Grounded Neural Networking: Modeling Complex Quantitative Data
Grounded theory method, Cognitive models, Research methods, Modeling, Qualitative data, Social interaction, Text analytics, Primary health care
Social Statistics | Sociology | Statistics and Probability
The latest advances in artificial intelligence software (neural networking) have finally made it possible for qualitative researchers to apply the grounded theory method to the study of complex quantitative databases in a manner consistent with the postpositivistic, neopragmatic assumptions of most symbolic interactionists. The strength of neural networking for the study of quantitative data is twofold: it blurs the boundaries between qualitative and quantitative analysis, and it allows grounded theorists to embrace the complexity of quantitative data. The specific technique most useful to grounded theory is the Self-Organizing Map (SOM). To demonstrate the utility of the SOM we (1) provide a brief review of grounded theory, focusing on how it was originally intended as a comparative method applicable to both quantitative and qualitative data; (2) examine how the SOM is compatible with the traditional techniques of grounded theory; and (3) demonstrate how the SOM assists grounded theory by applying it to an example based on our research.
Castellani, Brian; Castellani, John; and Spray, S Lee (2003). Grounded Neural Networking: Modeling Complex Quantitative Data. Symbolic Interaction 26(4), 577-589. doi: 10.1525/si.2003.26.4.577 Retrieved from https://digitalcommons.kent.edu/socpubs/29