When employing grounded theory, the researcher aims to develop their codes from the data itself. This approach is aimed at generating theory that can explain the central phenomenon under study at a more abstract level. The initial codes need to be organized into a coherent structure that forms the resulting theory.
To address this challenge, selective coding is the culminating step in grounded theory coding. In a nutshell, the researcher employs selective coding by choosing one core category to guide the development of theory in subsequent stages of coding and analysis. Selective coding is ultimately an essential part of any grounded theory analysis in that it allows researchers to point to key insights arising from their study.
When a study employs a grounded theory analysis, the researcher applies open codes (i.e., discrete units of meaning) and axial codes (i.e., relevant categories that are key to the research question) to the data.
For example, if you have a study that looks at smartphone usage, then the process of open coding might have produced initial codes such as "online games," "social networking," "time management apps," and "team collaboration apps." The axial coding process takes the codes from open coding and organizes them under broader relevant categories, such as "entertainment and leisure" and "productivity apps." By drawing relationships between the individual codes through categories, you will have created larger units of meaning that are useful for analysis.
However, for the purpose of developing theory through selective coding, a core variable or concept is necessary to further organize the codes and contribute to an understanding of the central phenomenon under study. In this case, selective coding helps you choose an overarching category that can serve as this core variable or concept to guide your analysis of the other categories as well as consideration of new data.
Selective coding is simply the act of choosing a core variable or concept among the existing categories you have created from the axial coding process to start forming the overarching theme that arises from your data and addresses your research question. In the example on smartphone usage above, you may find that discussions about smartphones center around entertainment and leisure. Through selective coding, you can select this category to represent the key theme of your project.
The act of choosing a core category through selective coding can guide theory development. With a core variable or concept in mind, the researcher can engage in focused coding where they selectively code based on the chosen category. In subsequent analysis with selective coding, one of the goals is to look out for new instances of smartphones for entertainment and leisure purposes that you may not have considered before. This new understanding can help you develop the key insights further.
Coding is a temporal process, meaning that your understanding of the data evolves and changes as you code your project and identify new relationships. Once you have chosen a central category for a core variable or concept, you will need to go back to the existing data and analyze new data to find out if your core category coherently explains patterns throughout your research project.
This is a concept in qualitative methodology called constant comparison. This refers to systematically comparing data in one category with data in other categories to generate concise descriptions of the categories and how they relate to the core variable or concept. This helps ensure that the developing theory and identified concepts have been generated empirically and rigorously.
Alternatively, through selective coding you may also find that any of the existing axial codes do not adequately fit with the other categories for the purpose of developing a theoretical framework. You may then consider organizing the existing categories into a newer, broader category that sufficiently represents the main, guiding theme in your project.
As you conduct qualitative research, keep in mind that the coding approaches discussed here aren't necessarily meant to be prescriptive. You can employ selective coding to select an existing category or employ something resembling another round of axial coding to create a new, overarching category. As long as you transparently outline your methods to ensure that the codes you generate are directly associated with the meaning embedded in the data, you should be able to produce a rigorous theoretical framework that can adequately address your research question.