Coding and data collection

Coding and Collecting Data for Qualitative Study Designs

Qualitative research often deals with social phenomenon, leaving its data that is utilized to be incredibly abstract. As a result, the clear cut statistical measurements of quantitative methodologies are not always appropriate for research and data analysis within a social sciences context that deals with these social phenomena. Thus, qualitative data collection and analysis becomes a key feature in contemporary research, which allows researchers to collect more abstract forms of data, and then code it into categories where meaningful concepts can be drawn about the nature of the phenomenon itself.

Data collection becomes an essential, yet difficult task within the context of qualitative analysis. The data researchers encounter in this area of research is often very abstract, and unable to be entirely quantified to fit into the specific needs of certain statistical analysis models. Qualitative data is often collected through survey or interview questions that researchers make sure to remain open ended. In this, participants are allowed greater freedom in expressing their opinions and answers regarding the nature of the questions they are being asked. Yet, this data is notoriously difficult to arrange in any sort of meaningful way were assumptions can be made by the research team. To handle this, many researchers turn towards coding this abstract data into meaningful categories so that they are able to make correlations and understand relationships between core concepts. Coding requires that researchers use frequently used words or concepts to separate answers or participant responses into particular categories. Once these categories have been laid out, researchers can then turn their work on finding the relationships among these categories that help answer the underlying questions driving the research.

One successful coding methodology for qualitative research is the use of grounded theory. This relatively new field of qualitative methodology focuses on taking the abstract and quantifying it to a degree where researchers can categorize and code survey and interview responses as to make meaningful connections between them. There are several rounds of coding that take place during this process. First, there is open coding, where frequently used words or concepts help separate useful data from other data that may not be useful in that particular research context. Next, the researchers turn to selective coding, which is "the process that links all categories and sub-categories to the core category thus facilitating the emergence of the 'storyline' or theory" (Corbin & Strauss, 2008, p. 155). This style of coding focuses on making relationships out of the emerging categories of more abstract qualitative data. Last, there is axial coding, which strengthens relationships between core concepts and categories. According to the research, "this process is used to make connections between categories and sub-categories and allows a conceptual framework to emerge" (Corbin & Strauss, 2008, p. 154). By using such coding methods, core concepts can help point to meaningful answers.