Qualitative Data Analysis
Qualitative data analysis refers to the process of interpreting non-numerical data such as interviews, observations, and documents to identify patterns, themes, and meanings. It is used to understand how and why people think, behave, or experience a particular phenomenon.
On this page:
- When to use qualitative data analysis
- Categories of qualitative data analysis
- Qualitative data analysis steps
| Method | Purpose | Key Feature |
|---|---|---|
| Content analysis | Categorise and summarise data | Coding and classification |
| Narrative analysis | Interpret stories and experiences | Focus on individual cases |
| Discourse analysis | Analyse language and communication | Context and meaning |
| Framework analysis | Structured thematic analysis | Multi-stage process |
| Grounded theory | Develop new theory | Iterative and inductive |
Qualitative data analysis methods at glance
When to Use Qualitative Data Analysis
Qualitative data analysis is most appropriate when your research aims to explore meanings, experiences, or complex social phenomena.
You should use qualitative data analysis if:
- Your research focuses on understanding behaviours, perceptions, or experiences
- You are using interviews, focus groups, or observations
- The research problem is complex or context-dependent
- You are following an inductive or interpretivist approach
- You aim to generate new insights or theories
Use qualitative data analysis when you want to understand why something happens, not just how often it happens
Categories of Qualitative Data Analysis
Qualitative data refers to non-numeric information such as interview transcripts, notes, video and audio recordings, images and text documents. Qualitative data analysis can be divided into the following five categories:
1. Content analysis. This refers to the process of categorizing verbal or behavioural data to classify, summarize and tabulate the data.
2. Narrative analysis. This method involves the reformulation of stories presented by respondents taking into account context of each case and different experiences of each respondent. In other words, narrative analysis is the revision of primary qualitative data by researcher.
3. Discourse analysis. A method of analysis of naturally occurring talk and all types of written text.
4. Framework analysis. This is more advanced method that consists of several stages such as familiarization, identifying a thematic framework, coding, charting, mapping and interpretation.
5. Grounded theory. This method of qualitative data analysis starts with an analysis of a single case to formulate a theory. Then, additional cases are examined to see if they contribute to the theory.
Qualitative Data Analysis Steps
Qualitative data analysis can be conducted through the following three steps:
Step 1: Developing and Applying Codes. Coding can be explained as categorization of data. A ‘code’ can be a word or a short phrase that represents a theme or an idea. All codes need to be assigned meaningful titles. A wide range of non-quantifiable elements such as events, behaviours, activities, meanings etc. can be coded.
There are three types of coding:
- Open coding. The initial organization of raw data to try to make sense of it.
- Axial coding. Interconnecting and linking the categories of codes.
- Selective coding. Formulating the story through connecting the categories.
Coding can be done manually or using qualitative data analysis software such as
NVivo, Atlas ti, HyperRESEARCH, Max QDA and others.
When using manual coding you can use folders, filing cabinets, wallets etc. to gather together materials that are examples of similar themes or analytic ideas. Manual method of coding in qualitative data analysis is rightly considered as labour-intensive, time-consuming and outdated.
In computer-based coding, on the other hand, physical files and cabinets are replaced with computer based directories and files. When choosing software for qualitative data analysis you need to consider a wide range of factors such as the type and amount of data you need to analyse, time required to master the software and cost considerations.
Moreover, it is important to get confirmation from your dissertation supervisor prior to application of any specific qualitative data analysis software.
The following table contains examples of research titles, elements to be coded and identification of relevant codes:
| Research title | Elements to be coded | Codes |
| Born or bred: revising The Great Man theory of leadership in the 21st century |
Leadership practice |
Born leaders
Made leaders Leadership effectiveness |
| A study into advantages and disadvantages of various entry strategies to Chinese market
|
Market entry strategies |
Wholly-owned subsidiaries
Joint-ventures Franchising Exporting Licensing |
| Impacts of CSR programs and initiative on brand image: a case study of Coca-Cola Company UK. |
Activities, phenomenon |
Philanthropy
Supporting charitable courses Ethical behaviour Brand awareness Brand value |
| An investigation into the ways of customer relationship management in mobile marketing environment |
Tactics |
Viral messages
Customer retention Popularity of social networking sites |
Qualitative data coding
Step 2: Identifying themes, patterns and relationships. Unlike quantitative methods, in qualitative data analysis there are no universally applicable techniques that can be applied to generate findings. Analytical and critical thinking skills of researcher plays significant role in data analysis in qualitative studies. Therefore, no qualitative study can be repeated to generate the same results.
Nevertheless, there is a set of techniques that you can use to identify common themes, patterns and relationships within responses of sample group members in relation to codes that have been specified in the previous stage.
Specifically, the most popular and effective methods of qualitative data interpretation include the following:
- Word and phrase repetitions – scanning primary data for words and phrases most commonly used by respondents, as well as, words and phrases used with unusual emotions;
- Primary and secondary data comparisons – comparing the findings of interview/focus group/observation/any other qualitative data collection method with the findings of literature review and discussing differences between them;
- Search for missing information – discussions about which aspects of the issue was not mentioned by respondents, although you expected them to be mentioned;
- Metaphors and analogues – comparing primary research findings to phenomena from a different area and discussing similarities and differences.
Step 3: Summarizing the data. At this last stage you need to link research findings to hypotheses or research aim and objectives. When writing data analysis chapter, you can use noteworthy quotations from the transcript in order to highlight major themes within findings and possible contradictions.
It is important to note that the process of qualitative data analysis described above is general and different types of qualitative studies may require slightly different methods of data analysis.
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