If a researcher had a 50-page transcript, it would be difficult to identify patterns and trends in the data. Grouping this large amount of information into categories can help the researcher analyse the dataset; this technique is called thematic analysis.
Millions of flashcards designed to help you ace your studies
Sign up for freeWhat type of data is required for thematic analysis?
Do content and thematic analysis produce the same form of data?
Of the following, which are examples of themes?
Of the following, which are examples of sub-themes?
Which type of thematic analysis matches the following definition ‘data is coded if determined to be relevant to the research question’?
Which type of thematic analysis matches the following definition: ‘line-by-line coding is carried out, and codes are created based on the data contents’?
Which are examples of qualitative datasets?
Which of the following provides a broader context of the data?
Can statistical analysis be carried out on data that has undergone thematic analysis?
What type of data does thematic analysis analyse?
How many stages of thematic analysis are there?
What type of data is required for thematic analysis?
Do content and thematic analysis produce the same form of data?
Of the following, which are examples of themes?
Of the following, which are examples of sub-themes?
Which type of thematic analysis matches the following definition ‘data is coded if determined to be relevant to the research question’?
Which type of thematic analysis matches the following definition: ‘line-by-line coding is carried out, and codes are created based on the data contents’?
Which are examples of qualitative datasets?
Which of the following provides a broader context of the data?
Can statistical analysis be carried out on data that has undergone thematic analysis?
What type of data does thematic analysis analyse?
How many stages of thematic analysis are there?
Achieve better grades quicker with Premium
Geld-zurück-Garantie, wenn du durch die Prüfung fällst
Review generated flashcards
Start learning or create your own AI flashcards
Team Thematic Analysis Teachers
Researchers collect two types of data known as quantitative and qualitative. Both have their strengths and weaknesses, and they differ in how they are collected and analysed.
Quantitative data is relatively easy to analyse objectively, as we can use statistical analysis to understand what the results are telling us and whether they are significant. In comparison, qualitative data is harder to analyse objectively and relies more on subjective analysis.
Thematic analysis is the method used to analyse and produce qualitative data. The process involves reading a qualitative dataset form, such as a transcript. The researcher then identifies critical themes that are evident in the data. These are reported using extracts of the data as evidence.
Thematic analysis can be used in psychology research when the researcher wants to explain the phenomenon investigated in-depth.
Thematic analysis is arguably one of the most frequently used forms of analysis where qualitative research is concerned and excels at finding patterns in data to identify an overall theme.
The thematic analysis process generally involves six stages that researchers should follow. Before starting a thematic analysis, the researchers must identify a research question, operationalise a hypothesis, and conduct the research. The data used for thematic analysis needs to be in qualitative form.
1. Read through the data – the researcher becomes familiar with it by re-reading it multiple times. While reading the data, researchers take notes of their thoughts concerning the data.
2. Decide preliminary codes – systematically organising the data is the starting process. The researcher must categorise the data codes based on key themes that the researcher has identified.
The codes that researchers use are related to the research question/hypothesis.
During this stage, multiple researchers look through the data to compare the codes identified to check if these are consistent and if the coding system is reliable.
We distinguish two different types of thematic analysis: theoretical and inductive thematic analysis.
3. Identify themes in the data – the identified themes are based on patterns researchers identified in the data.
During this stage, the researchers identify if there are themes in the coded data. The identified themes provide a broader context for the coded data.
4. Checking themes – themes are modified during this stage. The themes are checked to see if they support the data.
If the themes do not support the data, they can take away the context of the data.
The context and depth of information provided is the main advantage of qualitative data, so the researcher should avoid including irrelevant themes.
5. Themes are defined – the themes are finalised, the sub-themes are defined, and the researcher justifies how the themes and sub-themes are related. These are often illustrated in the form of thematic maps.
6. The results are written up – this is the final stage, where the researcher explains the results they found.
During this stage, the researcher needs to justify the codes and themes found to show that bias did not influence them (which reduces the validity of the research).
The researcher will write the codes and themes found in the report and provide data extracts as support.
Fig. 1 - Thematic analysis is used to analyse qualitative data.
