Thematic analysis is one of the most qualitative analyses used. They are a compelling kind of analysis when appropriately used. The thematic analysis uses codes for summarising the essential concepts within a set of data. Codes here are vital for the themes. A theme here refers to patterns that can be identified within the set of data. Grouping your data in different themes is very helpful in answering research questions. It helps achieve the aims of the research and other objectives. Thematic analysis is a study of patterns of meanings. We can also say it’s all about analyzing data with different themes to identify its purpose.
Hence, Your research questions entirely drive the process. It is needed to focus on critical aspects related to your research question rather than identifying different data themes. Though it is important to remember that the questions with thematic analysis are not necessarily fixed. You might feel thematic analysis is an exploratory process. This involves progress with codes and theme identification. There are many ways of doing thematic analysis for doing analysis of data. These are very beneficial when working with large amounts of data as it allows you to divide and categorize. Thematic analysis is very particular in the case of any subjective information conducted for data derived from. Your research questions also provide you with an understanding of using thematic analysis or not. Mentioned below are a few examples of how your research questions should be:
- What are the experiences of teachers with a shift to an online class?
- How is gender constructed in college classroom settings?
Both of the above questions focus on the participants’ subjective experiences. They aim to assess expertise and opinion. This helps make the thematic experience possible. We can conclude that thematic analysis is a good choice when categorizing huge data categories. It provides a great experience when you are particularly interested in subjective analysis.
Here is a framework for conducting a thematic analysis:
- Familiarising with the data
The very first step with analysis is reading the transcript. Through this, you can become thoroughly familiar with the entire body of data. Here you can make valuable notes about the extract.
- Generating initial codes
Here you can organize the set of data in a meaningful and systematic way. Coding here reduces large amounts of data into minor meanings. Here you can also determine the methods for perspective and research questions. When coding different segments of data, you can capture exciting research questions. Not to forget, each set of data is about coding the transcript separately. Well, there are many free coding BootCamp available in the market. After comparing all the codes, you can also change them before moving to the next steps.
- searching for themes
Themes are patterns that capture interesting things about your research. Themes are categorized as per the importance of your research. If you have small data sets, there can be overlapping between the coding and the preliminary stages for the identification of themes. In that case, you can quickly determine the codes to get fitted in the themes.
At the end of the step, you can organize your theme more broadly to say something specific about the research questions. Most of the codes here are associated with more than one theme.
- Review themes
Here you can modify and develop themes. It’s helpful to gather all the data relevant to the articles. You can easily cut and paste any function in the word packaging process. While reading the data associated with the theme and consider data support in it. It’s important to note that your themes must be different from each other. There are several other considerations which include:
- Do your theme makes sense
- Data support to the theme.
- In case of theme overlap, look for really separate themes?
- Look for any sub-themes in your analysis.
- Look for themes within the data.
It’s essential to look for themes in every aspect of the data set. Depending upon your research, you might be interested in looking for how often these themes occur.
- Writing up
With research, there is a point of reporting with some journals in the area of learning and teaching. This is how you feel good about a thematic analysis.
When To Use Thematic Analysis?
A thematic approach is an excellent approach to research to find people’s views, opinions, knowledge, experience, and value from different qualitative data sets. For answering questions, you will collect data from a group of participants and then analyze them. The thematic analysis allows you to interpret the data flexibly and quickly. It sorts large data sets into broad themes. yet, the thematic analysis also involves the risk of data variation. Thematic analyses are a bit subjective and are dependent on judgment research. Hence it is needed to reflect on your own choices and interpretations. We can here conclude that we use thematic analysis when we need information about the data. It’s beneficial in dealing with large amounts of data. It can be used for categorizing data into different themes with analysis.
What Are The Benefits Of Thematic Analysis Coding?
Thematic analysis is a flexible approach for qualitative data research. It allows easy changes in the design during the research processes. You do not need to follow perceptions with thematic analysis when collecting data in different forms. These subjective approaches are subjective hence can be related to many theories. Every researcher uses their technique for thematic analysis. This allows the researchers with different levels of analysis. The flexibility of thematic analysis attracts the user towards it for making a qualitative approach.
2. The idea for a large volume of data
Making a qualitative study is not easy with extensive data. The user easily gets distracted from their goal if they feel distracted by a bunch of data. But with thematic analysis, it’s easy to make analyses for large amounts of data. The data gets divided into different data sets hence saving the user from distraction.
