-themes can also be created. This process is usually done with the help of visuals so that a reader can take an in-depth look at first glance itself.
Now you have to take an in-depth look at all the awarded themes again. You have to check whether all the given themes are organised properly or not. It would help if you were careful and focused because you have to note down the symmetry here. If you find that all the themes are not coherent, you can revise them. You can also reshape the data so that there will be symmetry between the themes and dataset here.
For better understanding, a mind-mapping example is given here:
You need to review the themes after coding them. At this stage, you are allowed to play with your themes in a more detailed manner. You have to convert the bigger themes into smaller themes here. If you want to combine some similar themes into a single theme, then you can do it. This step involves two steps for better fragmentation.
You need to observe the coded data separately so that you can have a precise view. If you find that the themes which are given are following the dataset, it’s okay. Otherwise, you may have to rearrange the data again to coherence in the coded data.
Here you have to take into consideration all the corpus data again. It would help if you found how themes are arranged here. It would help if you used the visuals to check out the relationship between them. Suppose all the things are not done accordingly, so you should check out the previous steps for a refined process. Otherwise, you can move to the next step. However, make sure that all the themes are satisfactory and you are not confused.
When all the two steps are completed, you need to make a more précised mind map. An example following the previous cases has been given below:
Now you have to define all the themes which you have given to your data set. You can recheck them carefully if you feel that some of them can fit into one concept, you can keep them, and eliminate the other irrelevant themes. Because it should be precise and clear, there should not be any ambiguity. Now you have to think about the main idea and check out that all the given themes are parallel to your main idea or not. This can change the concept for you.
The given names should be so that it can give any reader a clear idea about your findings. However, it should not oppose your thematic analysis; rather, everything should be organised accurately.
If not, we can help. Our panel of experts makes sure to keep the 3 pillars of Research Methodology strong.
Also, read about discourse analysis , content analysis and survey conducting . we have provided comprehensive guides.
You need to make the final report of all the findings you have done at this stage. You should include the dataset, findings, and every aspect of your analysis in it.
While making the final report , do not forget to consider your audience. For instance, you are writing for the Newsletter, Journal, Public awareness, etc., your report should be according to your audience. It should be concise and have some logic; it should not be repetitive. You can use the references of other relevant sources as evidence to support your discussion.
What is meant by thematic analysis.
Thematic Analysis is a qualitative research method that involves identifying, analyzing, and interpreting recurring themes or patterns in data. It aims to uncover underlying meanings, ideas, and concepts within the dataset, providing insights into participants’ perspectives and experiences.
Disadvantages of primary research – It can be expensive, time-consuming and take a long time to complete if it involves face-to-face contact with customers.
Content analysis is used to identify specific words, patterns, concepts, themes, phrases, or sentences within the content in the recorded communication.
In historical research, a researcher collects and analyse the data, and explain the events that occurred in the past to test the truthfulness of observations.
USEFUL LINKS
LEARNING RESOURCES
COMPANY DETAILS
Reference management. Clean and simple.
When is thematic analysis used, braun and clarke’s reflexive thematic analysis, the six steps of thematic analysis, 1. familiarizing, 2. generating initial codes, 3. generating themes, 4. reviewing themes, 5. defining and naming themes, 6. creating the report, the advantages and disadvantages of thematic analysis, disadvantages, frequently asked questions about thematic analysis, related articles.
Thematic analysis is a broad term that describes an approach to analyzing qualitative data . This approach can encompass diverse methods and is usually applied to a collection of texts, such as survey responses and transcriptions of interviews or focus group discussions. Learn more about different research methods.
A researcher performing a thematic analysis will study a set of data to pinpoint repeating patterns, or themes, in the topics and ideas that are expressed in the texts.
In analyzing qualitative data, thematic analysis focuses on concepts, opinions, and experiences, as opposed to pure statistics. This requires an approach to data that is complex and exploratory and can be anchored by different philosophical and conceptual foundations.
A six-step system was developed to help establish clarity and rigor around this process, and it is this system that is most commonly used when conducting a thematic analysis. The six steps are:
It is important to note that even though the six steps are listed in sequence, thematic analysis is not necessarily a linear process that advances forward in a one-way, predictable fashion from step one through step six. Rather, it involves a more fluid shifting back and forth between the phases, adjusting to accommodate new insights when they arise.
And arriving at insight is a key goal of this approach. A good thematic analysis doesn’t just seek to present or summarize data. It interprets and makes a statement about it; it extracts meaning from the data.
Since thematic analysis is used to study qualitative data, it works best in cases where you’re looking to gather information about people’s views, values, opinions, experiences, and knowledge.
Some examples of research questions that thematic analysis can be used to answer are:
To begin answering these questions, you would need to gather data from participants who can provide relevant responses. Once you have the data, you would then analyze and interpret it.
Because you’re dealing with personal views and opinions, there is a lot of room for flexibility in terms of how you interpret the data. In this way, thematic analysis is systematic but not purely scientific.
A landmark 2006 paper by Victoria Braun and Victoria Clarke (“ Using thematic analysis in psychology ”) established parameters around thematic analysis—what it is and how to go about it in a systematic way—which had until then been widely used but poorly defined.
Since then, their work has been updated, with the name being revised, notably, to “reflexive thematic analysis.”
One common misconception that Braun and Clarke have taken pains to clarify about their work is that they do not believe that themes “emerge” from the data. To think otherwise is problematic since this suggests that meaning is somehow inherent to the data and that a researcher is merely an objective medium who identifies that meaning.
Conversely, Braun and Clarke view analysis as an interactive process in which the researcher is an active participant in constructing meaning, rather than simply identifying it.
The six stages they presented in their paper are still the benchmark for conducting a thematic analysis. They are presented below.
This step is where you take a broad, high-level view of your data, looking at it as a whole and taking note of your first impressions.
This typically involves reading through written survey responses and other texts, transcribing audio, and recording any patterns that you notice. It’s important to read through and revisit the data in its entirety several times during this stage so that you develop a thorough grasp of all your data.
After familiarizing yourself with your data, the next step is coding notable features of the data in a methodical way. This often means highlighting portions of the text and applying labels, aka codes, to them that describe the nature of their content.
In our example scenario, we’re researching the experiences of women over the age of 50 on professional networking social media sites. Interviews were conducted to gather data, with the following excerpt from one interview.
Interview snippet | Codes |
---|---|
It’s hard to get a handle on it. It’s so different from how things used to be done, when networking was about handshakes and business cards. | Confusion Comparison with old networking methods |
It makes me feel like a dinosaur. | Sense of being left behind |
Plus, I've been burned a few times. I'll spend time making what I think are professional connections with male peers, only for the conversation to unexpectedly turn romantic on me. It seems like a lot of men use these sites as a way to meet women, not to develop their careers. It's stressful, to be honest. | Discomfort and unease Unexpected experience with other users |
In the example interview snippet, portions have been highlighted and coded. The codes describe the idea or perception described in the text.
It pays to be exhaustive and thorough at this stage. Good practice involves scrutinizing the data several times, since new information and insight may become apparent upon further review that didn’t jump out at first glance. Multiple rounds of analysis also allow for the generation of more new codes.
Once the text is thoroughly reviewed, it’s time to collate the data into groups according to their code.
Now that we’ve created our codes, we can examine them, identify patterns within them, and begin generating themes.
Keep in mind that themes are more encompassing than codes. In general, you’ll be bundling multiple codes into a single theme.
To draw on the example we used above about women and networking through social media, codes could be combined into themes in the following way:
Codes | Theme |
---|---|
Confusion, Discomfort and unease, Unexpected experience with other users | Negative experience |
Comparison with old networking methods, Sense of being left behind | Perceived lack of skills |
You’ll also be curating your codes and may elect to discard some on the basis that they are too broad or not directly relevant. You may also choose to redefine some of your codes as themes and integrate other codes into them. It all depends on the purpose and goal of your research.
This is the stage where we check that the themes we’ve generated accurately and relevantly represent the data they are based on. Once again, it’s beneficial to take a thorough, back-and-forth approach that includes review, assessment, comparison, and inquiry. The following questions can support the review:
With your final list of themes in hand, the next step is to name and define them.
In defining them, we want to nail down the meaning of each theme and, importantly, how it allows us to make sense of the data.
Once you have your themes defined, you’ll need to apply a concise and straightforward name to each one.
In our example, our “perceived lack of skills” may be adjusted to reflect that the texts expressed uncertainty about skills rather than the definitive absence of them. In this case, a more apt name for the theme might be “questions about competence.”
To finish the process, we put our findings down in writing. As with all scholarly writing, a thematic analysis should open with an introduction section that explains the research question and approach.
This is followed by a statement about the methodology that includes how data was collected and how the thematic analysis was performed.
Each theme is addressed in detail in the results section, with attention paid to the frequency and presence of the themes in the data, as well as what they mean, and with examples from the data included as supporting evidence.
The conclusion section describes how the analysis answers the research question and summarizes the key points.
In our example, the conclusion may assert that it is common for women over the age of 50 to have negative experiences on professional networking sites, and that these are often tied to interactions with other users and a sense that using these sites requires specialized skills.
Thematic analysis is useful for analyzing large data sets, and it allows a lot of flexibility in terms of designing theoretical and research frameworks. Moreover, it supports the generation and interpretation of themes that are backed by data.
There are times when thematic analysis is not the best approach to take because it can be highly subjective, and, in seeking to identify broad patterns, it can overlook nuance in the data.
What’s more, researchers must be judicious about reflecting on how their own position and perspective bears on their interpretations of the data and if they are imposing meaning that is not there or failing to pick up on meaning that is.
Thematic analysis offers a flexible and recursive way to approach qualitative data that has the potential to yield valuable insights about people’s opinions, views, and lived experience. It must be applied, however, in a conscientious fashion so as not to allow subjectivity to taint or obscure the results.
The purpose of thematic analysis is to find repeating patterns, or themes, in qualitative data. Thematic analysis can encompass diverse methods and is usually applied to a collection of texts, such as survey responses and transcriptions of interviews or focus group discussions. In analyzing qualitative data, thematic analysis focuses on concepts, opinions, and experiences, as opposed to pure statistics.
A big advantage of thematic analysis is that it allows a lot of flexibility in terms of designing theoretical and research frameworks. It also supports the generation and interpretation of themes that are backed by data.
A disadvantage of thematic analysis is that it can be highly subjective and can overlook nuance in the data. Also, researchers must be aware of how their own position and perspective influences their interpretations of the data and if they are imposing meaning that is not there or failing to pick up on meaning that is.
How many themes make sense in your thematic analysis of course depends on your topic and the material you are working with. In general, it makes sense to have no more than 6-10 broader themes, instead of having many really detailed ones. You can then identify further nuances and differences under each theme when you are diving deeper into the topic.
Since thematic analysis is used to study qualitative data, it works best in cases where you’re looking to gather information about people’s views, values, opinions, experiences, and knowledge. Therefore, it makes sense to use thematic analysis for interviews.
After familiarizing yourself with your data, the first step of a thematic analysis is coding notable features of the data in a methodical way. This often means highlighting portions of the text and applying labels, aka codes, to them that describe the nature of their content.
Run a free plagiarism check in 10 minutes, automatically generate references for free.
Published on 5 May 2022 by Jack Caulfield . Revised on 7 June 2024.
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:
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.
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:
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.
Once you’ve decided to use thematic analysis, there are different approaches to consider.
There’s the distinction between inductive and deductive approaches:
There’s also the distinction between a semantic and a latent approach:
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:
Interview extract | Codes |
---|---|
Personally, I’m not sure. I think the climate is changing, sure, but I don’t know why or how. People say you should trust the experts, but who’s to say they don’t have their own reasons for pushing this narrative? I’m not saying they’re wrong, I’m just saying there’s reasons not to 100% trust them. The facts keep changing – it used to be called global warming. |
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:
Codes | Theme |
---|---|
Uncertainty | |
Distrust of experts | |
Misinformation |
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.
If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.
Caulfield, J. (2024, June 07). How to Do Thematic Analysis | Guide & Examples. Scribbr. Retrieved 23 September 2024, from https://www.scribbr.co.uk/research-methods/thematic-analysis-explained/
Other students also liked, qualitative vs quantitative research | examples & methods, inductive reasoning | types, examples, explanation, what is deductive reasoning | explanation & examples.
Plain-Language Explanation, Definition & Examples
Before we begin, let’s first lay down some terminology. When undertaking thematic analysis, you’ll make use of codes . A code is a label assigned to a piece of text, and the aim of using a code is to identify and summarise important concepts within a set of data, such as an interview transcript.
For example, if you had the sentence, “My rabbit ate my shoes”, you could use the codes “rabbit” or “shoes” to highlight these two concepts. The process of assigning codes is called qualitative coding . If this is a new concept to you, be sure to check out our detailed post about qualitative coding .
Codes are vital as they lay a foundation for themes . But what exactly is a theme? Simply put, a theme is a pattern that can be identified within a data set. In other words, it’s a topic or concept that pops up repeatedly throughout your data. Grouping your codes into themes serves as a way of summarising sections of your data in a useful way that helps you answer your research question(s) and achieve your research aim(s).
Alright – with that out of the way, let’s jump into the wonderful world of thematic analysis…
Thematic analysis is the study of patterns to uncover meaning . In other words, it’s about analysing the patterns and themes within your data set to identify the underlying meaning. Importantly, this process is driven by your research aims and questions , so it’s not necessary to identify every possible theme in the data, but rather to focus on the key aspects that relate to your research questions .
Although the research questions are a driving force in thematic analysis (and pretty much all analysis methods), it’s important to remember that these questions are not necessarily fixed . As thematic analysis tends to be a bit of an exploratory process, research questions can evolve as you progress with your coding and theme identification.
