What is Thematic Analysis? Advantages and Disadvantages PhD Students Should Know

This blog comprises the advantages and disadvantages of thematic analysis, which is important for every PhD student struggling to choose the right research method for their large data. Keep reading to discover everything about this research method.

Imagine you have a big pile of data in the form of observations, graphs, interviews, and transcripts to analyse during your PhD research. You would get overwhelmed by the analysis of this extensive data and end up reporting a weak research analysis. That is where thematic analysis comes as a saviour for you.

This qualitative research method is essential for identifying key themes and patterns in data, yet many PhD students overlook its importance. In this blog, The Academic Papers UK have explained everything you need to know about this powerful research technique.

Let’s start with learning what thematic analysis is.

What is Thematic Analysis?

According to a study by ScienceDirect, “thematic analysis is a technique to identify, analyse, and interpret patterns, especially those generated through qualitative data.”

It is a beneficial technique for researching qualitative data for any research project. This analysis is used to understand people's experiences, perspectives and behaviours by identifying themes. 

Most of the time, researchers only prefer those themes that help answer the research questions. Nonetheless, sometimes they consider all generated themes and evolve their research questions with the data. 

Further, researchers and PhD scholars use this method to gain a more in-depth understanding of the data that is available on their topics. They also generate themes using different codes in this analysis. These codes are the pieces of text that highlight major concepts. 

To better understand this analysis, let’s explore some common types of this research method.

Four Types of Thematic Analysis

• Inductive Approach: In the inductive approach, researchers don't have any preconceptions about themes; instead, they dive into the data to generate themes.
• Deductive Approach: In this approach, data analysts already have a set of themes that they expect to generate from the data. In addition, they may find these themes while doing a literature review. These themes may answer their research questions as well.
• Semantic Approach: In this type of thematic analysis, researchers don't need to dig into the subjective meaning of the data. Instead, this approach is used to take the viewpoints of the people.
• Latent Approach: In this approach, researchers want to dive into the data. This is because they want to understand its meaning and get a deeper understanding of data. The best part of this method is that researchers can interpret the data during this analysis.

Now that you have explored what thematic analysis is, it’s time to learn some advantages and disadvantages that every research student should know.

5 Advantages of Thematic Analysis

Thematic analysis offers various benefits, especially when you are conducting qualitative research to write your research paper. This type of research method goes beyond the surface-level analysis and gives in-depth details about the patterns or themes within the available data. In simpler words, it is the simplest and effective research method for doctoral programs.

Nonetheless, some PhD students find it difficult to collect data during this analysis and fail to write a winning research paper. To ensure the successful completion of their paper, they can avail themselves of research paper writing services from our experts. 

Do you want to know more about the benefits of this analysis method? Continue reading this section to explore these.

1. Flexibility

A research study needs a flexible method that allows PhD scholars at every level of analysis. The flexibility is the real beauty of thematic analysis. That's why most researchers use this technique to conduct a qualitative approach.

The thematic analysis allows them to use a flexible approach to the data. Are you wondering what type of flexibility this method offers? We have an answer for you.

• During this thematic analysis, you can make changes in the design of the studies. 
• The research objectives can also be changed during the research process. 
• We can collect data in different forms. 
• It uses a subjective approach so that we can relate many theories to it. Every researcher can have his/her technique for conducting thematic analysis. 

2. Inductive Development of Code

Thematic analysis helps dig into the data without any preconceptions. It allows you to generate real codes from the data. This approach increases the authenticity of this analysis as it gives a true picture of the underlying concept, shares the content and explains its reasons. 

Additionally, it helps in explaining concepts that are new to people. Humans are not machines that can be judged at face value or based on numbers. It is important to understand the actual meaning of their actions and people's words. Additionally, this analysis helps in understanding these aspects through a different lens.

3.  Good For Large Data

Analysing large data is not an easy task in a qualitative study for PhD programs. The scholars can become distracted from their goals. They feel uncomfortable dealing with a bundle of data. Thematic analysis helps in such situations. It is easy to conduct this analysis with large data. This advantage of this analysis attracts many qualitative researchers as well.

Have a look at how this method aids in handling the extensive data.

• The thematic analysis divides the data into different data sets. 
• Researchers can easily analyse a large set of data without any hesitation. 
• By analysing data into small portions, researchers avoid any kind of distraction that they may face during the research.  

4. Answer Every Research Question

Another great benefit of thematic analysis is that it enables researchers to answer every possible research question, which is significantly beneficial for the subjective approach. Because in this type of approach, everything has some meaning. Researchers and PhD students widely use this approach to answer questions that can look difficult.

In addition, this analysis method empowers them to know the hidden meaning of the keywords, which is generally difficult with other methods. The reason for this is that research methods usually explore the research related to the topic, but don’t explain the meaning of the keyword, which makes it hard for researchers to understand the purpose of the research. 

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5. Personal Knowledge Can Be Applicable

Most of the research analysis methods have specific rules for conducting research. But this analysis doesn't bind us to a specific set of rules. In thematic analysis, personal experiences have significant importance. Personal experiences are also involved in the topic. They can provide a deeper understanding of the topic. 

