Abstract

Traditional Q-methodology captures subjective viewpoints in a single round, assuming immediate comprehension and stable opinions. Such assumptions are increasingly problematic in today’s dynamic digital environments. To address these limitations, we introduce Claros, a hybrid Q-Delphi methodology that integrates iterative, anonymized feedback with structured Q-sorts. Claros enables participants to reflect upon, revise, and stabilize their judgments across multiple rounds through interactive visualizations and test-sorting activities. We outline the methodological phases of Claros and propose a study to assess its utility.

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