There are many potential qualitative analysis methods that you can use to analyse a dataset. For example, content analysis , discourse analysis , and narrative analysis are popular choices. So why use thematic analysis?
Thematic analysis is highly beneficial when working with large bodies of data , as it allows you to divide and categorise large amounts of data in a way that makes it easier to digest. Thematic analysis is particularly useful when looking for subjective information , such as a participant’s experiences, views, and opinions. For this reason, thematic analysis is often conducted on data derived from interviews , conversations, open-ended survey responses , and social media posts.
Your research questions can also give you an idea of whether you should use thematic analysis or not. For example, if your research questions were to be along the lines of:
These examples are all research questions centering on the subjective experiences of participants and aim to assess experiences, views, and opinions. Therefore, thematic analysis presents a possible approach.
In short, thematic analysis is a good choice when you are wanting to categorise large bodies of data (although the data doesn’t necessarily have to be large), particularly when you are interested in subjective experiences .
Broadly speaking, there are two overarching approaches to thematic analysis: inductive and deductive . The approach you take will depend on what is most suitable in light of your research aims and questions. Let’s have a look at the options.
The inductive approach involves deriving meaning and creating themes from data without any preconceptions . In other words, you’d dive into your analysis without any idea of what codes and themes will emerge, and thus allow these to emerge from the data.
For example, if you’re investigating typical lunchtime conversational topics in a university faculty, you’d enter the research without any preconceived codes, themes or expected outcomes. Of course, you may have thoughts about what might be discussed (e.g., academic matters because it’s an academic setting), but the objective is to not let these preconceptions inform your analysis.
The inductive approach is best suited to research aims and questions that are exploratory in nature , and cases where there is little existing research on the topic of interest.
In contrast to the inductive approach, a deductive approach involves jumping into your analysis with a pre-determined set of codes . Usually, this approach is informed by prior knowledge and/or existing theory or empirical research (which you’d cover in your literature review ).
For example, a researcher examining the impact of a specific psychological intervention on mental health outcomes may draw on an existing theoretical framework that includes concepts such as coping strategies, social support, and self-efficacy, using these as a basis for a set of pre-determined codes.
The deductive approach is best suited to research aims and questions that are confirmatory in nature , and cases where there is a lot of existing research on the topic of interest.
Regardless of whether you take the inductive or deductive approach, you’ll also need to decide what level of content your analysis will focus on – specifically, the semantic level or the latent level.
A semantic-level focus ignores the underlying meaning of data , and identifies themes based only on what is explicitly or overtly stated or written – in other words, things are taken at face value.
In contrast, a latent-level focus concentrates on the underlying meanings and looks at the reasons for semantic content. Furthermore, in contrast to the semantic approach, a latent approach involves an element of interpretation , where data is not just taken at face value, but meanings are also theorised.
“But how do I know when to use what approach?”, I hear you ask.
Well, this all depends on the type of data you’re analysing and what you’re trying to achieve with your analysis. For example, if you’re aiming to analyse explicit opinions expressed in interviews and you know what you’re looking for ahead of time (based on a collection of prior studies), you may choose to take a deductive approach with a semantic-level focus.
On the other hand, if you’re looking to explore the underlying meaning expressed by participants in a focus group, and you don’t have any preconceptions about what to expect, you’ll likely opt for an inductive approach with a latent-level focus.
Simply put, the nature and focus of your research, especially your research aims , objectives and questions will inform the approach you take to thematic analysis.
Now that you’ve got an understanding of the overarching approaches to thematic analysis, it’s time to have a look at the different types of thematic analysis you can conduct. Broadly speaking, there are three “types” of thematic analysis:
Let’s have a look at each of these:
Reflexive thematic analysis takes an inductive approach, letting the codes and themes emerge from that data. This type of thematic analysis is very flexible, as it allows researchers to change, remove, and add codes as they work through the data. As the name suggests, reflexive thematic analysis emphasizes the active engagement of the researcher in critically reflecting on their assumptions, biases, and interpretations, and how these may shape the analysis.
Reflexive thematic analysis typically involves iterative and reflexive cycles of coding, interpreting, and reflecting on data, with the aim of producing nuanced and contextually sensitive insights into the research topic, while at the same time recognising and addressing the subjective nature of the research process.
Codebook thematic analysis , on the other hand, lays on the opposite end of the spectrum. Taking a deductive approach, this type of thematic analysis makes use of structured codebooks containing clearly defined, predetermined codes. These codes are typically drawn from a combination of existing theoretical theories, empirical studies and prior knowledge of the situation.
Codebook thematic analysis aims to produce reliable and consistent findings. Therefore, it’s often used in studies where a clear and predefined coding framework is desired to ensure rigour and consistency in data analysis.
Coding reliability thematic analysis necessitates the work of multiple coders, and the design is specifically intended for research teams. With this type of analysis, codebooks are typically fixed and are rarely altered.
The benefit of this form of analysis is that it brings an element of intercoder reliability where coders need to agree upon the codes used, which means that the outcome is more rigorous as the element of subjectivity is reduced. In other words, multiple coders discuss which codes should be used and which shouldn’t, and this consensus reduces the bias of having one individual coder decide upon themes.
To recap, the two main approaches to thematic analysis are inductive , and deductive . Then we have the three types of thematic analysis: reflexive, codebook and coding reliability . Which type of thematic analysis you opt for will need to be informed by factors such as:
Now that we’ve covered the “what” in terms of thematic analysis approaches and types, it’s time to look at the “how” of thematic analysis.
At this point, you’re ready to get going with your analysis, so let’s dive right into the thematic analysis process. Keep in mind that what we’ll cover here is a generic process, and the relevant steps will vary depending on the approach and type of thematic analysis you opt for.
The first step in your thematic analysis involves getting a feel for your data and seeing what general themes pop up. If you’re working with audio data, this is where you’ll do the transcription , converting audio to text.
At this stage, you’ll want to come up with preliminary thoughts about what you’ll code , what codes you’ll use for them, and what codes will accurately describe your content. It’s a good idea to revisit your research topic , and your aims and objectives at this stage. For example, if you’re looking at what people feel about different types of dogs, you can code according to when different breeds are mentioned (e.g., border collie, Labrador, corgi) and when certain feelings/emotions are brought up.
As a general tip, it’s a good idea to keep a reflexivity journal . This is where you’ll write down how you coded your data, why you coded your data in that particular way, and what the outcomes of this data coding are. Using a reflexive journal from the start will benefit you greatly in the final stages of your analysis because you can reflect on the coding process and assess whether you have coded in a manner that is reliable and whether your codes and themes support your findings.
As you can imagine, a reflexivity journal helps to increase reliability as it allows you to analyse your data systematically and consistently. If you choose to make use of a reflexivity journal, this is the stage where you’ll want to take notes about your initial codes and list them in your journal so that you’ll have an idea of what exactly is being reflected in your data. At a later stage in the analysis, this data can be more thoroughly coded, or the identified codes can be divided into more specific ones.
Step 2! You’re going strong. In this step, you’ll want to look out for patterns or themes in your codes. Moving from codes to themes is not necessarily a smooth or linear process. As you become more and more familiar with the data, you may find that you need to assign different codes or themes according to new elements you find. For example, if you were analysing a text talking about wildlife, you may come across the codes, “pigeon”, “canary” and “budgerigar” which can fall under the theme of birds.
As you work through the data, you may start to identify subthemes , which are subdivisions of themes that focus specifically on an aspect within the theme that is significant or relevant to your research question. For example, if your theme is a university, your subthemes could be faculties or departments at that university.
By now you’ll have a good idea of your codes, themes, and potentially subthemes. Now it’s time to review all the themes you’ve identified . In this step, you’ll want to check that everything you’ve categorised as a theme actually fits the data, whether the themes do indeed exist in the data, whether there are any themes missing , and whether you can move on to the next step knowing that you’ve coded all your themes accurately and comprehensively . If you find that your themes have become too broad and there is far too much information under one theme, it may be useful to split this into more themes so that you’re able to be more specific with your analysis.
By this point, your analysis will really start to take shape. In the previous step, you reviewed and refined your themes, and now it’s time to label and finalise them . It’s important to note here that, just because you’ve moved onto the next step, it doesn’t mean that you can’t go back and revise or rework your themes. In contrast to the previous step, finalising your themes means spelling out what exactly the themes consist of, and describe them in detail . If you struggle with this, you may want to return to your data to make sure that your data and coding do represent the themes, and if you need to divide your themes into more themes (i.e., return to step 3).
When you name your themes, make sure that you select labels that accurately encapsulate the properties of the theme . For example, a theme name such as “enthusiasm in professionals” leaves the question of “who are the professionals?”, so you’d want to be more specific and label the theme as something along the lines of “enthusiasm in healthcare professionals”.
It is very important at this stage that you make sure that your themes align with your research aims and questions . When you’re finalising your themes, you’re also nearing the end of your analysis and need to keep in mind that your final report (discussed in the next step) will need to fit in with the aims and objectives of your research.
In your reflexivity journal, you’ll want to write down a few sentences describing your themes and how you decided on these. Here, you’ll also want to mention how the theme will contribute to the outcomes of your research, and also what it means in relation to your research questions and focus of your research.
You’re nearly done! Now that you’ve analysed your data, it’s time to report on your findings. A typical thematic analysis report consists of:
When writing your report, make sure that you provide enough information for a reader to be able to evaluate the rigour of your analysis. In other words, the reader needs to know the exact process you followed when analysing your data and why. The questions of “what”, “how”, “why”, “who”, and “when” may be useful in this section.
So, what did you investigate? How did you investigate it? Why did you choose this particular method? Who does your research focus on, and who are your participants? When did you conduct your research, when did you collect your data, and when was the data produced? Your reflexivity journal will come in handy here as within it you’ve already labelled, described, and supported your themes.
If you’re undertaking a thematic analysis as part of a dissertation or thesis, this discussion will be split across your methodology, results and discussion chapters . For more information about those chapters, check out our detailed post about dissertation structure .
Getting familiar with your data: Here you’ll read through your data and get a general overview of what you’re working with. At this stage, you may identify a few general codes and themes that you’ll make use of in the next step.
Search for patterns or themes in your codes : Here you’ll dive into your data and pick out the themes and codes relevant to your research question(s).
Review themes : In this step, you’ll revisit your codes and themes to make sure that they are all truly representative of the data, and that you can use them in your final report.
Finalise themes : Here’s where you “solidify” your analysis and make it report-ready by describing and defining your themes.
Produce your report : This is the final step of your thematic analysis process, where you put everything you’ve found together and report on your findings.
In the video below, we share 6 time-saving tips and tricks to help you approach your thematic analysis as effectively and efficiently as possible.
In this article, we’ve covered the basics of thematic analysis – what it is, when to use it, the different approaches and types of thematic analysis, and how to perform a thematic analysis.
If you have any questions about thematic analysis, drop a comment below and we’ll do our best to assist. If you’d like 1-on-1 support with your thematic analysis, be sure to check out our research coaching services here .
Triangulation is one of the best ways to enhance the credibility of your research. Learn about the different options here.
Learn everything you need to know about research limitations (AKA limitations of the study). Includes practical examples from real studies.
Learn about in vivo coding, a popular qualitative coding technique ideal for studies where the nuances of language are central to the aims.
Learn about process coding, a popular qualitative coding technique ideal for studies exploring processes, actions and changes over time.
Inductive, Deductive & Abductive Coding Qualitative Coding Approaches Explained...
📄 FREE TEMPLATES
Research Topic Ideation
Proposal Writing
Literature Review
Methodology & Analysis
Academic Writing
Referencing & Citing
Apps, Tools & Tricks
The Grad Coach Podcast
I really appreciate the help
Hello Sir, how many levels of coding can be done in thematic analysis? We generate codes from the transcripts, then subthemes from the codes and themes from subthemes, isn’t it? Should these themes be again grouped together? how many themes can be derived?can you please share an example of coding through thematic analysis in a tabular format?
I’ve found the article very educative and useful
Excellent. Very helpful and easy to understand.
This article so far has been most helpful in understanding how to write an analysis chapter. Thank you.
My research topic is the challenges face by the school principal on the process of procurement . Thematic analysis is it sutable fir data analysis ?
It is a great help. Thanks.
Best advice. Worth reading. Thank you.
Where can I find an example of a template analysis table ?
Finally I got the best article . I wish they also have every psychology topics.
Hello, Sir/Maam
I am actually finding difficulty in doing qualitative analysis of my data and how to triangulate this with quantitative data. I encountered your web by accident in the process of searching for a much simplified way of explaining about thematic analysis such as coding, thematic analysis, write up. When your query if I need help popped up, I was hesitant to answer. Because I think this is for fee and I cannot afford. So May I just ask permission to copy for me to read and guide me to study so I can apply it myself for my gathered qualitative data for my graduate study.
Thank you very much! this is very helpful to me in my Graduate research qualitative data analysis.
Thank you very much. I find your guidance here helpful. Kindly let help me understand how to write findings and discussions.
i am having troubles with the concept of framework analysis which i did not find here and i have been an assignment on framework analysis
I was discouraged and felt insecure because after more than a year of writing my thesis, my work seemed lost its direction after being checked. But, I am truly grateful because through the comments, corrections, and guidance of the wisdom of my director, I can already see the bright light because of thematic analysis. I am working with Biblical Texts. And thematic analysis will be my method. Thank you.
lovely and helpful. thanks
very informative information.
thank you very much!, this is very helpful in my report, God bless……..
Thank you for the insight. I am really relieved as you have provided a super guide for my thesis.
Thanks a lot, really enlightening
excellent! very helpful thank a lot for your great efforts
I am currently conducting a research on the Economic challenges to migrant integration. Using interviews to understand the challenges by interviewing professionals working with migrants. Wouks appreciate help with how to do this using the thematic approach. Thanks
The article cleared so many issues that I was not certain of. Very informative. Thank you.
i really appreciate the learning that learned from here
This was absolutely informative! I’ll certainly be using Grad Coach often 🙂 thank you!
