Qualitative analysis is a challenging, yet crucial aspect of advancing
research in the field of Human-Computer Interaction (HCI). Recent studies show
that large language models (LLMs) can perform qualitative coding within
existing schemes, but their potential for collaborative human-LLM discovery and
new insight generation in qualitative analysis is still underexplored. To
bridge this gap and advance qualitative analysis by harnessing the power of
LLMs, we propose CHALET, a novel methodology that leverages the human-LLM
collaboration paradigm to facilitate conceptualization and empower qualitative
research. The CHALET approach involves LLM-supported data collection,
performing both human and LLM deductive coding to identify disagreements, and
performing collaborative inductive coding on these disagreement cases to derive
new conceptual insights. We validated the effectiveness of CHALET through its
application to the attribution model of mental-illness stigma, uncovering
implicit stigmatization themes on cognitive, emotional and behavioral
dimensions. We discuss the implications for future research, methodology, and
the transdisciplinary opportunities CHALET presents for the HCI community and
beyond.

CHALET 是一种结合人工智能模型与人类合作的新方法，用于促进质性研究的概念化和推动质性分析的新观点生成，通过应用于心理疾病污名归因模型，揭示了认知、情绪和行为维度上的隐性污名化主题。