This benefit makes this method the first choice of researchers as it can help them in circumstances when there is not enough data available on the topic. Using the personal data and interpretation results with this analysis will help them to write an outstanding discussion section of their dissertation. 

If adequate data is not available and you find it hard to write your dissertation for your doctorate, you can get dissertation writing services from our professional writers to write a high-scoring dissertation and ensure your academic success. 

5 Disadvantages of Thematic Analysis

As you have explored the advantages of thematic analysis, it’s time to check out some drawbacks of this analysis method so you can better understand whether this analysis method is suitable for your PhD dissertation or not. 

1. Difficult To Focus

In thematic analysis, different types of themes are generated from the data. Novice researchers or PhD students may find it difficult to handle such data sets. They don't understand what data to focus on. This distraction may cause a loss of important data. 

Furthermore, they may also have difficulty in differentiating between the themes and codes. Most of the time, beginner researchers consider the themes as codes and vice versa. Thematic analysis is favourable for novice researchers, but at the same time, it also distracts them from their objectives.

2. Time-consuming

Thematic analysis is time-consuming, as in this research method, the researcher has to analyse a large volume of datasets. According to a report by the BMJ Journal, it takes 125.6 hours for thematic analysis of a dataset having 21 semi-structured interviews.

Reading this larger data and assigning codes to each dataset is not possible within a short time. A researcher requires a significant amount of time for this analysis, as he has to perform the following tasks during this method.

• Reading
• Coding
• Analysing themes
• Reviewing

It is saying without any doubt that this process becomes tedious and drains the energy of the researchers. Thus, this research method is not good for projects with short deadlines.

3. A Subjective Approach

One of the biggest disadvantages of thematic analysis is that it is a subjective approach, which means it encourages researchers and PhD students to apply their knowledge. Unfortunately, most new researchers start depending on their personal experiences and ignore the study's theoretical framework, which eventually makes this research method biased to some extent. 

In addition, ignorance of the theoretical framework decreases the importance of the study. The researcher’s experience gains importance if it aligns with the study's theoretical framework. Further, different researchers have different interpretations regarding the same data, which may raise a question about the credibility of this research method.

4. No Objection on Language

The thematic analysis doesn't have any technical claim about the use of language. The data can be in any form, such as 

• Surveys
• Interviews
• Transcription

If researchers collect data from the interviews, they don't ask respondents to answer only in English. It only concerns a large amount of rich data. The data can be in English or the mother language of the respondents. The researchers don't object to the language. However, the language barrier makes data difficult to analyse and compare with other studies. This is one of the major disadvantages of thematic analysis.

5. Miss The Rich Amount Of Data

Most of the time, PhD students stick with the theoretical framework. This behaviour may cause the analysis of a large amount of data. However, they don’t dig into the data to understand its meaning. They don’t relate their personal experiences to the data either. 

Additionally, they ignore the themes that do not meet academic framework requirements, but those that pop up from the data. This is a basic disadvantage of thematic analysis, where a large amount of data confuses the PhD scholars, and they only accept data that fulfils their academic requirements.

As you have discovered some advantages and disadvantages of this analysis for PhD students, we hope that you better understand this research method. Do you want to know how to conduct this process? We have got you covered. In the next section, we are going to discuss how to conduct this analysis.

How to Conduct a Thematic Analysis? A Step-by-Step Guide

There is no general method to conduct a thematic analysis, which makes conducting this analysis overwhelming for novice beginners. Every researcher has their own way to analyse a larger text using this method; however, some researchers and PhD students find a solid background in thematic analysis by Braun and Clarke (2006). 

Have a look at the key thematic analysis steps.

1. Familiarise Yourself with Data

The first step for your thematic analysis is to familiarise yourself with the data. This familiarising data will help you to identify the general themes that pop up in your mind while analysing your data. At this step, you must follow your preliminary thoughts that come to you about coding your data in the later stages, such as what type of code you will use for your data and how you will code it.

Additionally, it is good to keep in mind your research topic, research objectives, and aims. For instance, if you want to know what people feel about different breeds of cats, you may code your data according to the cat breed and feelings of people who brought for each type.

It is good to keep a reflexivity journal during your analysis to write down the plan about how you’ll code your data. Keeping this journal helps you in the following way:

• This journal will help you in the final stage of your analysis to identify whether your coding is reliable or not
• It also allows you to analyse the data systematically and consistently.
• With the reflexivity journal, you can identify what exactly your data reflects. 

2. Develop Initial Codes

In the second step, you have to start making code from the data. At this stage, the researcher goes back and forth from the data to find new codes. In this process, they also refine their codes. These codes set the platform for understanding relevant concepts. Researchers give significant importance to the process of coding. Statements with the same meaning need to be considered under the same code umbrella.

This coding can be done manually or with the help of the software. Further, these codes are small segments of raw data that can be categorised in some meaningful ways as per the concept that is being discussed in the research work. This coding becomes a little bit tricky for researchers and is considered one of the common limitations of this analysis. 

