Computing grounded theory: a quantitative method to develop theories

Abstract

Abstract The inductive logic of grounded theory and the principle of avoiding theoretical preconceptions are significantly different from the deductive logic and hypothesis testing of traditional quantitative research. Based on the limitations of theory production in quantitative research, this paper proposes a Computing Grounded Theory (CGT) approach that directly quantitatively assists theories. With the help of machine learning and attribution algorithms, CGT identifies variables that have not been the focus of previous studies based on the predictive power of the independent variables to propose new theoretical hypotheses, following the principle that causality is a sufficient and unnecessary condition for predictability. This paper systematically discusses CGT’s basic idea, logical premise, and methodological foundation while providing an empirical example. This method bridges the gap in the theoretical production of quantitative research and is of great value in theory, discipline, knowledge systems and social governance.

Keywords

• Computing grounded theory (CGT)
• Grounded theory
• Machine learning
• Attribution algorithms
• Quantitative research