Your email address will not be published. Required fields are marked *
Save my name, email, and website in this browser for the next time I comment.
Submit Comment
Saul McLeod, PhD
Editor-in-Chief for Simply Psychology
BSc (Hons) Psychology, MRes, PhD, University of Manchester
Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.
Learn about our Editorial Process
Olivia Guy-Evans, MSc
Associate Editor for Simply Psychology
BSc (Hons) Psychology, MSc Psychology of Education
Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.
On This Page:
Thematic analysis is a qualitative research method used to identify, analyze, and interpret patterns of shared meaning (themes) within a given data set, which can be in the form of interviews , focus group discussions , surveys, or other textual data.
Thematic analysis is a useful method for research seeking to understand people’s views, opinions, knowledge, experiences, or values from qualitative data.
This method is widely used in various fields, including psychology, sociology, and health sciences.
Thematic analysis minimally organizes and describes a data set in rich detail. Often, though, it goes further than this and interprets aspects of the research topic.
It’s important to note that the types of thematic analysis are not mutually exclusive, and researchers may adopt elements from different approaches depending on their research questions, goals, and epistemological stance.
The choice of approach should be guided by the research aims, the nature of the data, and the philosophical assumptions underpinning the study.
Feature | Coding Reliability TA | Codebook TA | Reflexive TA |
---|---|---|---|
Conceptualized as topic summaries of the data | Typically conceptualized as topic summaries | Conceptualized as patterns of shared meaning that are underpinned by a central organizing concept | |
Involves using a coding frame or codebook, which may be predetermined or generated from the data, to find evidence for themes or allocate data to predefined topics. Ideally, two or more researchers apply the coding frame separately to the data to avoid contamination | Typically involves early theme development and the use of a codebook and structured approach to coding | Involves an active process in which codes are developed from the data through the analysis. The researcher’s subjectivity shapes the coding and theme development process | |
Emphasizes securing the reliability and accuracy of data coding, reflecting (post)positivist research values. Prioritizes minimizing subjectivity and maximizing objectivity in the coding process | Combines elements of both coding reliability and reflexive TA, but qualitative values tend to predominate. For example, the “accuracy” or “reliability” of coding is not a primary concern | Emphasizes the role of the researcher in knowledge construction and acknowledges that their subjectivity shapes the research process and outcomes | |
Often used in research where minimizing subjectivity and maximizing objectivity in the coding process are highly valued | Commonly employed in applied research, particularly when information needs are predetermined, deadlines are tight, and research teams are large and may include qualitative novices. Pragmatic concerns often drive its use | Well-suited for exploring complex research issues. Often used in research where the researcher’s active role in knowledge construction is acknowledged and valued. Can be used to analyze a wide range of data, including interview transcripts, focus groups, and policy documents | |
Themes are often predetermined or generated early in the analysis process, either prior to data analysis or following some familiarization with the data | Themes are typically developed early in the analysis process | Themes are developed later in the analytic process, emerging from the coded data | |
The researcher’s subjectivity is minimized, aiming for objectivity in coding | The researcher’s subjectivity is acknowledged, though structured coding methods are used | The researcher’s subjectivity is viewed as a valuable resource in the analytic process and is considered to inevitably shape the research findings |
Coding reliability TA emphasizes using coding techniques to achieve reliable and accurate data coding, which reflects (post)positivist research values.
This approach emphasizes the reliability and replicability of the coding process. It involves multiple coders independently coding the data using a predetermined codebook.
The goal is to achieve a high level of agreement among the coders, which is often measured using inter-rater reliability metrics.
This approach often involves a coding frame or codebook determined in advance or generated after familiarization with the data.
In this type of TA, two or more researchers apply a fixed coding frame to the data, ideally working separately.
Some researchers even suggest that some coders should be unaware of the research question or area of study to prevent bias in the coding process.
Statistical tests are used to assess the level of agreement between coders, or the reliability of coding. Any differences in coding between researchers are resolved through consensus.
This approach is more suitable for research questions that require a more structured and reliable coding process, such as in content analysis or when comparing themes across different data sets.
Codebook TA, such as template, framework, and matrix analysis, combines coding reliability and reflexive elements.
Codebook TA, while employing structured coding methods like those used in coding reliability TA, generally prioritizes qualitative research values, such as reflexivity.
In this approach, the researcher develops a codebook based on their initial engagement with the data. The codebook contains a list of codes, their definitions, and examples from the data.
The codebook is then used to systematically code the entire data set. This approach allows for a more detailed and nuanced analysis of the data, as the codebook can be refined and expanded throughout the coding process.
It is particularly useful when the research aims to provide a comprehensive description of the data set.
Codebook TA is often chosen for pragmatic reasons in applied research, particularly when there are predetermined information needs, strict deadlines, and large teams with varying levels of qualitative research experience
The use of a codebook in this context helps to map the developing analysis, which is thought to improve teamwork, efficiency, and the speed of output delivery.
This approach emphasizes the role of the researcher in the analysis process. It acknowledges that the researcher’s subjectivity, theoretical assumptions, and interpretative framework shape the identification and interpretation of themes.
In reflexive TA, analysis starts with coding after data familiarization. Unlike other TA approaches, there is no codebook or coding frame. Instead, researchers develop codes as they work through the data.
As their understanding grows, codes can change to reflect new insights—for example, they might be renamed, combined with other codes, split into multiple codes, or have their boundaries redrawn.
If multiple researchers are involved, differences in coding are explored to enhance understanding, not to reach a consensus. The finalized coding is always open to new insights and coding.
Reflexive thematic analysis involves a more organic and iterative process of coding and theme development. The researcher continuously reflects on their role in the research process and how their own experiences and perspectives might influence the analysis.
This approach is particularly useful for exploratory research questions and when the researcher aims to provide a rich and nuanced interpretation of the data.
The process is characterized by a recursive movement between the different phases, rather than a strict linear progression.
This means that researchers might revisit earlier phases as their understanding of the data evolves, constantly refining their analysis.
For instance, during the reviewing and developing themes phase, researchers may realize that their initial codes don’t effectively capture the nuances of the data and might need to return to the coding phase.
This back-and-forth movement continues throughout the analysis, ensuring a thorough and evolving understanding of the data
Familiarization is crucial, as it helps researchers figure out the type (and number) of themes that might emerge from the data.
Familiarization involves immersing yourself in the data by reading and rereading textual data items, such as interview transcripts or survey responses.
You should read through the entire data set at least once, and possibly multiple times, until you feel intimately familiar with its content.
By the end of the familiarization step, the researcher should have a good grasp of the overall content of the data, the key issues and experiences discussed by the participants, and any initial patterns or themes that emerge.
This deep engagement with the data sets the stage for the subsequent steps of thematic analysis, where the researcher will systematically code and analyze the data to identify and interpret the central themes.
Codes are concise labels or descriptions assigned to segments of the data that capture a specific feature or meaning relevant to the research question.
The process of qualitative coding helps the researcher organize and reduce the data into manageable chunks, making it easier to identify patterns and themes relevant to the research question.
Think of it this way: If your analysis is a house, themes are the walls and roof, while codes are the individual bricks and tiles.
Coding is an iterative process, with researchers refining and revising their codes as their understanding of the data evolves.
The ultimate goal is to develop a coherent and meaningful coding scheme that captures the richness and complexity of the participants’ experiences and helps answer the research questions.
Coding can be done manually (paper transcription and pen or highlighter) or by means of software (e.g. by using NVivo, MAXQDA or ATLAS.ti).
After generating your first code, compare each new data extract to see if an existing code applies or a new one is needed.
Most codes will be a mix of descriptive and conceptual. Novice coders tend to generate more descriptive codes initially, developing more conceptual approaches with experience.
You have enough codes to capture the data’s diversity and patterns of meaning, with most codes appearing across multiple data items.
The number of codes you generate will depend on your topic, data set, and coding precision.
Searching for themes begins after all data has been initially coded and collated, resulting in a comprehensive list of codes identified across the data set.
This step involves shifting from the specific, granular codes to a broader, more conceptual level of analysis.
Thematic analysis is not about “discovering” themes that already exist in the data, but rather actively constructing or generating themes through a careful and iterative process of examination and interpretation.
The process of collating codes into potential themes involves grouping codes that share a unifying feature or represent a coherent and meaningful pattern in the data.
The researcher looks for patterns, similarities, and connections among the codes to develop overarching themes that capture the essence of the data.
By the end of this step, the researcher will have a collection of candidate themes and sub-themes, along with their associated data extracts.
However, these themes are still provisional and will be refined in the next step of reviewing the themes.
The searching for themes step helps the researcher move from a granular, code-level analysis to a more conceptual, theme-level understanding of the data.
This process is similar to sculpting, where the researcher shapes the “raw” data into a meaningful analysis.
This involves grouping codes that share a unifying feature or represent a coherent pattern in the data:
Thematic maps can help visualize the relationship between codes and themes. These visual aids provide a structured representation of the emerging patterns and connections within the data, aiding in understanding the significance of each theme and its contribution to the overall research question.
Example : Studying first-generation college students, the researcher might notice that the codes “financial challenges,” “working part-time,” and “scholarships” all relate to the broader theme of “Financial Obstacles and Support.”
Braun and Clarke distinguish between two different conceptualizations of themes : topic summaries and shared meaning
When grouping codes into themes, it’s crucial to ensure they share a central organizing concept or idea, reflecting a shared meaning rather than just belonging to the same topic.
Thematic analysis aims to uncover patterns of shared meaning within the data that offer insights into the research question
For example, codes centered around the concept of “Negotiating Sexual Identity” might not form one comprehensive theme, but rather two distinct themes: one related to “coming out and being out” and another exploring “different versions of being a gay man.”
In this approach, themes simply summarize what participants mentioned about a particular topic, without necessarily revealing a unified meaning.
These themes are often underdeveloped and lack a central organizing concept.
It’s crucial to avoid creating themes that are merely summaries of data domains or directly reflect the interview questions.
Example : A theme titled “Incidents of homophobia” that merely describes various participant responses about homophobia without delving into deeper interpretations would be a topic summary theme.
Tip : Using interview questions as theme titles without further interpretation or relying on generic social functions (“social conflict”) or structural elements (“economics”) as themes often indicates a lack of shared meaning and thorough theme development. Such themes might lack a clear connection to the specific dataset
Instead, themes should represent a deeper level of interpretation, capturing the essence of the data and providing meaningful insights into the research question.
These themes go beyond summarizing a topic by identifying a central concept or idea that connects the codes.
They reflect a pattern of shared meaning across different data points, even if those points come from different topics.
Example : The theme “‘There’s always that level of uncertainty’: Compulsory heterosexuality at university” effectively captures the shared experience of fear and uncertainty among LGBT students, connecting various codes related to homophobia and its impact on their lives.
Once a potential theme is identified, all coded data extracts associated with the codes grouped under that theme are collated. This ensures a comprehensive view of the data pertaining to each theme.
This involves reviewing the collated data extracts for each code and organizing them under the relevant themes.
For example, if you have a potential theme called “Student Strategies for Test Preparation,” you would gather all data extracts that have been coded with related codes, such as “Time Management for Test Preparation” or “Study Groups for Test Preparation”.
You can then begin reviewing the data extracts for each theme to see if they form a coherent pattern.
This step helps to ensure that your themes accurately reflect the data and are not based on your own preconceptions.
It’s important to remember that coding is an organic and ongoing process.
You may need to re-read your entire data set to see if you have missed any data that is relevant to your themes, or if you need to create any new codes or themes.
The researcher should ensure that the data extracts within each theme are coherent and meaningful.
Example : The researcher would gather all the data extracts related to “Financial Obstacles and Support,” such as quotes about struggling to pay for tuition, working long hours, or receiving scholarships.
Once you have gathered all the relevant data extracts under each theme, review the themes to ensure they are meaningful and distinct.
This step involves analyzing how different codes combine to form overarching themes and exploring the hierarchical relationship between themes and sub-themes.
Within a theme, there can be different levels of themes, often organized hierarchically as main themes and sub-themes.
Consider how the themes tell a coherent story about the data and address the research question.
If some themes seem to overlap or are not well-supported by the data, consider combining or refining them.
If a theme is too broad or diverse, consider splitting it into separate themes or sub-theme.
Example : The researcher might identify “Academic Challenges” and “Social Adjustment” as other main themes, with sub-themes like “Imposter Syndrome” and “Balancing Work and School” under “Academic Challenges.” They would then consider how these themes relate to each other and contribute to the overall understanding of first-generation college students’ experiences.
The researcher reviews, modifies, and develops the preliminary themes identified in the previous step.
This phase involves a recursive process of checking the themes against the coded data extracts and the entire data set to ensure they accurately reflect the meanings evident in the data.
The purpose is to refine the themes, ensuring they are coherent, consistent, and distinctive.
According to Braun and Clarke, a well-developed 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”.
Revisions at this stage might involve creating new themes, refining existing themes, or discarding themes that do not fit the data
The themes are finalized when the researcher is satisfied with the theme names and definitions.
If the analysis is carried out by a single researcher, it is recommended to seek feedback from an external expert to confirm that the themes are well-developed, clear, distinct, and capture all the relevant data.
Defining themes means determining the exact meaning of each theme and understanding how it contributes to understanding the data.
This process involves formulating exactly what we mean by each theme. The researcher should consider what a theme says, if there are subthemes, how they interact and relate to the main theme, and how the themes relate to each other.
Themes should not be overly broad or try to encompass too much, and should have a singular focus. They should be distinct from one another and not repetitive, although they may build on one another.
In this phase the researcher specifies the essence of each theme.
Naming themes involves developing a clear and concise name that effectively conveys the essence of each theme to the reader. A good name for a theme is informative, concise, and catchy.