3. Search for Themes

The next step is to group codes for generating themes.  It is important to understand the codes and their relationship with one another. We can represent codes by using single words. In contrast, themes must be sentences that give some meaning. Sometimes you might also find sub-themes from the data. These subthemes help in finding the bigger ones that answer the research question. For instance, if your theme is bird, then falcon, canary, or pigeon may be your sub-themes.

Additionally, it is not necessary that moving from coding to theme will be a smooth process. Sometimes, it happens that when you go further after coding, you will better understand your data and find out that there is a need to change previously assigned codes. Hence, it's important to understand the importance of your data. Please don't ignore any data by considering it useless. This mistake might cause the loss of important findings.

4. Reviewing Theme

After carefully generating the themes and sub-themes, the next step is to review them. In this phase, we review themes about the codes as a whole. While reviewing the selected themes, you have to keep the following things in mind.

• Check whether the themes represent the codes or not.
• You must also observe that these themes are not what they are meant to be.
• Further, review your themes carefully to ensure that these themes are relevant to the real data.
• Also, consider their relationship with the research questions. So, if you are dealing with a large data set, you will not get distracted by our research objectives. Thus, the review of themes has significant importance for answering the research questions.

Additionally, this review might also help PhD students and researchers in evolving the research questions. Defining the themes more specifically allows the researcher to reach more significant findings. Each theme has its meaning, which relates to the whole scenario. 

Researchers need to use the statements for a theme that clarifies the concepts for them and their other audiences, too. Reviewing the themes again and again helps a researcher understand the in-depth meaning of themes. This understanding helps the researcher explain the real meaning of data as well.

5. Finalise Themes

Now it’s time to finalise the themes and name them. Finalising themes means you have to name your themes and describe their details. This step is significantly helpful in shaping your analysis. It is important to keep in mind that if you moved from the previous stage, that doesn’t mean you cannot go back to revise your theme. So, if you are struggling with your data and want to go back to the previous stage to refine your theme, you can go back and make the changes that you need.

Additionally, when you name your themes at this stage, you have to make sure you select the right labels that clearly showcase the properties of your themes. For instance, a theme named ‘hard work professionals’ may raise the question ‘who are the hard work professionals?’Therefore, you must give a clear label that depicts the specifications of your theme. 

In that case, you have to elaborate on the theme as ‘hardworking healthcare professionals.’ In case of using a reflexivity journal, you have to write a few lines describing the theme and why you chose it. Moreover, in this journal, you have to mention how your selected theme will contribute to the outcomes. 

6. Give Your Narrative

After thoroughly reviewing the themes, it's time for PhD scholars to share their narratives in a report. This narrative must share the true picture of what the researcher interpreted from the data. In this report, the researcher should add the following things.

• Researchers need to quote data that strengthens their points. 
• They also have to give strong arguments to prove their claims.
• While writing this report, researchers should remember that they have to answer the research questions, so their narrative must answer them effectively. If their narrative doesn't answer the research questions, they must revise it. Answering the research questions is the priority of researchers. Suppose they don't answer, then the validity of that study will be a question mark. 

In simple words, researchers' narratives have significant importance for readers. This narrative has something that catches the readers' interest. A graphical representation of themes, quotes, and stats can explain this narrative. Hence, the researchers have to be very careful while sharing their narratives.

As you have learned how to conduct a thematic analysis, we hope that you can now analyse your data to write a winning case study. If you are still confused about how to analyse your data using this research method, you can get customised case study writing services from our professional writers, who can help you at every stage of writing your case study. 

What Type of Data is Required for Thematic Analysis?

Most of the time, thematic analysis is used as a primary form of data analysis. But it can also be used on secondary types of data. For example, we can collect data through face-to-face interviews, and audio and video-based statements as well. The data collected through these can be used to conduct this analysis. 

Observations and field research are other forms of data to conduct this analysis. In longitudinal studies, behaviours and attitudes are also considered during this analysis. So there are diverse forms of data that we use for this aspect.

When Do We Use Thematic Analysis?

The thematic analysis in research is used to analyse the qualitative research data. Interviews, experiences, and observations are the most common data types that are being analysed with this research method. This method categorises the data into different themes that can be analysed.

Below are some exemplary research questions about which you can use the thematic analysis to answer:

• How can patients approach the doctors in an emergency?
• What are some unpopular opinions about climate change?
• What are working women's experiences in the workplace?

To answer these types of questions, you would have to collect the data from a large group of data and analyse it. Further, this technique will give you flexibility by allowing you to add your own perspective to answer it.

Conclusion

Thematic analysis in qualitative research is a powerful technique which allows PhD students to understand the patterns or themes in the data. The primary aim of this research method is to explore people’s observations, experiences, and perspectives by identifying the themes within the data. This analysis method is popular in the academic research realm due to its amazing benefits. 

The greatest benefit of this analysis method is its flexibility for PhD scholars. With this, it is good for analysing large data, answering research questions effectively, and presenting personal knowledge. However, with these benefits, there are some drawbacks of this research method, which include subjectivity, time-consuming nature, a lack of focus, using any language, and missing a large amount of data. 

If you want to avoid the potential disadvantages of this analysis method, you can follow the steps mentioned in this guide. We hope that these steps will help you overcome its drawbacks and successfully conduct this method.

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