What is Thematic Analysis? Advantages and Disadvantages

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. Researchers use this method for taking a more in-depth understanding of the data. It is used for the understanding of experiences, perspectives and behaviours of the people. The researchers use thematic analysis on an extensive level for conducting qualitative research.

Most of the time, only those themes are preferred that help in answering the research questions. According to experts of dissertation writing services, 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 texts that highlight major concepts. In thematic analysis, we are sharing the piece of data and interpreting 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, 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 another form 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 observation. So whenever we need subjective information about the data, we use a thematic analysis. It is helpful in dealing with a large amount of data. Moreover, it categorises the data into different themes that can be analysed.

What Are the Main Approaches to Conducting Thematic Analyses?

Inductive Approach:

In this approach, we don’t have any pre-conceptions about themes. Instead, we dive into the data for generating themes.

Deductive Approach:

In this approach, we already have a set of themes that 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.

Semantic Approach:

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 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. For taking 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 thematic analysis. The five steps of conducting 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 take an idea of what kind of themes can be generated from the data. You can also convert the form of your data in this phase.

Developing Codes:

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 same umbrella of the code.

Generate Themes:

The next step is to group codes for generating themes.  It is an important task 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.

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 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. We have to carefully review whether these themes are relevant to the real data or not. We also consider their relationship with the research questions. While dealing with a large set of data, we should not get distracted from our research objectives. So the reviewing of themes has a 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 own 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 audience 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, now 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 need to revise their research questions.

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

Flexibility:

Thematic analysis allows us to use a flexible approach for the data. We can make changes in 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. The thematic analysis uses a subjective approach so that we can relate many theories with it. Every researcher can have his/her own technique to conduct thematic analysis. The study shows that thematic analysis is 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 the 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 goal. They feel uncomfortable dealing with a bundle of data. Thematic analysis helps in such situations. It is easy to conduct thematic analysis with large data. 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 a large number of 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 their reasons. Thematic analysis helps in explaining the concepts that are new for people. Humans are not machines that can be judged on face value or on the basis of numbers. It is important to understand the actual meaning of their actions and the words of people. 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 the large number of researchers opting for it.

Personal Knowledge Can Be Applicable:

Most of the thematic analyses have a specific set of 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 within 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 in handling 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.

Limited Imperative:

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 theoretical framework of the study.

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. 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 are not meeting theoretical frameworks 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 fulfils their academic requirements.

Conclusion:

Thematic analysis is a widely used technique for conducting qualitative research. It helps in understanding the patterns of meaning within a text. Thematic analysis can be used on both primary and secondary data types. We use both inductive and deductive approaches for conducting thematic analysis. We can use any data including interviews, observations, field research, and even qualitative data. Thematic analysis 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. The thematic analysis 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 also distracts them, and they don’t meet their research objectives. Thematic analysis is a wonderful technique that attracts not only experienced but also novice researchers. It encourages the researcher to interpret, and not just describe the data.

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References:

  • Guest, G., MacQueen, K. M., & Namey, E. E. (2012). Introduction to applied thematic analysis. Applied thematic analysis3(20), 1-21.
  • Kiger, M. E., & Varpio, L. (2020). Thematic analysis of qualitative data: AMEE Guide No. 131. Medical teacher42(8), 846-854.

Author: Albert Barkley

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