Data analysis is basically about breaking things up into smaller parts (Kozinets, 2020). A complex phenomenon can be divided into several independent processes, each divided into the elements that make up these processes and how the elements and the processes are related to each other. During data analysis, you focus on the particularities of elements and processes; perhaps you categorize different types of elements and processes and try to discern different forms of associative ties between them.

A central part of data analysis is coding. During analytical coding, you break data into smaller parts and then assign descriptive labels (or codes) to these parts. Through coding, you can discover patterns and meaning, and with rigorous coding efforts, you also contribute to making your research valid and more transparent (Kozinets, 2020).

In doing this, you should be guided by your research questions (see 3.1.1) and the theoretical assumptions and concepts that inform your inquiry. For example, your theoretical understanding may allow you to categorize certain elements according to theoretical categories.

Exercise: Analytical coding

Select a dataset you have collected while you studied the third lesson of this module, or try to find an openly available dataset, for example, using Google Dataset Search or Kaggle. One example, where you can find several transcribed interviews from a longitudinal study on students' writing development at the University of Michigan, can be found here.

Try to break the phenomenon of study into smaller components and discern the specifics of these components and how they are related. Next, work with analytical coding by assigning meaningful labels to the identified components. While doing this, move between your research questions, previous knowledge, and empirical data. Can you see any patterns emerging?

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References

• Kozinets, R.V. (2020). Netnography: The essential guide to qualitative social media research (3 ed.). Thousand Oaks, CA: Sage Publications. Chapters 11 & 12