For example, “‘There’s always that level of uncertainty’: Compulsory heterosexuality at university” is a strong theme name because it captures the theme’s meaning. In contrast, “incidents of homophobia” is a weak theme name because it only states the topic.
For instance, a theme labeled “distrust of experts” might be renamed “distrust of authority” or “conspiracy thinking” after careful consideration of the theme’s meaning and scope.
A thematic analysis report should provide a convincing and clear, yet complex story about the data that is situated within a scholarly field.
A balance should be struck between the narrative and the data presented, ensuring that the report convincingly explains the meaning of the data, not just summarizes it.
To achieve this, the report should include vivid, compelling data extracts illustrating the themes and incorporate extracts from different data sources to demonstrate the themes’ prevalence and strengthen the analysis by representing various perspectives within the data.
The report should be written in first-person active tense, unless otherwise stated in the reporting requirements.
Regardless of the presentation style, researchers should aim to “show” what the data reveals and “tell” the reader what it means in order to create a convincing analysis.
The analysis should go beyond a simple summary of the participant’s words and instead interpret the meaning of the data.
Themes should connect logically and meaningfully and, if relevant, should build on previous themes to tell a coherent story about the data.
The report should include vivid, compelling data extracts that clearly illustrate the theme being discussed and should incorporate extracts from different data sources, rather than relying on a single source.
Although it is tempting to rely on one source when it eloquently expresses a particular aspect of the theme, using multiple sources strengthens the analysis by representing a wider range of perspectives within the data.
Researchers should strive to maintain a balance between the amount of narrative and the amount of data presented.
When researchers are both reflexive and transparent in their thematic analysis, it strengthens the trustworthiness and rigor of their findings.
The explicit acknowledgement of potential biases and the detailed documentation of the analytical process provide a stronger foundation for the interpretation of the data, making it more likely that the findings reflect the perspectives of the participants rather than the biases of the researcher.
Reflexivity involves critically examining one’s own assumptions and biases, is crucial in qualitative research to ensure the trustworthiness of findings.
It requires acknowledging that researcher subjectivity is inherent in the research process and can influence how data is collected, analyzed, and interpreted.
Reflexivity encourages researchers to explicitly acknowledge their preconceived notions, theoretical leanings, and potential biases.
By actively reflecting on how these factors might influence their interpretation of the data, researchers can take steps to mitigate their impact.
This might involve seeking alternative explanations, considering contradictory evidence, or discussing their interpretations with others to gain different perspectives.
Transparency refers to clearly documenting the research process, including coding decisions, theme development, and the rationale behind behind theme development.
This openness allows others to understand how the analysis was conducted and to assess the credibility of the findings
This transparency helps ensure the trustworthiness and rigor of the findings, allowing others to understand and potentially replicate the analysis.
Transparency requires researchers to provide a clear and detailed account of their analytical choices throughout the research process.
This includes documenting the rationale behind coding decisions, the process of theme development, and any changes made to the analytical approach during the study.
By making these decisions transparent, researchers allow others to scrutinize their work and assess the potential for bias.
Academia.edu no longer supports Internet Explorer.
To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser .
Enter the email address you signed up with and we'll email you a reset link.
Download Free PDF
Data analysis is central to credible qualitative research. Indeed the qualitative researcher is often described as the research instrument insofar as his or her ability to understand, describe and interpret experiences and perceptions is key to uncovering meaning in particular circumstances and contexts. While much has been written about qualitative analysis from a theoretical perspective we noticed that often novice, and even more experienced researchers, grapple with the ‘how’ of qualitative analysis. Here we draw on Braun and Clarke’s (2006) framework and apply it in a systematic manner to describe and explain the process of analysis within the context of learning and teaching research. We illustrate the process using a worked example based on (with permission) a short extract from a focus group interview, conducted with undergraduate students.
Book Chapter in: Data Collection and Analysis in Scientific Qualitative Research, 2024
Thematic analysis has evolved as a prominent qualitative data analysis method, rooted in the rich history of social sciences and, in particular, psychology. It stands as a cornerstone in qualitative inquiry, offering researchers a systematic approach to identifying and interpreting patterns, or themes, within qualitative data. This chapter seeks to provide a comprehensive overview of thematic analysis within the context of qualitative research, addressing its theoretical foundations, methodolog-ical considerations, and practical applications. It explores approaches to thematic analysis, including induction, deduction, and abduction, highlighting their distinctive characteristics and applications to qualitative data analysis. It also outlines key steps involved in conducting thematic analysis. Overall, this chapter aims to equip researchers, practitioners, and students with the knowledge and skills necessary to conduct rigorous and insightful thematic analyses of qualitative data.
Currents in Pharmacy Teaching and Learning, 2018
A Brief Introduction to Thematic Analysis, 2019
This paper discusses thematic analysis, a popular yet often misunderstood qualitative data analysis method. It is written with postgraduate and institutional researchers who already have an appreciation of qualitative data analysis in mind. The paper takes a practical rather than a theoretical approach to thematic analysis focusing on methods and processes that are applied by our researchers as they go about the analysis. The approaches taken may, therefore, slightly differ from theory although they are highly relatable.
Thematic analysis (TA), as a qualitative analytic method, is widely used in health care, psychology, and beyond. However, scant details are often given to demonstrate the process of data analysis, especially in the field of education. This article describes how a hybrid approach of TA was applied to interpret multiple data sources in a practitioner inquiry. Particular attention is given to the inductive and deductive coding and theme development process of TA. Underpinned by the constructivist epistemology, codes were driven by both data per se and theories, through a "bottom-up" and "top-down" approach to identify themes. A detailed example of six steps of data analysis is presented, which evidences the systematic analysis of raw data from observation and research journals, students' focus groups, and a classroom teacher's semistructured interviews. This example demonstrates how classroom practice was unpacked and how insiders' insights were interpre...
The Qualitative Report, 2023
Gareth Terry and Nikki Hayfield's book, Essentials of Thematic Analysis, introduces readers to reflexive thematic analysis, a method for analyzing interview and focus group transcripts, qualitative survey responses, and other qualitative data. This method is based on the understanding that we all exist in a context from which we can see and speak. In this way, researchers produce knowledge that represents situated truths and allow them to understand others' perspectives on a given topic. The book shows how to construct a "positioned reality of the situation" from qualitative data. According to the authors, this method is not a methodology but rather a method; that is, a theoretical framework. They emphasize adaptability and subjectivity and go beyond data summaries to understand underlying structures. This method requires frequent data exploration and re-evaluation. It can be studied by both novices and experts. The method is illustrated with notes, illustration, and examples. This book provides a straightforward, concise, and comprehensive description of the authors' approach, including its methodological rigor, advantages, and limitations.
Qualitative techniques for workplace data analysis, 2018
The popularity of qualitative methods in social science research is a well-noted and most welcomed fact. Thematic analysis, the often-used methods of qualitative research, provides concise description and interpretation in terms of themes and patterns from a data set. The application of thematic analysis requires trained expertise and should not be used in a prescriptive, linear, and inflexible manner while analyzing data. It should rather be implemented in relation to research question and data availability. To ensure its proper usage, Braun and Clarke have propounded the simplest yet effective six-step method to conduct thematic analysis. In spite of its systematic step-driven process, thematic analysis provides core skills to conduct different other forms of qualitative analysis. Thematic analysis, through its theoretical freedom, flexibility, rich and detailed yet complex analytical account has emerged as the widely used and most effective qualitative research tool in social and organizational context.
Thematic analysis has received increased attention from the research academic community, echoing Braun and Clarke's (2006) influential argument of its theoretical accessibility and flexibility. Along with its current status, dilemmas have arisen in regard to its practice as a result of escalated demand for analytical software programs. Synchysis, the rhetorical practice of creating bewilderment by scattering words, endures in critical reviews and in the analysis of data derived from social media platforms. This paper departs from a simple replication of existing studies by addressing current issues as a result of the evolution of thematic analysis. Furthermore, it outlines specific implications (step-by-step guidance) while incorporating the somewhat overlooked phase of the creation of conceptual diagrams and theory-development during the stages of conducting a rigorous thematic analysis.
Qualitative data analysis is a distinctive form of analysis in the social research enterprise. It is an approach that is less understood than its counterpart—quantitative analysis. Diversity and flexibility are main features of qualitative data analysis. These features also expose it to the danger of doing it anyhow—a slapdash analysis unbecoming of scientific endeavor. Despite its diversity there are common features to the analysis of qualitative data that beginning researchers or trainee-social scientists, such as undergraduates, should be familiar with. This is the focus of this chapter. It focuses on necessary areas in data analysis to help this category of students to make sense of their qualitative data. It covers sources and types of qualitative data, basic issues and procedures in qualitative data analysis. It presents a systematic, disciplined, transparent and describable process to the analysis of qualitative data in consonance with the nature of the science and its method.
Nurse Researcher, 2017
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.
Human Resource Development Review, 2020
Qualitative Research, 2010
International Forum, 2017
BMC Medical Research Methodology
Qualitative research in psychology, 2006
Evidence-Based Nursing, 2013
Social Science Computer Review. 22: 187-196, 2004
Journal of advanced nursing, 2001
Prof Doc Thesis
Authors | |
---|---|
Type | Prof Doc Thesis |
Abstract | The impact of technology has been a key interest in gambling literature. Quantitative research studies appear to be prominent in the gambling field identifying positive correlations between positive attitudes towards gambling and problem gambling. Given the increased coverage of gambling in the media and the advances in technology, young people are more exposed to the behaviour. Young adults at the age of 18 are legal to gamble anywhere, it would be important for us to understand how they perceive gambling in order to shape support services for young people with problem gambling. |
Year | 2017 |
Digital Object Identifier (DOI) | |
Publication dates | |
Aug 2017 | |
Publication process dates | |
12 Jun 2018 | |
Publisher's version | Rajmangal T Thesis.pdf |
https://repository.uel.ac.uk/item/84qyz
Log in to edit
Dissertations 2: structure: thematic.
In the humanities, a thematic dissertation is often structured like a long essay. It can contain:
Title page
Abstract
Table of contents
Introduction
Literature review (which can be included in the introduction rather than as a separate chapter. Check with your supervisor if you are unsure).
Theme 1
Theme 2
Theme 3
Conclusion
Bibliography
Appendices
Abstracts are used by other researchers to establish the relevance of the study to their own work. Therefore, they should contain the what, why, who, where and how of your project.
They are typically between 250 – 300 words long, offer a summary of the main findings and present the conclusions, so you should attempt to write an abstract (if requested), after you have finished writing the dissertation.
A typical abstract summarises:
What the study aimed to achieve
The methodology used
Why the research was conducted
Why the research is important
Who/what was researched
The table of contents should list all the items included in your dissertation.
It is a good idea to use the electronic table of contents feature in Word to automatically link it to your chapter headings and page numbers. Attempting to manually create a table of contents means that you will have to adjust your page numbers every time you edit your work before submission, which may waste valuable time!
This useful video will walk you through the formatting of longer documents using the electronic table of contents feature.
The introduction explains the how, what, where, when, why and who of the research. It introduces the reader to your dissertation and should act as a clear guide as to what it will cover.
The introduction may include the following content:
Introduce the topic of the dissertation
Identify the scope of your research
Indicate your approach
Normally, the introduction is roughly 10% of a dissertation word count.
The term “literature” in “literature review” comprises scholarly articles, books, and other sources (e.g. reports) relevant to a particular issue, area of research or theory. In a dissertation, the literature review illustrates what the literature already says on your research subject, providing summary and synthesis of such literature.
It is generally structured by topic, starting from general background and concepts, and then addressing what can be found - and cannot be found - on the specific focus of your dissertation. Indeed, the literature review should identify gaps in the literature, that your research aims to fill. This requires you to engage critically with the literature, not merely reproduce the critical understanding of others.
In sum, literature reviews should demonstrate how your research question can be located in a wider field of inquiry. Therefore, a literature review needs to address the connections between your work and the work of others by highlighting links between them. In doing so, you will demonstrate the foundations of your project and show how you are taking the line of inquiry forwards.
By the end of your literature review, your reader should be able to see:
The gap in knowledge and understanding which you say exists in the field.
How your research question will work within that gap.
The work other researchers have carried out and the issues debated in the field.
That you have a good understanding of the field and that you are critically engaged with the debates (Burnett, 2009).
For more detailed guidance on how to write literature reviews, check out the Literature Review Guide.
In a thematic structure, the core chapters present analysis and discussion of different themes relevant to answer the research question and support the overall argument of the dissertation. The chapters will include analysis of texts/ research material. They can explore and connect academic theories/research to develop an argument. Stella Cottrell offers some good guidance on how to structure your theme chapters. Each chapter should have the following elements (Cottrell, 2014, p183):
Theme: What is the theme of this chapter? Sequence your themes logically (e.g. from general to specific).
Argument: What argument does this chapter present?
Material: What material you will be using for this chapter?
Clustering: What are the main points you want to make? Deal with one point at a time, and don't jum around? Dedicate your points to sub-headings and paragraphs.
Sequence: In what order are you going to present the points you want to make in this chapter? Draw an outline of the chapter before starting writing it.
Introduction and Conclusion: Each chapter should have a short introduction and conclusion.
The conclusion is the final chapter of your dissertation. It should flow logically from the previously presented text; therefore, you should avoid introducing new ideas, new data, or a new direction.
Ideally, the conclusion should leave the reader with a clear understanding of the discovery or argument you have advanced.
This can be done by:
Summarising and synthesising your main findings and how they relate to your research question or hypotheses
Demonstrating the relevance and importance of your work in the wider context of your field. For example, what recommendations would you make for future research? What do we know now that we didn’t know before?
Link your conclusion to your introduction as both frame your dissertation.
