Thematic analysis is a type of qualitative data analysis method that takes large bodies of data and groups them according to their relative similarities, which can be termed as themes. Thematic analysis can be applied to various sorts of data set from secondary sources, like media to records of focus group meetings or interviews. Also, it tends to be used to analyze huge datasets, as well as little datasets. Rigorous thematic analysis can produce trustworthy and insightful discoveries (Braun and Clarke, 2006).
Thematic analysis is an exploratory process. So, it’s not unusual for one’s research questions to develop or might even change as they progress through the analysis. Boyatzis (1998) describes thematic analysis as a translation between researchers using qualitative research methods and researchers using quantitative research methods, and it enables researchers using different methods to communicate with each other. For any research that endeavors disclosure through interpretation of data, thematic analysis is viewed as the most suitable since it gives a precise element for data analysis (Alhojailan, 2012). The approach to thematic analysis forces researchers to adopt an all-around organized strategy to process the data and produce an accurate and organized final report (King, 2004).
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The approach to thematic data analysis method can occur in four forms:
- Inductive approach: this consists of a process of coding which occurs without trying to adjust the data into a pre-existing frame.
- Deductive approach: this is more of a theoretical approach; it includes analysis that is limited to preconceived frames.
- Semantic approach: this approach does not imply the researchers to dig deep and understand the subjective meaning of the data, rather it allows them to take a viewpoint of the people.
- Latent approach: this method allows the researcher to dive deep into the dataset and have a deeper understanding of the meaning of it and enables them to interpret it.
A general limitation of the thematic data analysis approach is that since multiple themes can be generated from large datasets, it may make the process of handling such large datasets quite difficult for new researchers. This can lead to situations that may cause a loss of important data if the researchers are not able to understand what data they need to focus on. The researchers sometimes might consider the themes to be codes and vice-versa.
A benefit of using thematic analysis as an approach for qualitative data analysis it helps researchers generate real codes from the data and dig into them without any preconceptions. This increases the authenticity of the research, as well as the analysis approach. Since most of the code generation process is carried out by advanced software such as MAXQDA, HubSpot and Quirkos, it gives the researchers a balanced true picture of the underlying conceptual literature and data. Thematic analysis also helps in explaining the concepts that are new for some people by providing the explanation through an unbiased approach.
The thematic data inventory of qualitative research can enhance the idea generation process for the readers and help them think outside the box when designing their studies (Noemi Sinkovics, A. Rebecca Reuber, 2021).
Although thematic analysis has specific rules for conducting research it does give significant importance to the researcher’s personal experiences and interest in the research topic as it can help provide a deeper and more analytical understanding of the topic. This can help them identify even the minor details in the dataset and themes of the research that, once explored, can further advance on published works of literature in that field.
One consideration towards adopting a thematic approach for qualitative data analysis is that simple thematic analysis is disadvantaged when compared to other methods, as it does not allow the researchers to make claims about language use or twist data to fit into a theme (Braun and Clarke, 2006). Thematic analysis does not explicitly have any rule about the use of language, i.e., the data collected can be in any form. The researchers cannot ask the respondents to answer only in English or just read from a script they provide. The data can also be in the mother language of the respondents as well. The respondents are not forced to say anything they are not comfortable with, which can affect the accuracy and authenticity of the result data. The researchers also do not object to the language that creates a language barrier which in turn makes data difficult to analyze.
When it comes to the thematic approach, every theme and code generated and groped from the data set has to have some meaning. Amateur researchers have used this approach to answer the research question they found to be tough (Guest, 2012). This major advantage has led to the wider adoption of the thematic approach for analyzing large data sets. Although when it comes to advance empirical research papers in major business journals, authors believe sometimes using only a qualitative data collection and analysis approach does not generate satisfactory results for all the scholars in the academic field. Hence, in some situations, if the authors use both qualitative and quantitative approaches for data collection and analysis (mixed methods research), the key findings and conclusion of that study can prove to be more robust (Maziriri et al., 2019).
Thematic allows the researchers to use a flexible approach towards analyzing the data, one can make changes to the study designs. It being an exploratory process enables us to even change the research objective during the research process. A researcher does not need to follow specific prescriptions and can collect the data in different forms. Thematic analysis uses a subjective approach towards the data so the researcher can relate multiple theories with it. This flexibility gives every researcher the chance to have their own technique to conduct thematic analysis.
Thematic analysis has also been one the most favorable and go-to approach for amateur researchers as well, but at the same time it can distract them from their objectives. As discussed earlier, thematic analysis gives the researcher the flexibility to apply their own knowledge in the analysis process, which makes it a popular choice. Unfortunately, some amateur researchers may end up depending too much on their knowledge and tend to ignore their study’s theoretical framework, which decreases the importance of the study. When analyzing a large data set the researchers sometimes also ignore the themes that do not meet the theoretical framework requirement, but those that pop up from the data. This is a major disadvantage of thematic analysis where researchers get confused by the large amount of data present and only accept the data fulfils their academic requirements.
Overall, thematic analysis is a great strategy that draws in experienced, as well as amateur researchers. It urges the researcher to decipher, and not simply describe the information.
References
- Braun, V. and Clarke, V. 2006. Using Thematic Analysis in Psychology. Qualitative Research in Psychology, 3(2), pp.77-101.
- Bonatto, F., Resende, L. and Pontes, J. 2021. Supply Chain Governance: A Conceptual Model. Journal of Business & Industrial Marketing 37 (2), pp.309-325.
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