What is Thematic Analysis?
Thematic analysis is a technique to identify, analyse, and interpret patterns. Especially those generated through qualitative data. Thematic analysis is a very useful technique for conducting research on qualitative data for a dissertation. Researchers use this method to take a more in-depth understanding of the dissertation data. It is used to understand people's experiences, perspectives and behaviours. The researchers use thematic analysis on an extensive level for conducting qualitative research.
Most of the time, only those themes that help answer the research questions have been preferred. According to dissertation writing services experts, another viewpoint is to consider all generated themes and evolve your research questions with the data. In thematic analysis, researchers generate themes with the help of different codes. These codes are the pieces of text that highlight major concepts. In thematic analysis, we share the data and interpret its subjective meaning to clarify the concepts.
What Type of Data is Required for Thematic Analysis?
Most of the time, thematic analysis is used as a primary form of data. 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 piece of statements as well. The data collected through these can be used to conduct thematic analysis. Observations and field research is other forms of data to conduct thematic analysis. In longitudinal studies, behaviours and attitudes are also considered to conduct thematic analysis. So there are diverse forms of data that we use for this aspect.
When Do We Use Thematic Analysis?
As discussed above, we collect data from interviews, experiences, and observations. So we use a thematic analysis whenever we need personal information about the data. It helps deal with a large amount of data. Moreover, it categorises the data into different themes that can be analysed.
What Are the two types of Thematic Analyses?
In this approach, we don't have any preconceptions about themes. Instead, we dive into the data for generating themes.
In this approach, we already have a set of themes we expect to generate from the data. We may find these themes while doing a literature review. Moreover, these themes may answer your research questions as well.
In this approach, we don't need to dig in to understand the subjective meaning of the data. Instead, this approach is used to take the viewpoints of the people.
In this approach, researchers want to dive into the data. This is because they want to understand its meaning. To take a deeper understanding of data, they also interpret it.
How Can We Conduct a Thematic Analysis?
The beauty of thematic analysis is that it can vary from researcher to researcher. But some generic steps can help in conducting a thematic analysis. The five steps of conducting a thematic analysis are shared below;
Familiarising With The Data:
In the first step, you need to familiarise yourself with your data. Then, you have to know what kind of themes can be generated from the data. You can also convert the form of your data in this phase.
In the 2nd step, you start making codes from the data. 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.
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. 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.
After carefully generating the themes and sub-themes, the next step is to review them. In this phase, we review themes about the codes and as a whole. We review whether our themes represent the codes or not. We also observe that these themes are not what they are meant to be. Further, we must carefully review whether these themes are relevant to the real data. Also, we consider their relationship with the research questions. While dealing with a large data set, we should not get distracted by our research objectives. So the reviewing of themes has significant importance for answering the research questions.
This review might also help in evolving the research questions. Defining the themes more specifically helps the researcher 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 clears 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.
Give Your Narrative:
After thoroughly reviewing the themes, it's time for researchers to share their narratives. This narrative must share the true picture. While sharing a narrative, researchers need to quote data that strengthens their points. Researchers need to give strong arguments to prove their claims. They also need to 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 them.
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. Researchers' narratives have significant importance for readers. Your narrative has something that catches the readers' interests. Graphical representation of themes, quotes, and stats can explain the narrative. Hence, the researchers have to be very careful while sharing their narratives.
Advantages of Thematic Analysis
The thematic analysis allows us to use a flexible approach to the data. We can make changes in the design of the studies. The research objectives can also be changed during the research process. We don't have to follow prescriptions. 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. The study shows a flexible method that allows researchers of every level to use this analysis technique (Kiger, 2020). This flexibility is the real beauty of thematic analysis. That's why most of researchers use this technique to conduct a qualitative approach.
Good For Large Data:
Analysing large data is not an easy task in a qualitative study. The researchers 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 it with large data. The thematic analysis divides the data into different data sets. It also saves the researchers from distraction. They can easily analyse a large set of data without any hesitation. This advantage of thematic analysis attracts many qualitative researchers as well.
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 approach. Because it gives a true picture of the underlying concept, it shares the content and explains its reasons. Thematic analysis helps in explaining concepts that are new for 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. Thematic analysis helps in understanding these aspects through a different lens.
Answer Every Research Question:
Thematic analysis is also helpful in answering any research question. In the subjective approach, everything has some meaning. Researchers widely use this approach to answer questions that can look difficult (Guest, 2012). This is a major advantage of thematic analysis that attracts many researchers to opt for it.
Personal Knowledge Can Be Applicable:
Most of the thematic analyses have specific rules for conducting research. But the thematic analysis doesn't bound 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.
Disadvantages Of Thematic Analysis:
Difficult To Focus:
In thematic analysis, different types of themes are generated from the data. Novice researchers may feel difficult to handle such data sets. They don't understand what data to focus on. This distraction may cause a loss of important data. They may also feel difficulty in differentiating between the themes and codes. Most of the time, novice 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.
Thematic analysis encourages researchers to apply their knowledge. Unfortunately, most novice researchers start depending on their personal experiences and ignore the study's theoretical framework. This ignorance of the theoretical framework decreases the importance of the study. The researcher’s personal experience gains importance if it aligns with the study's theoretical framework.
No Objection About The Respondent’s Language:
The thematic analysis doesn't have any technical claim about the use of language. The data can be in any form. If researchers collect data from the interviews, they don't ask respondents to answer only in English. The thematic analysis 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. This is one of the major disadvantages of thematic analysis.
Miss The Rich Amount Of Data:
Most of the time, researchers 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 with the data either. They also 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 researcher, and they only accept data that fulfil their academic requirements.
Thematic analysis is a technique for conducting qualitative research. It helps in understanding the patterns of meaning within a text. We use it for both primary and secondary data types. We use inductive and deductive approaches for this analysis and use any data, including interviews, observations, field research, and even qualitative data. It is used to understand the in-depth meaning of the data. Researchers understand their meanings and then generate codes. These codes further generate themes that help reach specific results.
Most of the time, researchers use this to get subjective information about the data. It is also helpful in dealing with a large amount of data. It gives flexibility to the researchers. It allows them to use their personal experiences. Novice researchers use a thematic analysis. Sometimes a large amount of information distracts them, and they don't meet their research objectives. It is a wonderful technique that attracts experienced and novice researchers. It encourages the researcher to interpret and not just describe the data.
- Guest, G., MacQueen, K. M., & Namey, E. E. (2012). Introduction to applied thematic analysis. Applied thematic analysis, 3(20), 1-21.
- Kiger, M. E., & Varpio, L. (2020). Thematic analysis of qualitative data: AMEE Guide No. 131. Medical teacher, 42(8), 846-854.
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