A conclusion is roughly five to ten percent of the word count of the dissertation.
Avoid excessive detail. Decide what your reader needs to know.
Don’t introduce any new information such as theories, data or ideas.
Sum up the main points of your research.
While writing your dissertation, you would have referred to the works and research of many different authors and editors in your field of study. These works should be acknowledged in the bibliography where you will list writers alphabetically by surname.
For example:
Poloian, L.R. (2013). Retailing principles: global, multichannel, and managerial viewpoints. New York: Fairchild. Biggs, J. and Tang, C. (2011). Teaching for quality learning at university . Maidenhead: Open University Press. Ramsay, P., Maier, P. and Price, G. (2010). Study skills for business and management students . Harlow: Longman.
Unless otherwise specified by your module leader, the University uses the Harvard (author-date) style of citing and referencing. For more guidance and support on how to reference effectively check out the Referencing Guide . You can also book an appointment with an Academic Engagement Librarian for extra help with referencing.
While the main results of your study should be placed in the body of your dissertation, any extra information can be placed in the appendices chapter. This supplementary information, for instance, can consist of graphs, charts, or tables that demonstrate less significant results or interview transcripts that would disrupt the flow of the main text if they were included within it.
You can create one long appendix section or divide it into smaller sections to make it easier to navigate. For example, you might want to have an appendix for images, an appendix for transcripts, and an appendix for graphs. Each appendix (each graph or chart, etc.) should have its own number and title. Further, the sources for all appendices should be acknowledged through referencing and listed in the bibliography.
Don’t forget to mention each appendix at least once during your dissertation! This can be done using brackets in the following way: (see appendix 1).
Discover the world's research
Intended for healthcare professionals
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.
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
Step 1: reading.
All manuscript authors read the data
All manuscript authors write summary memos
Coders perform both data management and early data analysis
Codes are complete thoughts or sentences, not categories
Researchers host a thematic analysis session and share different perspectives
Themes are complete thoughts or sentences, not categories
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
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.
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.
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
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.
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.
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.
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
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.
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 ).
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
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.
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.
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
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.
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.
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
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
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.
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 ).
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
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
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.
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.
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
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.
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.
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.
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.
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.
Review our examples before placing an order, learn how to draft academic papers, what is thematic analysis dissertation | explore examples and advantages.
A thematic analysis dissertation is a special kind of research project where you look closely at a specific theme or a group of related themes. You study the patterns in the information you gather and figure out if they have anything to do with the main research question.
If you want to get a better idea of how this works, you can check out a full example of a thematic analysis dissertation below.
An Insight into Alternative Dispute Resolution (ADR) and Its Execution to Solve Common Construction Disputes
Thematic analysis is an important tool for any student researching a particular topic. It is used to help identify the main themes in a research piece and enable the researcher to draw meaningful conclusions from their data.
Learn More About What Thematic Analysis is
This article will lay down an overview of what thematic analysis is, how it works, and its benefits, uses, and thematic analysis examples.
Main components of a thematic analysis dissertation, 1. introduction.
The introduction offers an overview of the research topic , outlines the primary research questions being asked, and lays out the structure of the dissertation. It also includes background information about previous studies related to the research topic. Additionally, it should discuss any potential limitations or challenges associated with conducting this type of research.
This section outlines the methodology used in conducting the thematic analysis dissertation and explains why this methodology was chosen over other methods. This section should also include an explanation of how data was collected (e.g., interviews, surveys, etc.) and any ethical considerations associated with collecting and analyzing data for this particular study.
In this section of the dissertation, you will analyze your collected data according to your research questions or hypotheses. Here you will discuss how you arrived at your conclusions based on your analysis of the data collected from participants or other sources (e.g., literature reviews). You should include examples from your analysis here if applicable.
You will summarize your findings from your thematic analysis dissertation and provide recommendations for further research on this topic. You may also discuss implications for practitioners in this field and any limitations identified during your study that could be addressed in future studies.
Finally, include all relevant references cited throughout your dissertation so readers can easily locate additional sources pertinent to their work or interests in this topic area.
Testimonials
This is our reason for working. We want to make all students happy, every day. Review us on Sitejabber
Here are three main steps to do a thematic analysis for a dissertation.
The first step in the thematic analysis is to become familiar with the data you are working with. Read through your text multiple times and note any ideas that stand out to you.
Create categories and subcategories to organize related thoughts and ideas as you read through. Doing this will help you identify patterns and connections within the text that will inform your analysis later in the process.
Once you understand your data set well, it’s time to start looking for themes or topics that repeatedly appear throughout the text.
To do this, look for words or phrases that appear multiple times throughout the text and group similar ideas under common themes. Be sure to take notes as you go so you can easily refer back to specific points later in your analysis.
Once you have identified all the themes in your data set , it's time to start analyzing them more deeply and thoroughly. Look at each theme individually and examine how they interrelate with one another and how they may contribute to larger concepts within your study's scope.
When analyzing each theme, ask yourself questions such as
Make sure that each theme is backed up by evidence from your data set so that your results are legitimate and accurate!
The following steps are essential to know how to write a thematic analysis dissertation.
The first step in writing any thesis or dissertation is conducting thorough research and collecting data. When it comes to a thematic analysis dissertation, this means collecting as much relevant data as possible.
Explore What are the Ways to Collect Data for Thematic Analysis
Use interviews, surveys, and group discussions to learn about your topic. Check if other sources are good and true before using them in your paper.
Once you have collected the necessary data for your thematic analysis dissertation, it's time to start identifying themes.
Learn What are Things Important to Analyze Themes
To analyze these themes further, you can use coding techniques such as content analysis or discourse analysis which can help you better understand the context of each piece of data in relation to the overall theme being studied.
Now that you have identified and analyzed potential themes within your data set, it’s time to craft a thesis statement and create an outline for your paper. Your thesis statement should succinctly explain the main points discussed throughout your paper while providing insight into why these topics are important.
Creating an outline helps organize all of the information into cohesive sections so that readers can easily follow along with your argument as they read through each section of your paper.
Doing a thematic analysis can be really helpful for students. Here's why:
Please fill the free topic form and share your requirements
The writer starts to find a topic for you (based on your requirements)
The writer shared custom topics with you within 24 hours
Thematic analysis has applications in many areas, including market research , customer experience management, product design and development and education research.
For example, market researchers can use thematic analysis to understand customer opinions about a product or service by analyzing responses from surveys or focus groups.
Educators can use thematic analysis to analyze student essays to understand student learning outcomes better and improve teaching strategies where needed.
It can also be used in software development projects to uncover user needs so that developers can create products that meet those needs more effectively.
A thematic analysis dissertation allows researchers to uncover patterns within their data that can help answer their primary research questions or hypotheses while providing meaningful insights into their subject matter. With an understanding of how thematic analysis works, students can take full advantage of this method when conducting their research studies. To review more thematic analysis dissertation examples, click here .
If you are looking for professional help in your thematic analysis dissertation, Contact Premier Dissertations. Our dissertation writing guides will make you write a 3 cm thick dissertation document in no time. Explore them below.
Your Number
Academic Level Select Academic Level Undergraduate Masters PhD
Area of Research
Comments are closed.
An official website of the United States government
The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.
The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.
The PMC website is updating on October 15, 2024. Learn More or Try it out now .
Childhood obesity is a major concern in today's society. Research suggests the inclusion of the views and understandings of a target group facilitates strategies that have better efficacy. The objective of this study was to explore the concepts and themes that make up children's understandings of the causes and consequences of obesity. Participants were selected from Reception (4–5 years old) and Year 6 (10–11 years old), and attended a school in an area of Sunderland, in North East England. Participants were separated according to age and gender, resulting in four focus groups, run across two sessions. A thematic analysis (Braun & Clarke, 2006) identified overarching themes evident across all groups, suggesting the key concepts that contribute to children's understandings of obesity are “Knowledge through Education,” “Role Models,” “Fat is Bad,” and “Mixed Messages.” The implications of these findings and considerations of the methodology are discussed in full.
The Health Survey for England 2009 illustrated that 65.9% of men and 56.9% of women have a body mass index (BMI) higher than 25 kg/m 2 , classing them as overweight, obese (>30 kg/m 2 ), or morbidly obese (>40 kg/m 2 ). Obesity is linked to many chronic illnesses, including type II diabetes, heart disease, and some cancers—specifically bowel and others within the digestive system (Renehan, Tyson, Egger, Heller, & Zwahlen, 2008 ). As a result, the direct cost to the National Health Service (NHS) of treating obesity was estimated to be between £991 and £1,124 million, for the 2001/2002 financial year (McCormick & Stone, 2007 ).
Childhood obesity is of particular concern because obese children are far more likely than children of a normal weight to become obese adults (Alexander & Sherman, 1991 ). The Health Survey for England 2009 showed that between 1995 and 2008, the percentage of overweight and obese girls rose from 25.5 to 29.2% and from 24.5 to 31.4% for boys. This is despite the fact that during the same period reported total energy intake in the United Kingdom (UK) fell by around 20% (Statistics on Obesity, Physical Activity and Diet England, 2006 ). These contradictory figures highlight the complexity of factors contributing to obesity, pointing to issues such as levels of physical activity, which have significantly fallen over the past two decades (Prentice & Jebb, 1995 ).
Many other factors influence incidences of obesity. The negative impact of childhood obesity causes the greatest concern and needs to be further understood. Obese children are more likely to become obese adults and experience increased health problems. Knowler, Pettitt, and Saad ( 1991 ), highlighted the links between childhood obesity and a poor immune system, risk of raised blood pressure, and cardiovascular problems. Studies have also identified that overweight and obese children are more likely to suffer psychological problems associated with low self-esteem, bullying, and social exclusion (Breat, Mervielde, & Vandereycken, 1997 ).
On an international scale, obesity can be seen as a problem of the developed world, a result of economic wealth, high food availability, and low levels of manual labour leading to lower levels of physical activity. This is in conjunction with high levels of car ownership and wide ranging public transport systems adding to the problem. In short, at the heart of obesity lies a homeostatic biological system that works constantly to maintain energy balance to keep the body at a constant weight. This system has not yet adapted to the world in which we currently live because the pace of technological progress has surpassed evolution resulting in a more sedentary lifestyle (Department of Innovation Universities and Skills, 2007). One surprising feature of the geographical distribution of obesity is its increased prevalence in economically and socially deprived areas in the western world, including the focus of this current piece of research, the United Kingdom. This phenomenon is very much a recent development, because historically deprived areas tended to see higher levels of under-nutrition. Brunt, Lester, Davies, and Williams ( 2008 ) illustrate how this situation has now reversed. They found between 1995 and 2005 the gap between obesity levels in the most deprived areas compared to the least (the latter typically having the higher levels) was steadily closing, and that by 2005 obesity levels in the most deprived areas had overtaken those in the least deprived areas, a phenomena that persists today.
The Childhood Measurement Programme (Department of Health and Department for Children, Schools and Families, 2008 ) demonstrated Sunderland in the north-east of England has some of the highest levels of overweight and obese children in the United Kingdom. This same publication also points out the strong positive correlation between areas considered as deprived and levels of obesity in children in Reception (4–5 year olds) and Year 6 (10–11 year olds). Areas of Sunderland are considered to be economically and socially deprived meaning the children who live there can be considered high risk. The statistics relating to Sunderland, where this study took place, demonstrate that 27.8% of Reception-aged children are either overweight or obese and for Year 6 pupils this rises to 38.4%.
The Foresight Report (Department of Innovation Universities and Skills, 2007), tackling obesity, points out that current policies are failing because they do not provide the depth and range of interventions needed. This might lead to positive interventions being ineffective if they are undermined by other areas in society such as social factors and the power of media advertising. The government launched its Healthy Schools Initiative in 2005; however, there has been no substantial reduction in obesity levels since 2005 (Department of Health and Department for Children, Schools and Families, 2008). With this in mind it would seem timely to approach the problem from a different perspective. Effective policies to tackle obesity need to consider all parties involved. However, current policies have been formed using a top down approach i.e., from government, health and education professionals, and even celebrity chefs! Even though these groups are likely to have a broad understanding of the problem from its roots to the long-term consequences, there has been a notable failure to take into consideration the understandings of the individuals at highest risk of obesity, the children themselves. There is growing evidence that interventions incorporating the views of the target population have a greater level of success (Hesketh, Water, Green, Salmon, & Williams, 2005). In the United Kingdom there has been a strong movement to ensure the inclusion of children in decision making particularly in relation to issues that directly affect them such as education, social care, and health (Department of Health, 2002 ; Department of Health and Department for Education and Skills, 2004 ). The collection and dissemination of the understandings of children relating to obesity could provide an insight into why so many strategies are failing. This in turn could lead to the development of policies that can be delivered to provide more successful outcomes.
There is a clear shortage of research examining children's understandings’ of obesity, the studies that have attempted to explore this domain have focused on exploring parent and care giver perceptions (Young-Hyman, Herman, Scott, & Schlundt, 1999), and the understandings of health professionals (Chamberlin, Sherman, Jain, Powers, & Whitaker, 2002 ). More recently studies have considered the understandings of care givers, health professionals, and teachers alongside those of the children themselves (Borra, Kelly, Shirreffs, Neville, & Geiger, 2003; Hesketh et al., 2005). Studies that have examined children's understanding have been focused on body image, overweight versus underweight (Hill & Silver, 1995 ), and peer perceptions of overweight and eating behaviour (Bell & Morgan, 2000 ; Oliver & Thelen, 1996 ), but not on the understandings’ of the children themselves with regards to the causes and consequences of obesity.
