The previous steps were essential in preparing your data for analysis, but they are not substitutes for thinking about your data. After organizing and cleaning your data, your thoughts may still be all over the place. That’s okay! Trust that the time you are spending now will help you identify themes in the data and what relationships may begin to appear between data sets. Ideally, as you organize your data you can continue to jot down emerging themes you find to be important and potential connections between interviews. That is why many researchers consider organizing their data as essentially the first step in the coding process.
Here are some useful ways to organize your thoughts about the data:
Your eventual analysis will require you to develop conceptual categories, or codes, which you can explore within your analytical framework. While we will discuss coding in more detail in the following section, the process of creating codes can begin as you prepare your data for further analysis. There are two main approaches to coding: inductive and deductive.
Inductive coding simply means that you start with the data, group the data by theme, and develop codes from the themes you are noticing. This type of coding is most common in qualitative data analysis and is helpful when little is known about your research topic.
On the other hand, deductive coding means that you begin with a series of codes based on an existing framework, and look for excerpts in the data that match these codes. This approach is useful if you have an idea of what participant responses will be like before the interview process even begins.
For example, imagine you are a researcher studying the eating habits of children. You could choose to analyze your data either inductively or deductively:
More is better when it comes to the early stages of coding, so err on the side of creating many distinct codes rather than a few very general codes. This will allow you to compare relationships in the data and better understand what the data is telling you.
Read on to the next section to learn all about coding and analyzing your data!
Personal Project
Take some time to think about an organizational strategy that might work best for your data. How much data will you be working with? How might you name your folders and files to keep all that data navigable? If you are already working with some data you can jump right into the early stages of analysis and begin to work on organizing and cleaning your transcripts. There is no such thing as being too immersed in the data, so go ahead and begin reading and re-reading the data you have and writing down initial thoughts and memos.
