CollabCoder: A Lower-barrier, Rigorous Workflow for Inductive Collaborative Qualitative Analysis with Large Language Models
Jie Gao, Yuchen Guo, Gionnieve Lim, Tianqin Zhang, Zheng Zhang, Toby Jia-Jun Li, Simon T. Perrault
Abstract
Collaborative Qualitative Analysis (CQA) can enhance qualitative analysis rigor and depth by incorporating varied viewpoints. Nevertheless, ensuring a rigorous CQA procedure itself can be both complex and costly. To lower this bar, we take a theoretical perspective to design a one-stop, end-to-end workflow, CollabCoder, that integrates Large Language Models (LLMs) into key inductive CQA stages. In the independent open coding phase, CollabCoder offers AI-generated code suggestions and records decision-making data. During the iterative discussion phase, it promotes mutual understanding by sharing this data within the coding team and using quantitative metrics to identify coding (dis)agreements, aiding in consensus-building. In the codebook development phase, CollabCoder provides primary code group suggestions, lightening the workload of developing a codebook from scratch. A 16-user evaluation confirmed the effectiveness of CollabCoder, demonstrating its advantages over the existing CQA platform. All related materials of CollabCoder, including code and further extensions, will be included in: https://gaojie058.github.io/CollabCoder/.