Focus groups have proved to be a particularly useful method for collecting data from children, they are most effective with groups of three children and in situations where the children know and like each other. Groups must be carefully selected to ensure the children are comfortable with each other. Talking together in small groups is familiar territory for children because it simulates class work. This method allows the researcher to structure the discussion around themes or topics rather than direct questions. This in turn enables the children to take control of the discussion (Mauthner, 1997 ) with the researcher present to keep things on track. Conducting group discussions in single sex groups can also prove to be more successful because boys are often louder and more willing to talk and this can mean they direct the topic of conversation. It has also been noted the use of some sort of structured activity such as drawing, reading, or sorting cards, can help focus discussion in particular with young children. When discussing diet with children, nutritionists and dieticians regularly use replica food items to help visualise the topic under discussion and photos depicting scenes of physical activity have proved effective in qualitative studies (Hesketh et al., 2005 ).
In summary the objective of this research is to investigate the understandings of a high risk group of children (high risk because of their socio-economic status so determined by their locality), of some of the causes and consequences of obesity, and its links to diet and physical activity. The concepts and themes generated by this research should be used to provide an insight that may inform local policies and interventions that need to be developed to provide a broader and deeper range of options to address this multi-faceted issue.
In order to address the gaps in current literature it was decided this research should focus on identifying themes within the participants understanding. This would provide the researcher with scope for further investigation of the subject in question. It was therefore decided that the most appropriate method of analysis would be a thematic analysis. However, there have been criticisms of this approach in the past due to the lack of clear guidelines for researchers employing such methods. This has subsequently contributed to some researchers omitting “how” they actually analysed their results (Attride-Stirling, 2001 ). It was of upmost importance to the authors in this current study to employ a clear, replicable, and transparent methodology.
Braun and Clarke ( 2006 ) outline a series of phases through which researchers must pass in order to produce a thematic analysis. This procedure allows a clear demarcation of thematic analysis, providing researchers with a well-defined explanation of what it is and how it is carried out whilst maintaining the “flexibility” tied to its epistemological position. The authors in this paper take a position that acknowledges our desire to incorporate the individual experiences of the participants and the meanings they attach to them. However, we also wish to consider the impact of the wider social context on these meanings. Braun and Clarke describe such a position as “contextualist,” sitting firmly between essentialism or realism and constuctionism. Not all theorists describe these two poles of epistemological outlook in the same way; Madill et al. ( 2000 ) refers to them as “naive realist” and “radical relativist.” Methodologies that go hand in hand with this mid-ground position are typically phenomenological in nature, but the flexibility of thematic analysis means that it can also be underpinned by an “in-between” epistemological position. Willig ( 2008 , p. 13) summarises this by explaining a position that argues “while experience is always the product of interpretation and, therefore, constructed (and flexible) … it is nevertheless ‘real’ to the person who is having the experience.” We wish to consider the reality of obesity to the participants, through an exploration of their experiences and the meanings they attach to them, whilst incorporating the broader role society plays in contributing to and shaping the participants meaning making and subsequent understandings.
Twelve participants were selected through liaising with the school and class teachers, this was particularly important considering the sensitive nature of the research topic and the fact that the participants taking part in this study were children—a vulnerable group. Measures were taken to prevent any of the participants feeling stigmatised. Therefore, under the guidance of the class teachers, the participants approached to take part in the study were carefully selected to ensure no children who may have been made to feel uncomfortable by the discussion were included, and to make sure that the children selected to be in the same focus groups were comfortable with each other. Six (three boys and three girls) were selected from two school years; Reception, aged between 4 and 5 years and Year 6 aged between 10 and 11. The motivation for selecting these age groups was that government statistics relating to childhood obesity are published for these two age brackets. These age groups are viewed as critical points in measuring children's BMI and in monitoring their changing health status. Through looking at these age groups, it may help us to gain an insight into what understandings children arrive at school with (primarily shaped by their experiences set within a home environment) and those that they have later on in their school life when further social influence (school and peers) may play a role in shaping their understandings. Efforts were made to make the sample representative of ethnicities attending the school so a proportionate number of children of Bangladeshi and Afro-Caribbean heritage took part. Participants were not recruited on account of their BMI or weight status. The parents of the children were provided with a study information letter and, in addition, received a phone call from the school's community liaison officer to ensure that parents fully understood the nature of the study because the researcher was aware that for some parents English was not their first language. The phone calls were made in their mother tongue thus allowing the parents to sign the parental assent form with all their queries being answered. Participants were also asked for their verbal consent on the day prior to the study taking place.
The study had received ethical approval from Northumbria University's School of Psychology and Sports Science Ethics Board prior to commencing. The researcher had also been approved by means of an enhanced criminal records background check clearing her to work with children; this approval was required by both the school and the university.
The focus groups all took place in the same quiet room at the school and were conducted by the principal investigator (referred to herein as the researcher). On arrival, the researcher introduced herself and provided name badges for the participants. The researcher briefly explained to the participants that she was there to talk to them about food and exercise. The researcher also explained to the participants that she wanted them to assume that she knew nothing, they were not being tested, and she was only interested in hearing what they had to say—not whether they were right or wrong. Verbal instructions were provided to the participants and they provided verbal assent prior to the recording commencing. A series of questions were developed by the research team, these were designed to keep the focus group sessions on track whilst exploring issues relevant to the research question. The sessions started initially with a discussion centred on the replica food items laid out on the table. Participants were asked to use the replica food and pick out healthy foods and make what they thought would be a healthy lunch. They were asked to explain why it was healthy and what made it healthy. Participants were then asked about foods they liked and why they liked them. In addition, they were asked about the sorts of things they normally ate at home and in school and things they liked to eat. Once conversation had dwindled concerning the replica food the researcher introduced the laminated picture cards, and the discussion moved to physical activity with the researcher encouraging the participants to explore the relationship between diet and exercise. Questions focussed on what activities they thought were healthy (as the images depicted activities that were both physical and sedentary; that is, one image of somebody running another of somebody playing computer games). The participants were asked about what sorts of activities they liked doing and what made those activities good for them. They were asked what activities they regularly engaged with, the sorts of sports their parents and siblings took part in, and the activities they did as families. The themes of discussion were encouraged around the two elements pertinent to any strategy looking to reduce obesity: healthy eating and physical activity. Furthermore, questions also probed at what the participants thought the benefits were of following a healthy lifestyle and what the consequences were of not following one. They were also asked what advice they would give somebody who wanted to be healthier and how important it was to them to be healthy. The focus group guide was intended to provide a structure but not rigidly dictate the line of questioning. The researcher included prompts and encouraged participants to expand on their initial responses and followed up on notions that the participants raised themselves. The sessions on the first day lasted between 20 and 30 min, ending when the participants input was insufficient to continue. At the end of each session the researcher read out the participant debrief and provided each participant with a parental debrief information sheet to take home.
In order to strengthen the analysis process and gather the most appropriate data, the researchers reviewed the recording made on the first day and reflected on the procedures employed in the focus groups. Similar approaches of reviewing data to informing further data collection are used in methods such as grounded theory and it was felt that doing so would strengthen the current study. The decision was made not to use the props (replica food and cards) used on the first day in the second round of focus groups, as at times they had proved to be a distraction to the participants. As an alternative, Reception children were given colouring pens and paper to focus their attention. Year 6 focus groups were run again allowing for free discussion, following on from issues and understandings they had raised in the initial session. The second round of focus groups, other than the changes already detailed above, followed the same sequence as they had on day one and lasted around 30 min. The recordings were transcribed combining the recordings from both days creating four transcripts, one for each group.
The data collected from all the focus groups was transcribed by the principal investigator, during this process the initial thoughts and ideas were noted down as this is considered an essential stage in analysis (Riessman, 1993 ). The transcribed data was then read and re-read several times and, in addition, the recordings were listened to several times to ensure the accuracy of the transcription. This process of “repeated reading” (Braun & Clarke, 2006 ) and the use of the recordings to listen to the data, results in data immersion and refers to the researcher's closeness with the data. Following on from this initial stage and building on the notes and ideas generated through transcription and data immersion is the coding phase. These codes identified features of the data that the researcher considered pertinent to the research question. Furthermore, as is intrinsic to the method, the whole data set was given equal attention so that full consideration could be given to repeated patterns within the data. The third stage involved searching for themes; these explained larger sections of the data by combining different codes that may have been very similar or may have been considered the same aspect within the data. All initial codes relevant to the research question were incorporated into a theme. Braun and Clarke (2006) also suggest the development of thematic maps to aid the generation of themes. These helped the researchers to visualise and consider the links and relationships between themes. At this point any themes that did not have enough data to support them or were too diverse were discarded. This refinement of the themes took place on two levels, primarily with the coded data ensuring they formed a coherent pattern, secondly once a coherent pattern was formed the themes were considered in relation to the data set as a whole. This ensured the themes accurately reflected what was evident in the data set as a whole (Braun & Clarke, 2006 ). Further coding also took place at this stage to ensure no codes had been missed in the earlier stages. Once a clear idea of the various themes and how they fitted together emerged, analysis moved to phase five. This involves defining and naming the themes, each theme needs to be clearly defined and accompanied by a detailed analysis. Considerations were made not only of the story told within individual themes but how these related to the overall story that was evident within the data. In addition, it was highly important to develop short but punchy names that conveyed an immediate indication of the essence of the theme. The final stage or the report production involved choosing examples of transcript to illustrate elements of the themes. These extracts clearly identified issues within the theme and presented a lucid example of the point being made.
The thematic analysis process that was applied to the transcripts elicited key concepts that were evident in the data. These themes are viewed as essential in determining the understandings of all the participants. These categories have been labelled as “Knowledge through Education,” “Role Models,” “Fat is Bad,” and “Mixed Messages.” There are of course aspects of the participants’ understandings that overlap across these categories. This, however, should be viewed as a good interpretation of understandings and attitudes in general, which are never made up of isolated concepts but are all relative to each other.
This theme is defined by the ability of all the participants to understand the roles of diet and physical activity. This is, in part, likely to be defined by different levels of education that the two age groups represented have, but nothing conclusive can be drawn given the relatively small sample size. The impact of their education on their knowledge will be demonstrated through evidence from the transcript.
All participants in the reception age group expressed the ability to name and identify different food items from the replica food. When they were asked to prepare a healthy lunch from the food items, they were able to point out food that would typically be classified as healthy.
I: No none of it is real! So what have you put in your healthy lunches girls? You tell me what you have got. *: Apple, I've got pasta, egg, cracker, grapes, bun and cheese. Girls reception Open in a separate window
However, despite displaying that they “know” what healthy means there is evidence of confusion, and it would seem the concept of something being “good” for them is interpreted to be things they like to eat. This suggests that they don't yet fully understand the concept of “healthy” food.
I: And why's rice healthy? *: Because it's nice. I: What healthy food do you eat? *: Chips Boys reception Open in a separate window
Their definition of healthy is centred on food they believe will make them grow for which fruit is highlighted as being particularly important. However, they also attribute this property to the food that makes up their personal diets. This understanding might result from being told to eat so they grow up to be big and strong. It is important to consider younger children's understandings are likely to be primarily shaped by their home environment, where the emphasis is often on how much children are eating as opposed to what they are eating.
I: Why is a banana important? *: Because it makes you strong so you can grow you have to have fruit so you can grow. I: Can you tell me then girls, we have found all these things that are good, as an example can you tell me, sausage, why is sausage good? *: Because it makes you feel strong. Girls reception Open in a separate window
This understanding of the reception-aged girls represented in this study of eating so they can grow up to be strong is also evident with the boys in the same age group. However, the reception boys also place great importance on the necessity of exercise to develop strength, this demonstrates another aspect in their knowledge.
I: What about this one here, swimming, who likes swimming? *: Me *: Me *: Me I: And why is swimming good for you? *: Cos it makes you strong. Boys reception Open in a separate window
It is fair to say Year 6 groups relished the opportunity to express their knowledge. They were able to identify and name different food groups and discuss different types of physical activity; what's more they understand the link between the two in relation to obesity. It seems other influences have impacted on the children's understandings’ such as school and extracurricular groups.
*: This is a banana. I: Ok why's a banana healthy? *: Because it's got seeds inside, because it's a fruit. Girls year 6 Open in a separate window
The ability to identify a particular fruit by one of its universal characteristics shows a deeper level of understanding and suggests that a higher degree of learning. In fact it is explicitly stated that this nutritional knowledge has been gained at school.
I: So do you know the different groups of food like carbohydrates, I heard you say protein and dairy before? *: Done it in science. Girls year 6 Open in a separate window
Moreover, it isn't just a nutritional knowledge they have developed through education. They appear well versed in the concept of a balanced diet and also understand the importance of a balanced lifestyle in relation to physical activity. They are able to articulate the notion of a balanced, healthy lifestyle through a consideration of the consequences of over eating and not exercising.
I: So what happens to you if all you do is you do watch TV and play the computer, eat the food that you told me was the bad food, what would happen to you? *: You would have a miserable life. *: Get fat, teeth will fall out. Girls year 6 Open in a separate window
In the case of the Year 6 boys who took part in this study, it is apparent that although a great deal of their knowledge has come through education at school, other avenues have helped them develop different aspects of their understandings. In this case it seems to be through taking part in activities, typically sport outside of school, or and more uniquely to this group through the influence of their fathers.
*: I would say my dad likes fish so I eat fish loads. *: My dad likes chicken, so he gives me chicken cos after school I do sport, like boxing, he gives me a sandwich with loads of different toppings in cos meats a muscle maker and vegetables is like an energy maker, so if you eat those you will get fitter and healthier. Boys year 6 Open in a separate window
It is evident where the ability exists, or is encouraged, to apply knowledge they have in a context relevant to their own lives, the knowledge becomes embedded in their understandings; it is applicable to them and, therefore, moves from being written on the board in school to being important to their own existence. This is exhibited by those participants, in particular the boys who participated, who have an involvement in sport. Having a motivation to understand nutrition and exercise leads to a desire to apply it because they comprehend the potential benefits. This aspect within the initial theme of knowledge through education leads directly on to the next theme of role models. The key difference between these two themes is the first relates to information that is directly and intentionally meant to inform the children about healthy lifestyles in an institutional setting, while the second theme is typified by understandings that are formed through interactions with other people.
