As we get technologically more advanced than ever before, in the history of humankind the amount of information available to us gets proportionally larger. Because of these conditions, we can collect and use various data to study and analyze the different phenomena and discover new theories. Unlike the traditional way of researching, with a hypothesis as the starting point of the new theory’s deductive process, data analysis allows us a more algorithmic approach. The comparative method is a process that includes data collection and analysis for theory development. When, instead of bringing new insight, additional data proves the previous arguments this process can be closed.
Comparative analysis and file processing are not only basic elements of the method but also influential on the degree of problem-solving efficiency. The history of data almost inevitably evolved to the level of grounded theory and the constant comparative method development. Both models are based on the process of systematically comparing, analyzing, and recording data. Its objective is to collect data and combine them within chosen concepts until reaching theoretical saturation as a final point of the process. Because of its universality and simple approach to data examination, the constant comparative method is often used by many business branches for pure exploring and analyzing a large amount of information.
Constant Comparative Coding as a Career Choice
Constant comparative analysis is already an important part of many educational programs. Combined with the comparison method and other useful tools, this type of analysis is also frequently used in scientific theories and business-related decisions making. Judging by the number of available comparative analysis essay examples it seems that qualitative data evaluation skills and knowledge became valuable teaching techniques. This is hardly a surprise, as it’s proved to be an efficient way to establish similarities and differences within the issue. The constant comparative method creates conditions for the analysis of any question, item, idea, or problem in order to reach a deeper understanding of the topic. Additionally, in some cases, the process is used to form new theories and strategies.
Comparative analysis is a great introduction to the data analytics professional domain. In the modern era, companies, sports, and marketing organizations use data analysis to make business decisions, promotions, campaigns, and social media content. The analyses of the data collected from the interaction with customers can give them valuable information and new insight. As a result, these companies have a better understanding and idea of how to meet the client’s needs and expectations. Also, they can improve and grow their business faster and more efficiently.
Practical use of Constant Comparative Method
In reality, people practice this type of method every single day. Whenever we separate things into groups based on some similarity and then organize them to meet a certain kind of order, we’re actually creating a process similar to this method.
These three steps represent the first phase of the comparative method’s qualitative data analysis.
Step 1: Open coding
The purpose of this step is to create codes. The process starts when initial data are separated into individual portions or snippets. After that, these parts are mutually compared and connected into codes, based on their attributes. This is a basic example of the constant comparative method principle.
Step 2: Axial coding
The next step of the analysis is to search for the connections between previously formatted codes. The process of comparing codes with codes should result in getting the categories of connected codes.
Step 3: Selective coding
This is the third and final stage in the process of qualitative data analysis. At the end of this phase, the categories are compared with other categories to create a connection and a final, core category.
Conclusion
During the first phase, the compared groups of data will have at least three kinds of relations. With every next step, they will support or contradict previously analyzed data or initiate their further expansion. These relations will determine the course of the next activity. If a new set of data contradicts already formed codes and categories, there is a possibility that they are not composed properly.
Also, it could mean there is a lack of data. When new data induce expansion of the codes and categories, the process of data collecting and analysis should continue until new data start to support the previous data. Once new data support codes and categories, it’s a sign of theoretical saturation and a signal that this phase of the process is finished.
Author’s Bio
Alisia Stren loves technology and enjoys writing about it. Most of her work is dedicated to informing her audience, mostly students and tech enthusiasts about discoveries, new methods, procedures, and equipment novelties in this realm. Her opinions on these topics are available in the form of blogs, essays, and articles.
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