Q methodology is epistemologically grounded in the operant nature of Q sorting, where subjectivity is enacted through the act of sorting rather than merely represented by its final configuration. However, the digitization of Q methodology has introduced a significant methodological limitation. Most existing software systems capture only the final sorting outcome, while the dynamic decision-making process—commonly referred to as paradata—remains unrecorded. As a result, researchers analyze the “product” of subjectivity, while the behavioral text of its “production” is rendered invisible. To address this gap, I developed Qsort-Owl, a web-based, open-access Q methodology platform designed to systematically capture and analyze the decision processes underlying Q sorting. Grounded in Stephenson’s concept of operant subjectivity and Brown’s notion of behavioral text, Qsort-Owl operationalizes Q sorting as a four-stage decision process. The platform records hesitation events, cognitive conflicts, and movement trajectories as paralinguistic indicators that reflect how participants negotiate meaning during sorting. A central innovation of Qsort-Owl is the Inverse Integration Analysis Workflow, which I designed to bridge process data and post-sort statistical analysis. Researchers can upload external analytical outputs, such as KADE-derived factor loadings, which the system then cross-maps with individual-level decision behaviors. This integration produces a Decision Insights Report that aggregates behavioral patterns at the factor level, as well as an Interview Candidate Selection Matrix that combines factor membership with behavioral complexity to support theoretically informed qualitative follow-up. An illustrative demonstration using feasibility-testing data shows how process-level indicators reveal analytic patterns that remain undetected in outcome-based analyses alone．

Authors:
Ming Shinn Lee, National Dong Hwa University, Taiwan

About the Presenter(s)
Lee Ming-Shinn is a Professor at National Dong Hwa University, Taiwan. His research interests include Q methodology, mixed methods research, and AI-assisted research tools. He is currently developing FREE Q methodology software platforms

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