The application of knowledge gained through education is often facilitated by role models such as family members who reiterate this information through example. Role models play an important role in the concepts described by all the groups, for example, the older boys reported that their fathers helped encourage healthy behaviours, above and beyond the nutritional knowledge in the previous theme.
*: Like sometimes on an afternoon my dad goes to the gym, then there is these tracks outside, and I practice every day on my 100 meter sprint and I can do it in 12 seconds, and when I started doing it I was 21 second, so I keep practicing. Boys year 6 Open in a separate window
This demonstrates some of the participants’ understandings have developed by examples set for them by significant individuals in their lives. This is evident in the younger children's understandings in a less explicit manner; the example below demonstrates good health behaviours can be established through everyday behaviour exhibited by role models.
I: What about this one, walking to school? … Why is it good for you? *: Because me and my mam walk to school and its good. Girls reception Open in a separate window
There is some evidence that examples set to the girls who took part in this study, at home and by other role models, can encourage behaviours or ideals that are not beneficial to the girls health. Girls appear to look up to older female family members who aspire to be skinny.
*: I like to be skinny, my nana does as well, and she wants to be skinny because she's fat now but I still love her. Girls reception Open in a separate window
They also appear to have developed unrealistic ideas about weight loss and the consequences in terms of treatment. Viewing hospital treatment as a solution to obesity, demonstrates a lack of understanding about the role of lifestyle behaviours in the condition. This may also suggest that these participants don't appreciate the importance of lifestyle behaviours in the onset of obesity.
*: Guess what, I seen this film right the boy was fat right, his legs was right down to the bottom, he had a fat tummy, I was hiding cos I hated him, he was horrible, he will have to go to hospital, he was fat. Girls reception I: So what would you tell somebody if you pretend that I was really, really fat, what would you tell me to do. *: Go to the doctors … hospital, operation. Girls reception Open in a separate window
There was some evidence that the older girls in this study had a more balanced outlook on what sort of body shape was healthiest, because they were aware of the negative health consequences associated with being underweight. It is interesting, however, that they are aware that maintaining a healthy lifestyle may be a challenge and this may result in a barrier to adopting healthier practices.
I: What about the other end of the scale, you know if you've got overweight being fat on this side what about being underweight at this end? *: It's bad cos you're all bony and you can't do anything cos you're not strong enough, you're weak. *: So you need to be in the middle. I: Is it easy to stay in the middle? *: No, because sometimes you can't be bothered to eat well and exercise. Girls year 6 Open in a separate window
Within the theme of role models, there was some evidence of a difference between the genders in terms of available role models. The participating boys often cited football heroes as people whom they looked up to and aspired to be like. This highlights the role of the celebrity in providing a role model for today's children; the evidence from the participants in this study may suggest that typically boys look to footballers and other sporting heroes. It can be argued that such individuals do not always provide a strong moral code; they are seen as following a healthy lifestyle in terms of diet and exercise. It would seem that the female participants in this study often looked up to celebrities who weren't so explicitly seen to be following healthy lifestyles, or a sense of caution was attached to following healthier behaviours.
*: Yeah like Wayne Rooney. I: And why is he fit? *: Cos he's good at footballing. I: Do you think that they have to eat special food? *: Yes I: And what special food do they have to eat? *: Bananas and apples. Boys reception *: Actually you can put weight on running cos muscle weighs more than fat so you can put weight on—like Katie Price she put on 10 pounds cos she started running. Girls year 6 Open in a separate window
Another interesting aspect of the notion of role models’ is that the girls were more concerned with how they appeared in a physical sense; it was particularly striking that the Year 6 boys identified unhealthy behaviour in their female peers attributing this to a desire to be like models.
*: Yes, she wants to be a model so she starves herself, her mam gives her a big packed lunch and she puts most of it in the bin, she's like that skinny then she walks out of the dinner hall. Boys year 6 Open in a separate window
There were many aspects of the transcript that highlighted participants were aware that being underweight was as worrying as being overweight. However, across the board they were far more critical of individuals who were overweight and discussed wide ranging consequences for these individuals, this leads on to the next theme evident in the analysis.
There was a united consensus that being fat was something to avoid, that it was a bad thing, and had typically negative consequences. Elements of this theme have been demonstrated throughout the discussion of the previous two themes; however, this illustrates how their understanding impacts on their attitudes toward obesity.
*: Like all the fat goes through your blood and stuff. *: Like sugar, like all the sugar goes through your blood if you eat too much of it would clog up your arteries and you might die. Boys year 6 I: Like how? What would happen to you? Is something going to happen straight away or is it something that's going to happen to. *: You would get rotten teeth and you would not be as strong as you would be if you ate healthy and stuff. *: You could die. Girls year 6 *: Because fat would be horrible. *: Because it's bad for you, because it looks bad. *: Because people call you big fat. Girls reception Open in a separate window
In addition to the health issues and those relating to physical attractiveness were the issues of bullying and social exclusion, which seemed to play a big role in the children's understandings of what it would be like to be overweight. The stigma attached to being overweight is evident as participants often started giggling when talking about people being overweight.
I: Is it important to eat things that are good for you? *: Laughter I: What do you think happens to you if you eat lots of these biscuits? *: Fat I: And what good would stop you from getting fat, or would help you not be fat? *: Giggling Boys reception Open in a separate window
Inability to have a successful career and even death were understood to be the results of obesity. Participants felt people who were overweight were in some way bad or an embarrassment. There was even a sense of fear toward people who they considered overweight, indicating that they would avoid being seen with somebody who was obese.
I: So … so what do you think about being fat, like if you see somebody in the street who looks like they are not very healthy do you think? *: They can't do much, like most of the things you want to do in life, like swimming, jogging. *: Jobs when you grow older. Girls year 6 *: Like if my parents were proper massive and I went to the town with them I would just say they took me to the town and I don't know them. Boys year 6 Open in a separate window
It is clear that the participants’ understanding is that obesity is a very negative issue. However, there is also evidence that they understand the complexity of the condition and are also aware being underweight maybe as much of a problem. The older children in this study seemed to understand that it is a complex issue and fully grasped the concept of moderation. They often refer to the fact that you can have a small amount of things that maybe classified as unhealthy, as long as you don't eat them all the time or balance them out with exercise.
I: And what sort of things for eating well? *: Like fruit and vegetables. *: Some Sugar. *: If you eat vegetables and fruit and you might get back to underweight. *: And you want to be in the middle. *: You need a bit of fat on you. Girls year 6 Open in a separate window
This category of Fat is Bad highlights an issue that clouds all the children's understandings of issues surrounding obesity and that is of conflicting messages. This notion of mixed messages forms the final theme evident in the data.
The evidence presented here would suggest the information intended to educate and inform children is often met with equal amounts of contradictory or confusing messages and behaviours. The result of this is easily demonstrated by comparing what the children know they should be doing with what they actually talk about doing. For the majority of the participants their knowledge did not always match with their described behaviour, their food preferences often overriding their knowledge. This was perhaps not so surprising; knowledge does not by any means dictate behaviour.
I: Do you have breakfast most mornings? Do you normally have some breakfast, what do you normally have for breakfast? *: Miss I have chocolate cookies. I: What did you have for your tea last night? *: I just had for my supper. I: What did you have last night for your supper? *: Err sandwiches, cake and I: What about what did you have last night for your tea? *: Pizza Girls reception I: You eat two, two pieces of fruit? *: Yes, cos my mam chops it into two halves. Boys reception Open in a separate window
Conflict existed in a number of forms in the understandings expressed by the participants. It is worth reiterating that the younger girls who participated believed treatment for obesity was to go to the hospital and have an operation—something they have picked up from a TV documentary—this conflicts with diet and exercise education they receive at school. Other participants gave more specific and direct examples of receiving contradictory information. This ranged from conflicts in direct health messages to conflicting information and action between school and home. They felt that at times it was difficult to know which information was the right information, not only was it conflicting but it was forever changing.
*: And people say if you make fruit smoothies its healthy for you but it said in the news something about being obese again it said that if you drink a smoothie one a day you'll put on 13 pounds, that's nearly a stone in a year. Boys year 6 I: What about at home? You know if you're taught all this stuff at school what happens when you go home? Do Mum and Dad teach you the same things or is it different? *: Different I: And why is it different? *: I eat more sweets. Girls year 6 Open in a separate window
In addition to this, older children also pointed out they felt that healthy lifestyle information wasn't always delivered in the correct manner, there was a belief that stigmatising people who were overweight was negative. There was an awareness that there is a psychological aspect to overeating, and in some individuals it is this that needs to be addressed. Moreover, there was a feeling again demonstrated solely by the older participants that being overweight/obese could be difficult to rectify and maintaining a healthy weight could be a challenge.
*: So you need to be in the middle. I: Is it easy to stay in the middle? *: No, because sometimes you can't be bothered to eat well and exercise. Girls year 6 I: Do you think it's quite easy to lose weight? *: Yes *: Well for some people. *: If you put your mind to it, it is. I: No go on cos everyone's got different ideas. *: You can't just lose weight quickly. *: Cos my dad when he was young he was obese so he told me, but he's sort of addicted really. *: Addicted to what. *: Addicted he cannot stop but he's trying. *: He cannot stop what. *: Eating when he was young, he like learnt now he's saying to me about being fit cos he tells me about what happened when he was young so I try it. Boys year 6 Open in a separate window
This understanding of the complex nature of the obesity problem, coupled with the confusion and conflict in both the information and behaviours the participants are exposed to, can help explain some of the barriers to individuals adopting a healthier lifestyle.
The results detailed above highlight some important findings as to how children understand obesity in terms of some of its causes and consequences. It was particularly clear that knowledge, often imparted in a school setting, is getting through to the children who participated in this study. However, it appears equally evident that this knowledge in many cases does not transfer to behaviour. Further examination of the results allows us to explore the potential reasons behind the knowledge-behaviour gap.
Role models by their nature provide examples for both the children's beliefs and their behaviour. There are a wide variety of potential role models for children from parents, teachers, peers, and celebrities. What seems particularly important, in terms of being a positive role model with regards to healthy lifestyles, is that children have an opportunity to view the process of being healthy. In this study, this was typified by the examples of the Year 6 boys who participated in sport with their fathers. It appears this close and active relationship allows the knowledge that has been started at school to grow. Allowing children the opportunity to apply their knowledge and see the steps taken by a role model to get or stay fit help translate this knowledge into behaviour. What is interesting, however, is that it seems passive behaviours by role models can have the same impact. It was the case with these participants that the effect of passive knowledge transfer seemed to be more negative, but that is by no means to say that passive behaviours by role models will not also encourage positive lifestyle behaviours in other cases. The most obvious example of this within this data set was the seemingly implicit messages that the girls received about being skinny. There was not an overtly explicit attempt on the behalf of the role models described here to encourage a “skinny” ideal; however, messages seemed to reach the participants that would indicate this is the case. The key difference between these active and passive role models appears to come from whether the role models place focus on the process; taking part in sport (in the example of the older boys) or outcome being skinny (in the example of the girls). Focus on the action of being physically active or enjoying a healthy diet in the case of these participants produces a healthier outlook on maintaining a healthy body weight. When that focus is on the outcome—the weight loss or the weight gain—there seems to be less concern for actually “being healthy” in terms of body weight and lifestyle. This notion about process and outcome is intrinsically linked to the theme of Fat is Bad.
It is interesting to note that whilst the children expressed an understanding of fat as a component of diet and were able to identify high fat foods and their link to obesity, the focus was on fat as an outcome and not so much about it as input. It is a well-documented fact that fat is a requirement of a balanced diet. The participants were able to recite in great detail the consequences of becoming fat but were not so forthright about the processes involved in becoming fat. It can be suggested that by focussing on the process of becoming fat and understanding the need for fat in moderation and being physically active it may help to discourage fat becoming the output. This may also help to draw away the focus from physical appearance that is so closely tied to the stigma attached to being overweight and place it on living a healthy lifestyle and being healthy.
The key finding of this study is that it is evident that children receive contradictory messages when it comes to following a healthy diet and taking part in exercise. The research presented here highlights children's understandings of some of the causes of obesity and the consequences of becoming overweight. However, it is equally evident that this information has reached them on a knowledge level but has not or cannot be fully translated into behaviour. It appears that central to this problem are the multiple discourses that exist around diet and exercise. Whilst government campaigns may impart facts and figures and provide advice on changes that can be made, there are a whole host of other sources to contend with. There is an undoubted role played by the media both in terms of active advertising campaigns for junk food or sedentary games and the passive portrayal of unattainable body shapes and sizes in magazines and by celebrity culture. However, more than this, health messages are competing against a variety of cultural values, social, and personal norms that may well go against messages that encourage certain behaviours. What is more is that ultimately individuals have the power and autonomy to make their own choices about diet and exercise. Stakeholders need to ensure that people are in a position to make an informed decision and not one where their judgement is clouded by an array of contradicting messages. There is also a responsibility to ensure that individuals are able to act on advice given and to provide advice that is relevant and tailored to individual circumstances. It is easy to understand why parents on a low income may struggle to incorporate “5 a day” into their families diets when they perhaps don't have access to a car and the nearest shop selling fresh fruit and vegetables is several miles away. Ensuring people know that frozen fruits and vegetables are just as good and, in some cases better, is a far more useful and usable message.
The objective of this study was to explore children's understandings of obesity in terms of diet and physical activity; the children included were considered high risk because of their socio-economic status. To meet this objective, focus group data was analysed using thematic analysis. This analysis produced key themes pertaining to the understandings of the participants. There is not a wealth of prior research in this domain and it was for this reason thematic analysis was chosen to analyse the data. The method proved to be particularly useful in generating these exploratory data that are discussed here in relation to previous findings.
