Qualitative Data Analysis

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, behaviors, activities, meanings etc. can be coded.

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





Impacts of CSR programs and initiative on brand image: a case study of Coca-Cola Company UK.  

Activities, phenomenon


Supporting charitable courses

Ethical behavior

Brand awareness

Brand value

An investigation into the ways of customer relationship management in mobile marketing environment  


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 specific 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.


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John Dudovskiy