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The primary document families function like dichotomous variables see Figure 1. The labels are written in a specific syntax that turns families into nominal variables when exporting them as Excel table. For instance, all families starting with "country:: This means I developed codes inductively and very close to the data.
The code labels were still very descriptive or even taken directly from the data. When using a code word that matches the text, most software packages provide an option that is called "code in-vivo.
This is not the same as the meaning of an in-vivo code as implied by grounded theory, and I would not even call it a proper code. It is just a technical device that needs to be developed further into a code, most likely being renamed during this process. This process of descriptive and initial coding resulted in 78 codes see Figure 2.
Code list after the first phase of coding enlarge this figure here  3. A few codes in the list show high frequencies indicating that I have collected quite a number of quotations under these code labels. Therefore they are good candidates for a closer examination in order to develop sub categories.
Below I demonstrate this process using the code "consequences of financial crisis. By going through and reading the coded segments, I realized that the mentioned consequences can be divided into various aspects like economic, financial, governmental and political issues and I began to develop sub codes.
This meant adding new empty codes to the list. I prefer using capital letters for main category codes as visual aid. In addition I use the code color option to give all codes belonging to one category a uniform tint. Building sub categories enlarge this figure here  Next, I went through the data, adding content to the sub category codes.
In the interest of on-screen space, I replaced the main category code with the fitting sub category code. If needed, the contents of all sub category codes can be collected later by means of creating a so-called Code Family.
Filling sub categories with content  I repeated this process for all initial codes that showed a rather high frequency. Then I looked at those codes with a low frequency paying attention which of these can be summarized. The next section will describe that process. Developing higher order codes A number of the descriptive codes with low frequencies referred to individual consequences of the financial crisis, and as such, added to the insights surrounding the crisis.
So I collected them initially via a code family. The purpose was to filter these codes via the code family option. As the code list is sorted in alphabetic order, the codes referring to individual consequences appeared all over the place.
Setting a filter, I could focus on the individual consequences codes and it became easier to sort and order them by way of changing the name of the code labels. Codes that contained similar content were merged under a common higher aggregated code label see Figure 5.
Structured code system in list view enlarge this figure here  When you compare the list of codes in Figure 2 to the list in Figure 6, you see the difference. The list in Figure 6 can easily be explained to and comprehended by a third person due to the ways the codes have been developed and named, and thereby adds transparency to the research process.
In addition, in terms of handling the list, it becomes easy to navigate. When typing the first few letters of a category label, the cursor jumps to this category in the list and there is no need to scroll.
Further data analysis After coding, the next step was to look at the data with the research questions in mind. The research questions that were provided to us for analyzing the experiential study on the financial crisis were as follows: Did views or arguments about the causes and prognosis of the economic crisis change between and ?
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