The theme of knowledge has previously been identified by Hesketh et al. ( 2005 ) in terms of information and awareness that is pertinent to children's perceptions of healthy eating, activity, and preventing obesity. Increasing knowledge relating to diet and physical activity cannot prevent obesity but it can encourage children to make informed choices.
This study, as have others (Hesketh et al., 2005 ; Borra et al., 2003 ; Musaiger, Mater, Alekri, & Mahdi, 1991 ), identified misunderstandings in children's knowledge as barriers to healthful behaviour. It might be useful to address this issue, particularly with younger children who are developing their knowledge. Previous literature has identified young children often consume their recommended daily intake of fruit but fall well short when it comes to vegetables (Dennison, Rockwell, & Baker, 1998 ). Government campaigns encourage people to eat five portions of fruit and vegetables a day ( www.5aday.nhs.co.uk ); however, nutritionists would encourage three portions of vegetables and two of fruit—fruit having high sugar content. There was no evidence in the transcripts that any of the children were aware of or understood this distinction. This needs further investigation; however, education should encourage an understanding of fruit and vegetables as separate entities to help increase the consumption of vegetables (Gibson, Wardle, & Watts, 1998 ).
The evidence in this study suggests children grasp the causes of obesity, overeating, and low levels of physical activity; however, there was a general lack of understanding of the underlying physiological processes. There was a limited understanding of the concept of energy balance or that there might also be medical reasons for the obesity. Bell and Morgan ( 2000 ) demonstrated providing medical explanations for obesity can have a positive effect on children's attitudes to obese individuals. Overweight individuals were generally stigmatised by the participants in this study, so providing better medical information could help to alleviate these negative attitudes. It is fair to say those children who did have more in-depth knowledge of obesity were more sympathetic in their considerations of overweight individuals acknowledging the difficulty in making lifestyle changes.
The influence of parents concerning diet and exercise behaviours is well documented (Prout, 1996 ). Hesketh et al. (2005), Borra et al. (2003), and Young-Hyman et al. ( 2000 ) consider parental influence to be a determining factor in children's attitudes and understandings of obesity. It is clear this influence can be as detrimental as it can be beneficial. Previous research (Borra et al., 2003 ) argues interventions need to be developed that consider the role of the parent. Children cannot be expected to apply the information they receive at school to themselves if it is not reiterated at home. Nutritional education and physical education have not formed a core or extensive part of school curriculums in the United Kingdom in previous years, and there is now a generation of young parents who do not have the skills to attractively present appropriate foods (Tuttle & Truswell, 2002 ) or who regularly take part in sport themselves. The impact of this on their children's behaviour is that they don't always have examples of healthy behaviour to model their own on.
Of particular importance was the finding that children feel that they often receive mixed and contradicting messages. This is of great relevance when considering the development of policies and strategies that can be more effective. More over this backs up the findings of Dorey and McCool ( 2009 ) who conclude that nutritional messages evident in health promotion and advertising were often perceived by child audiences to be ambiguous. The authors warn that these contradictory messages could potentially serve to weaken the trustworthiness viewers have in health promotion initiatives. This really points to a key area in which health professionals can target efforts to tackle obesity. Clarity and consistency in healthy messages and recommendations are central to helping people take on board and act on the information they receive. Contradiction allows room for people to question the advice given and when effort is required to make a change in behaviour that change is less likely to be made if there is reason to doubt the accuracy of information. Furthermore, coherent messages need to consider person specific factors that may inhibit behaviour change; when individuals are encouraged to behave in a certain way but the constraints of day-to-day life lead to another, the results are confusion and hostility to the initial message (Owens & Driffill, 2008 ).
The main methodological issue arising was participants from Reception struggled to engage fully in conversation, and the sessions followed a structure more a kin to an interview (i.e., question and answer). It was difficult to encourage responses that were longer than a few words; often one word responses were given. There is the potential to gain some very useful information from children in this age group; however, it can be a long and time-consuming process to elicit enough information to make the analysis process worthwhile. The length of the sessions also must be kept relatively short because attention spans are not long lasting; this was a finding similar to that of Miller ( 2000 ). The replica food items selected to help provide structure to the focus groups were useful and did provide a catalyst for discussion; however, for very young children (i.e., those in Reception) they resemble toys too closely, this then leads to them becoming more of a distraction, hindering the discussion. The use of the picture cards and pens and paper as suggested by Backett and Alexander ( 1991 ) provided a more a suitable means of structuring focus groups for young children.
There were at times issues with certain members of the groups making themselves heard more than others, thus the researcher had to encourage those happier to sit back and let others take the lead (Kirk, 2007 ). However, through a little encouragement all participants appeared comfortable talking with each other and participated equally, a result of the careful selection process. It also appeared to be beneficial speaking to boys and girls separately, with the boys often more excitable in their discussion style in comparison to the girls. It also facilitated the identification of some important issues, for example, the Year 6 boys identified eating behaviours present in the Year 6 girls that the girls themselves did not discuss.
The Foresight Report (Department of Innovation Universities and Skills, 2007 ), in tackling obesity, points out that current policies are failing because they do not provide the depth and range of interventions needed. This present study has determined that central to children's understandings of the causes and consequences of obesity are the concepts of knowledge, the opportunity to apply this knowledge to their own lives, and the existence of role models to set an example. There exist certain myths and misconceptions that need to be addressed and children need to believe they can trust the health messages they receive because they are aware some messages are misleading or forever changing.
The key to this issue seems to be children learn by example, they can have all the knowledge in the world provided to them through an institution such as a school but this information needs to be supported by life at home. This provides evidence that campaigns need to target parents to tackle childhood obesity; this is an issue that policy makers are already aware of ( National Institute for Health and Clinical Excellence, 2006 ). However, this means health messages delivered to the general public need to be clearer and avoid ambiguity. There needs to be careful considerations of the context in which health messages are received, taking into account the understandings of the target population (Hesketh et al., 2005).
There were some issues raised in the focus group that were beyond the scope of this particular study. There was a representation of different ethnic minorities in the groups, and slight differences in the understandings of these different groups were identified. Further research should investigate the understandings of different minority groups to see if ethnicity influences or results in divergent concepts. Future study also needs to look at strategies that enable children to apply healthy lifestyle information to their own lives.
Children spend, on average, a quarter of their waking lives in schools; therefore, schools can be seen as an effective environment and source to help encourage healthy lifestyles. However, that leaves three quarters of a child's time in which they are out of the control of the school environment. Strategies must be developed to unite the teaching at school with practices in the home. This supports the conclusions of Hughes, Sherman, and Whitaker ( 2010 ) who write that strategies need to be framed in a manner that makes low income mothers feel more supported in addressing issues their children may have with their weight. Ensuring that approaches to encourage healthy lives take on a holistic format will also help to provide consistent and realistic role models. There needs to be a concerted effort from within society to develop role models who have a healthy relationship with food and exercise. These seem to already exist for young boys in the form of sporting heroes but seem in short supply for young girls who already consider that being healthy is the ideal but then look to surgery as a form of weight loss. Lieberman, Gauvin, Bukowski, and White ( 2001 ) highlight the importance of role models and peer influence in the onset of disordered eating in young girls and this needs to be seriously taken into account when sending out messages that being overweight is bad, girls need to be aware that being underweight also has severe health consequences.
In conclusion, the time children spend eating and taking part in physical activity out of school is likely to be the biggest challenge to preventing the continuing obesity problems in the United Kingdom, and this is where current strategies appear to be failing. Children understand obesity and its contributing factors in terms set out to them by those people they consider role models. It is only by helping these role models to provide consistent and reliable information by setting suitable active examples and by being aware of the impact of their passive actions that we can begin to address the problem of obesity.
The authors would like to thank Sunderland Children's Centres and Back on the Map for their support in facilitating this research.
The author have not received any funding or benefits from industry or elsewhere to conduct this study
IMAGES
VIDEO
COMMENTS
2 outlines the methodology employed in the research, including sample sizes and recording practices. Section 3 is the bulk of the report and outlines the results of a thematic analysis of the thirteen focus groups, dividing the text into thirteen separate over-arching themes. Section
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 ...
A THEMATIC ANALYSIS OF THE EXCEL PRE-COLLEGIATE PROGRAM AS AN AVENUE OF SUCCESSFUL POSTSECONDARY ENROLLMENT FOR LATINA/O STUDENTS College access and college enrollment rates are significantly lower for students of color, students from lower socioeconomic backgrounds, and first-generation students (Reese, 2008).
Thematic analysis is a research method used to identify and interpret patterns or themes in a data set; it often leads to new insights and understanding (Boyatzis, 1998; Elliott, 2018; Thomas, 2006).However, it is critical that researchers avoid letting their own preconceptions interfere with the identification of key themes (Morse & Mitcham, 2002; Patton, 2015).
The following sections are by Suzy Anderson, from her UWE Counselling Psychology Professional Doctorate thesis - The Problem with Picking: Permittance, Escape and Shame in Problematic Skin Picking. An example of a description of the thematic analysis process: Coding and analysis were guided by Braun and Clarke's (2006, 2013) guidelines for ...
Thematic Analysis is a qualitative research method that involves identifying, analyzing, and interpreting recurring themes or patterns in data. It aims to uncover underlying meanings, ideas, and concepts within the dataset, providing insights into participants' perspectives and experiences.
Generating themes. Reviewing themes. Defining and naming themes. Creating the report. It is important to note that even though the six steps are listed in sequence, thematic analysis is not necessarily a linear process that advances forward in a one-way, predictable fashion from step one through step six.
There are various approaches to conducting thematic analysis, but the most common form follows a six-step process: Familiarisation. Coding. Generating themes. Reviewing themes. Defining and naming themes. Writing up. This process was originally developed for psychology research by Virginia Braun and Victoria Clarke.
When undertaking thematic analysis, you'll make use of codes. A code is a label assigned to a piece of text, and the aim of using a code is to identify and summarise important concepts within a set of data, such as an interview transcript. For example, if you had the sentence, "My rabbit ate my shoes", you could use the codes "rabbit ...
Thematic analysis is a qualitative research method used to identify, analyze, and interpret patterns of shared meaning (themes) within a given data set, which can be in the form of interviews, focus group discussions, surveys, or other textual data. ... Questioning data saturation as a useful concept for thematic analysis and sample-size ...
Usually the end-point of research is some kind of report, often a journal article or dissertation. Table 4 includes a range of examples of articles, broadly in the area of learning and teaching, that we feel do a good job of reporting a thematic analysis. Table 4: Some examples of articles reporting thematic analysis. AISHE-J Volume 8, Number 3 ...
Semi-structured interviews were conducted with a sample of 10 participants between 18-25 years of age as they were deemed as young adults. Thematic analysis (TA) was used to analysis the transcripts. The analysis revealed three main themes; 'Temptation', 'Stay Away' and 'What Would Others Think?'. ... Prof Doc Thesis. Rajmangal, T ...
Learn how to use thematic analysis in qualitative research with this easy-to-follow explainer. In this video, we unpack thematic analysis for new researchers...
You may move forward and back between them, perhaps many times, particularly if dealing with a lot of complex data. Step 1: Become familiar with the data, Step 2: Generate initial codes, Step 3: Search for themes, Step 4: Review themes, Step 5: Define themes, Step 6: Write-up.
interviews and analysed using reflexive thematic analysis, situated within a constructivist paradigm. From this, four themes were developed: (1) 'forming a personal connection with the therapist', (2) 'the therapist's responsiveness to the client', (3) 'is the client in good hands?', and
4 • Essentials of Thematic Analysis 15 years ago, Braun a nd Clarke (2006) argued that TA was taken for granted, commonly deployed, and yet poorly described and delineated. The landscape has changed, though. Braun and Clarke's 2006 paper has now been cited many thousands of times on Google Scholar, and alongside other writing
In a thematic structure, the core chapters present analysis and discussion of different themes relevant to answer the research question and support the overall argument of the dissertation. The chapters will include analysis of texts/ research material. They can explore and connect academic theories/research to develop an argument.
synchronously online, using semi structured interviews. Thematic analysis was used to analyse the data. Four main themes were generated: 1. anonymity, 2. access and availability, 3. communication, and 4. control. The way in which young people perceived these as helpful and unhelpful is discussed for each.
I. Data Interpretation. Firstly, good qualitative research needs to be able to draw interpretations and be consistent with the data that is collected. With this in mind, Thematic Analysis is capable to detect and identify, e.g. factors or variables that influence any issue generated by the participants.
thematic map is a visual (see Braun & Clarke, 2006) or sometimes text-based (see Frith &. Gleeson, 2004) tool to map out the facets of your developing analysis and identify main themes, subthemes ...
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 ...
A thematic analysis dissertation is a special kind of research project where you look closely at a specific theme or a group of related themes. You study the patterns in the information you gather and figure out if they have anything to do with the main research question. If you want to get a better idea of how this works, you can check out a ...
A thematic analysis (Braun & Clarke, 2006) identified overarching themes evident across all groups, suggesting the key concepts that contribute to children's understandings of obesity are "Knowledge through Education," "Role Models," "Fat is Bad," and "Mixed Messages.". The implications of these findings and considerations of ...
Grey literature was searched for using the Proquest dissertation and Theses. The initial database search produced 2,759 results, with an additional eight being located through other methods. ... (EA). To ensure reliability, another member of the research team screened a random sample (10%) ... Thematic analysis was used by over half of the ...
Inductive thematic analysis was employed to analyze the data. ... and the publication type was limited to journal articles and thesis or dissertations. ... & Clarke, V. (2016). (Mis) conceptualising themes, thematic analysis, and other problems with Fugard and Potts'(2015) sample-size tool for thematic analysis. International Journal of ...