![]() These are the ideas you notice and collect when you begin the coding process. First cycle coding, according to Saldaña (2009: 45), refers to those processes that happen during the initial coding. The idea of the cycle fits the nature of the N-C-T model, where you have seen that qualitative analysis is cyclical rather than linear. I like Saldaña’s idea of first cycle and second cycle coding (Saldaña, 2013: 8). With more coding, they are likely to change and develop further. The first categories that you develop are likely to be provisional, as they are based on very little coding. Instead of being conducive to your analysis, too many codes prevent further analysis. ![]() ![]() This is far better than ten codes that only summarize one data segment each. After merging and reorganizing your codes, you will have a single code that might hold ten quotations in their original form. If you are a splinter, you need to stop coding new data at this point, review your coding and begin to merge your codes. Coders of this type are referred to as splintersin the literature. If you have noticed a lot of things – let’s say you already have 300 or more codes after coding a few interviews – your codes are probably very descriptive. If you do it much later, it will need more work because then you will have to go through all the documents again to apply newly developed subcategories and recheck all other codings. As soon as you reach this point, when you no longer add new codes (or only a few) and mostly use drag-and-drop coding, it is time to review your coding system. You have roughly described the various elements in the data at this stage. In technical terms, you will drag and drop existing codes from the Code Manager or navigation panel onto the data segments. You have reached a first saturation point. Do whatever feels most natural to you.Īt first, you will generate lots of new codes in time, you will reuse more and more of the codes you already have, and you won’t need to create new ones. If, however, after reading some of your data, you already have some ideas for codes, then go straight ahead and start coding in ATLAS.ti. If you feel that it is essential to read all the data first and write down notes on a piece of paper before you create codes in ATLAS.ti, then this is a suitable way to proceed. I look forward to exploring more, particularly data visualizations, coding mixed media, and Twitter imports (see examples below).Unless you want to code deductively using an existing framework, keep an open mind when you begin to code your data, notice as many things as you can and collect them via coding. While ATLAS.ti clearly made this small qualitative analysis project smoother and more accurate, a raw comparison of code frequencies is only the tip of the iceberg for ATLAS.ti. To analyze the data, I used the code comparison analysis feature. Although I was already working with data I produced, the ability to include descriptions for each code helped me stay accurate during the coding and begin articulating my commenting practices for the CCCC presentation. In fact, I spent significantly more time anonymizing and preparing the data than I did in the coding process. The ATLAS.ti interface and coding features were easy to use and helped me move quickly through the coding process. To get my feet wet and learn the software, I conducted a self-study of my responses to student writing for a CCCC presentation on translingual, antiracist writing practices.
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