Speaker 1: In this video, we're going to look at the ever popular qualitative analysis method, thematic analysis. We'll unpack what thematic analysis is, explore its strengths and weaknesses, and explain when and when not to use it. By the end of the video, you'll have a clearer understanding of thematic analysis so that you can approach your research project with confidence. By the way, if you're currently working on a dissertation or thesis or research project, be sure to grab our free dissertation templates to help fast-track your write-up. These tried and tested templates provide a detailed roadmap to guide you through each chapter, section by section. If that sounds helpful, you can find the link in the description down below. So, first things first, what is thematic analysis? Well, as the name suggests, thematic analysis, or TA for short, is a qualitative analysis method focused on identifying patterns, themes, and meanings within a data set. Breaking that down a little, TA involves interpreting language-based data to uncover categories or themes that relate to the research aims and research questions of the study. This data could be taken from interview transcripts, open-ended survey responses, or even social media posts. In other words, thematic analysis can be used on both primary and secondary data. Let's look at an example to make things a little more tangible. Assume you're researching customer sentiment toward a newly launched product line. Using thematic analysis, you could review open-ended survey responses from a sample of consumers looking for similarities, patterns, and categories in the data. These patterns would form a foundation for the development of an initial set of themes. You'd then reduce and synthesize these themes by filtering them through the lens of your specific research aims until you have a small number of key themes that help answer your research questions. By the way, if you're not familiar with the concept of research aims and research questions, be sure to check out our primer video covering that. Link in the description. Now that we've defined what thematic analysis is, let's unpack the different forms that TA can take, specifically inductive and deductive. Your choice of approach will make a big difference to the analysis process, so it's important to understand the difference. Let's take a look at each of them. First up is inductive thematic analysis. This type of TA is completely bottom-up, inductive in terms of approach. In other words, the codes and themes will emerge exclusively from your analysis of the data as you work through it rather than being determined beforehand. This makes it a relatively flexible approach as you can adjust, remove, or add codes and themes as you become more familiar with your data. For example, you could use inductive TA to conduct research on staff experiences of a new office space. In this case, you'd conduct interviews and begin developing codes based on the initial patterns you observe. You could then adjust or change these codes on an iterative basis as you become more familiar with the full data set, following which you develop your themes. By the way, if you're not familiar with the process of qualitative coding, we've got a dedicated video covering that. As always, the link is in the description. Next up, we've got deductive thematic analysis. Contrasted to the inductive option, deductive TA uses predetermined, tightly defined codes. These codes, often referred to as a priori codes, are typically drawn from the study's theoretical framework, as well as empirical research and the researcher's knowledge of the situation. Typically, these codes would be compiled into a codebook where each code would be clearly defined and scoped. As an example, your research might aim to assess constituent opinions regarding local government policy. Applying deductive thematic analysis here would involve developing a list of tightly defined codes in advance based on existing theory and knowledge. Those codes would then be compiled into a codebook and applied to interview data collected from constituents. Importantly, throughout the coding and analysis process, those codes and their descriptions would remain fixed. It's worth mentioning that deductive thematic analysis can be undertaken both individually or by multiple researchers. The latter is referred to as coding reliability TA. As the name suggests, this approach aims to achieve a high level of reliability with regard to the application of codes. By having multiple researchers apply the same set of codes to the same data set, inconsistencies in interpretation can be ironed out and a higher level of reliability can be reached. By the way, qualitative coding is something that we regularly help students with here at Grad Coach, so if you'd like a helping hand with your research project, be sure to check out the link that's down in the description. All right, we've covered quite a lot here. To recap, thematic analysis can be conducted using either an inductive approach where your codes naturally emerge from the data or a deductive approach where your codes are independently or collaboratively developed before analyzing the data. So now that we've unpacked the different types of thematic analysis, it's important to understand the broader strengths and weaknesses of this method so that you know when and when not to use it. One of the main strengths of thematic analysis is the relative simplicity with which you can derive codes and themes and, by extension, conclusions. Whether you take an inductive or a deductive approach, identifying codes and themes can be an easier process with thematic analysis than with some other methods. Discourse analysis, for example, requires both an in-depth analysis of the data and a strong understanding of the context in which that data was collected, demanding a significant time investment. Flexibility is another major strength of thematic analysis. The relatively generic focus on identifying patterns and themes allows TA to be used on a broad range of research topics and data types. Whether you're undertaking a small sociological study with a handful of participants or a large market research project with hundreds of participants, thematic analysis can be equally effective. Given these attributes, thematic analysis is best used in projects where the research aims involve identifying similarities and patterns across a wide range of data. This makes it particularly useful for research topics centered on understanding patterns of meaning expressed in thoughts, beliefs, and opinions. For example, research focused on identifying the thoughts and feelings of an audience in response to a new ad campaign might utilize TA to find patterns in participant responses. All that said, just like any analysis method, thematic analysis has its shortcomings and isn't suitable for every project. First, the inherent flexibility of TA also means that results can at times be kind of vague and imprecise. In other words, the broad applicability of this method means that the patterns and themes you draw from your data can potentially lack the sensitivity to incorporate text and contradiction. Second is the problem of inconsistency and lack of rigor. Put another way, the simplicity of thematic analysis can sometimes mean it's a little too crude for more delicate research aims. Specifically, the focus on identifying patterns and themes can lead to results that lack nuance. For example, even an inductive thematic analysis applied to a sample of just 10 participants might overlook some of the subtle nuances of participant responses in favor of identifying generalized themes. It could also miss fine details in language and expression that might reveal counterintuitive but more accurate implications. All that said, thematic analysis is still a useful method in many cases, but it's important to assess whether it fits your needs. So think carefully about what you're trying to achieve with your research project. In other words, your research aims and research questions. And be sure to explore all the options before choosing an analysis method. If you need some inspiration, we've got a video that unpacks the most popular qualitative analysis methods. Link is in the description. If you're enjoying this video so far, please help us out by hitting that like button. You can also subscribe for loads of plain language actionable advice. If you're new to research, check out our free dissertation writing course, which covers everything you need to get started on your research project. As always, links in the description. Okay, that was a lot. So let's do a quick recap. Thematic analysis is a qualitative analysis method focused on identifying patterns of meaning as themes within data, whether primary or secondary. As we've discussed, there are two overarching types of thematic analysis. Inductive TA, in which the codes emerge from an initial review of the data itself and are revised as you become increasingly familiar with the data. And deductive TA, in which the codes are determined beforehand based on a combination of the theoretical and or conceptual framework, empirical studies, and prior knowledge. As with all things, thematic analysis has its strengths and weaknesses and based on those is generally most appropriate for research focused on identifying patterns in data and drawing conclusions in relation to those. If you liked the video, please hit that like button to help more students find this content. For more videos like this one, check out the Grad Coach channel and make sure you subscribe for plain language, actionable research tips and advice every week. Also, if you're looking for one-on-one support with your dissertation, thesis, or research project, be sure to check out our private coaching service where we hold your hand throughout the research process step by step. You can learn more about that and book a free initial consultation at gradcoach.com.