A hypothetical study identified the following potential codes:
Anger (shouting).
Happiness (smiling).
Despair (phrases such as bad things keep happening to me).
Hostility (refusing to talk, holding back).
Loneliness (phrases such as I spend all my time alone).
Sadness (crying).
The potential themes the researchers may use are positive and negative emotions (this groups the coded data together and provides a broader context/explanation for the codes identified).
Using the example above that identified the themes as positive and negative emotions, the potential sub-themes identified could be negative thoughts (e.g. bad things keep happening to me) and negative actions (e.g. crying).
When writing the report, the researcher would identify all the themes and provide extracts from the data to support that such themes exist.
Thematic and content analysis differ because the thematic analysis is used purely for qualitative research and gives a detailed account of key themes and categories.
However, content analysis uses coding units to quantify the themes within a qualitative data set.
Content analysis is used to identify words, themes, and concepts in qualitative data and transform them into quantitative data; this method uses a similar protocol to thematic analysis.
An act or theme in a transcript is given a ‘coding unit’, which is clearly defined beforehand.
The researcher may identify physical violence, e.g. pushing, kicking, hitting etc., as physical bullying.
The content analysis then analyses the data and counts how many instances of these different coding units occur, allowing for quantitative statistical analysis of the qualitative data.
In thematic analysis, a researcher codes themes in the text to then apply them and organise them into related themes, grouping them to identify key themes and categories and present them accordingly.
The type of analysis used depends on the type of data the researcher is looking for.
Suppose the researcher is carrying out a case study. In that case, they will use thematic analysis to obtain enriched data that will help them learn more about the patterns or trends concerning the phenomenon.
However, a content analysis may be used to find the relationship between specific themes/behaviours occurrences and a phenomenon.
The advantages of thematic analysis are:
The process highlights patterns and behaviours that may affect or outcome of a situation with evidence. This in-depth information can increase understanding of why certain occurrences happen.
The researchers may identify unexpected themes, providing guidelines for future research.
This approach is flexible so that the analysis can be accommodated to the data as themes or codes do not have to be pre-defined.
In quantitative analysis methods, the analysis type that can be used is restricted based on if the data meet the criteria, which is not the case in thematic analysis.
Using thematic analysis also provides a lot of detail that quantitative research can miss.
The disadvantages of thematic analysis are:
The researcher analyses the data, so themes identified and results may be due to investigator bias, reducing the validity of findings.
Thematic analysis can be a time-consuming process.
Many psychologists regard this analysis technique as unscientific due to its subjective nature.
Establishing if the data support or negate a hypothesis is difficult.
Sign up for free to gain access to all our flashcards.
What is thematic analysis?
Thematic analysis is an analysis method used to analyse qualitative data. The data is analysed based on identifying themes in the data and grouping them into key themes and categories.
How to do a thematic analysis?
There are six stages to carrying out a thematic analysis:
What is a code in thematic analysis?
The codes in a thematic analysis are terms that meaningfully group data based on key themes identified by the researcher.
How to code data in thematic analysis?
There are two different types of thematic analysis:
Why is a thematic analysis used?
Thematic analysis is used in psychology research when the researcher wants to explain a phenomenon investigated in-depth.
At StudySmarter, we have created a learning platform that serves millions of students. Meet the people who work hard to deliver fact based content as well as making sure it is verified.
Lily Hulatt is a Digital Content Specialist with over three years of experience in content strategy and curriculum design. She gained her PhD in English Literature from Durham University in 2022, taught in Durham University’s English Studies Department, and has contributed to a number of publications. Lily specialises in English Literature, English Language, History, and Philosophy.
Get to know Lily
Gabriel Freitas is an AI Engineer with a solid experience in software development, machine learning algorithms, and generative AI, including large language models’ (LLMs) applications. Graduated in Electrical Engineering at the University of São Paulo, he is currently pursuing an MSc in Computer Engineering at the University of Campinas, specializing in machine learning topics. Gabriel has a strong background in software engineering and has worked on projects involving computer vision, embedded AI, and LLM applications.
Get to know Gabriel