3. Inductive code of development
Thematic analysis helps in getting big data without perceptions. It enables the researchers to generate codes from accurate data. Thematic analysis has increased the authenticity of this analysis. It is helpful in getting real pictures of the underlying concept of the data. These analyses are beneficial in explaining the concepts to new people. Thematic analysis helps understand the images from different angles.
4. Answers to every research question
These analyses are very helpful in getting solutions to every research question. Researchers use this type of analysis to get answers to complex questions. This feature attracts a large number of researchers.
5. Personal knowledge
Most thematic analysis researchers have set a specific set of rules for conducting the research. They can also have intimate knowledge of a particular topic. They also provide a deeper understanding of the topic.
What Are Different Approaches To Thematic Analysis?
Now, when you understand thematic analysis, it’s time to look forward to different approaches to thematic analysis you can conduct. Yet, the thematic analysis approach is highly dependent on research designs; hence, it is possible to use more than one approach.
- Inductive approach
This kind of approach involves deriving meaning and collecting data without any preconceptions. With an inductive approach, you can dive into data analysis without the idea of a theme emerging. Thus, the process enables the theme to be determined or emerge from data. Hypotheses of research and theoretical approach involve codes here. Let us consider an example of women’s negative perception about technology for mobility. This will be regarded as an inductive approach as previous researchers have already had negative experiences in the public space. Here research will reveal the gendered experience of the study, with women having more negative experiences as compared to men.
2. Semantic approach
These approaches ignore the meaning of data and identify the themes based on things at face value. The type of analysis looks after what people say or assume underpinning the data. This means ideas, assumptions, and conceptualizations are theorized for informing the semantic content of data. The example of #The meetoo movement represents a semantic approach.
3. Latent approach
This approach focuses beyond the underlying concepts, assumptions, and theories laid by the semantic system. These approaches are best for research as they require a process or a combination of techniques to align your data. Furthermore, this approach involves elements of interpretation. Here the data is not just taken at face value but also categorized.
Tips for thematic analysis
- Interpreting and analysis
It’s straightforward to get stuck in the loophole of summarising the data. You can use your point of view for summarising those data.
- Themes are identified from the data.
Do not opt for the data structure from research questions. This will make your theme represent your research question; instead, opt for finding themes that relate to the meaning of your data.
- Themes for convincing data:
Evaluate if your article has enough data for backup. There are no rules for proving the existence of a theme, but it’s essential to have convincing data with recurring meaning from your data.
- Data support and themes for narrative help
Ensuring your themes are correctly represented and narratives of your data are backed up. Cross-checking the connections with each step will ensure that you are not making long jumps between every step.
What is the one-to-one process of how to do a thematic analysis?
Once you have gathered all the necessary information regarding your data, it’s your time for building thematic analysis for your problems:
The first step with your data is familiarisation. This involves feelings for seeing the general themes pop-ups. It includes reading the comprehensive data with an overview of texts and concepts for taking personal notes if necessary. This will help you understand your data.
This includes highlighting certain groups of data that process together to indicate meaning. This will become easy when you are looking forward to getting the essence of the data.
- Generation of themes
After getting codes from your data, it’s time to generate themes from your data. The themes here should be as such that represents your data. This will further provide an idea about the codes being used again and again. And we can also discard the principles that do not serve the purpose.
- Reviewing themes
Here, you can compare the themes with the original data. This will help you find any irrelevant point in the data analysis. Here you can modify and justify themes as per your satisfaction by tracing the data back to the theme.
You can name the theme according to what they are indicating and what you want to understand from the data.
In the final step, you will come to a conclusion that your analysis will help in understanding. You can here conclude the perfect time for analyzing the research.
The Final Thought
Thematic analysis is an approach to make qualitative analysis. In our blog, we have covered the basics of thematic analysis coding. These easy-to-use codes lead researchers to more complex qualitative analysis. They provide complete flexibility to the researchers and can be used with every kind of theory. This analysis enables the researchers with a detailed description of the data from different lenses. Thematic analysis approaches are widely used in the present times for making qualitative approaches. It helps in better understanding text with different meanings. They can be easily used on both primary and secondary data. You can use this data for interviews, feedback, field research, observation, etc.
These data are highly used for understanding the depth of the data for getting specified results. Most of the researchers get subjective data from these researchers. They are accommodating in dealing with large amounts of data and allow users to use their data. The thematic analysis provides wonderful experiences. It not only attracts new researchers but also encourages the researchers to interpret and describe the data.
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