PsyCAT: Robust Posterior Mixtures for Adaptive Self-Report Assessment

Computerized adaptive testing (CAT) can shorten self-report questionnaires, reducing respondent burden while preserving assessment efficiency. Yet self-report CAT faces two challenges. First, self-report responses have no objectively correct answer, and differences in category interpretation and preferences for extreme or intermediate options can confound the latent trait with response style. Second, maximum-information selection can repeatedly probe one trait, revealing the system's provisional assessment and potentially inducing measurement reactivity. PsyCAT is proposed to systematically address these challenges. Response style is explicit modelled in proposed method in an ordinal measurement model and derives how uncertainty in style increases trait-estimation risk. A Gaussian mixture characterizes the joint posterior over latent traits and response style, separating uncertainty within components from disagreement across components. Shared-attention post-calibration adjusts these posteriors using observed answers, and minimax aggregation produces trait estimates. PsyCAT further controls information leakage by penalizing dependence between next-item selection and alternative response profiles. Across several published questionnaire datasets, PsyCAT achieved the lowest mean endpoint ranked probability score among the evaluated methods. Under controlled latent interventions, PsyCAT tracked response-style changes with little average trait drift in the style-only condition and tracked both variables in the joint condition. In a two-expert study, its selection policy is proven to reduce information leakage.