Qualitative data analysis is the process of extracting meaning from data that is not numerical. Qualitative data is different from quantitative data because it answers questions such as ‘who’, ‘what’, ‘when’, ‘where’, and ‘why’ rather than merely ‘how much’ or ‘how many. This type of analysis involves a set of techniques that are useful when dealing with unstructured information such as interviews, focus groups, and observations.
This type of analysis aims to understand why people or events behave in certain ways instead of simply what they do or how often they do it. We use qualitative data analysis when we want to understand why something happened, rather than just how frequently or with what frequency it occurred. This article provides an introduction to this subject and some useful pointers to online resources where you can learn more.
In This Article – Qualitative data analysis
- What is Qualitative Data Analysis?
- Why do Qualitative Data Analysis?
- Types of qualitative analysis
- Limitations of qualitative data analysis
- Key Takeaways
- Conclusion
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What is Qualitative Data Analysis?
Qualitative data is data that is not numerical. It is information that answers questions such as ‘who’, ‘what’, ‘when’, ‘where’, and ‘why’ rather than merely ‘how much’ or ‘how many. Qualitative data analysis is the process of extracting meaning from data that is not numerical. It is useful when dealing with unstructured information such as interviews, focus groups, and observations.
This type of data analysis is used when studying social sciences such as marketing, management, sociology, and psychology. It is not used in the same way in the natural sciences, where it is mostly used for secondary analysis of existing data.
Why do Qualitative Data Analysis?
Qualitative analysis is used when we want to understand why something happened, rather than just how frequently or with what frequency it occurred. This approach is helpful when trying to understand how people think and behave. It is particularly useful when conducting research with a sample of people or interviewing.
For instance, if we have a sample of people and ask them why they bought a certain product, we might get many different responses. One person might have bought the product because they had tried it before and liked it. Another person might have chosen to buy it because their partner recommended it to them.
Types of qualitative analysis
- Grounded Theory Analysis – This is a method of analyzing qualitative data that involves making sense of data through analysis and a process of coding, which will lead to the identification of key concepts. A researcher will formulate and test hypotheses as they go through the process of analysis so that the end result is a theory that is grounded in the data.
- Content Analysis – This type of analysis focuses on the content of the data, such as the language and style of an interview, rather than the research methods that were used. A researcher will use a set of codes to identify the main topics and subtopics in the data.
- Pattern Analysis – This is similar to content analysis but is more general. When analyzing data using a pattern approach, you may use more general codes that refer to a broader idea. For example, in an interview data set, you may focus on communications between people.
- Interpretive Analysis – This approach focuses on the meaning behind the data. It is useful when working with sensitive or controversial topics. As you analyse data, you may draw on your own experiences to help you understand what is behind the words.
- Ethnographic Analysis – This is the analysis of data that has been gathered in an ethnographic study. It is used when researchers are trying to understand the lives of a group of people in different cultural contexts.
Limitations of qualitative data analysis
- Quality – Although qualitative data can be useful, it is important to note that it can be subjective and difficult to standardize. This means that depending on who is conducting the analysis, their background, and the topic being studied, results can vary.
- Time – The main issue with qualitative data analysis is the time it takes to complete. The process of reading and rereading the data, taking notes, and analyzing the information is time-consuming.
- Limited scope – You will only be able to analyze the data that has been gathered. If the data is limited, the analysis will be limited too.
- Limited generalisability – Since this analysis is subjective, it can be difficult to make generalizations about the data using quantitative methods.
- Lack of precision – It is hard to quantify findings when conducting qualitative analyses. This means that the results will be broad and not as accurate as with quantitative methods.
- Sensitivity – It is important to consider the feelings and sensitivities of the people included in qualitative studies. It may be necessary to do additional analysis or find ways to ensure that people are not negatively affected by the research.
Key Takeaways
All research begins with a question: “What do we need to understand about this topic?” The best way to answer that question is by conducting qualitative data analysis. This type of analysis is useful when dealing with unstructured information such as interviews, focus groups, and observations.
Qualitative data analysis is the process of extracting meaning from data that is not numerical. It is useful when dealing with unstructured information such as interviews, focus groups, and observations. This analysis is used when studying social sciences such as marketing, management, sociology, and psychology. It is not used in the same way in the natural sciences, where it is mostly used for secondary analysis of existing data.
