Information Recovery Under Partial Observation: A Methodological Analysis of Multi-Informant Questionnaire Data
Issued Date
2026-03-01
Resource Type
eISSN
20782489
Scopus ID
2-s2.0-105033861629
Journal Title
Information Switzerland
Volume
17
Issue
3
Rights Holder(s)
SCOPUS
Bibliographic Citation
Information Switzerland Vol.17 No.3 (2026)
Suggested Citation
Thepnarin N., Leelasantitham A. Information Recovery Under Partial Observation: A Methodological Analysis of Multi-Informant Questionnaire Data. Information Switzerland Vol.17 No.3 (2026). doi:10.3390/info17030290 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/115981
Title
Information Recovery Under Partial Observation: A Methodological Analysis of Multi-Informant Questionnaire Data
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Abstract
This study examines information recovery under structured partial observation in multi-informant questionnaire systems. Rather than predicting an external ground truth, we evaluate the recoverability of an operational full-information decision rulewhen only partial informant views are available. In the empirical SNAP-IV calibration study, this reference is intentionally defined as a deterministic function of the combined informant views, so the combined-view result is treated only as an oracle-style ceiling and the substantive analysis concerns how single-view recovery degrades when one informant is withheld. To examine whether a similar qualitative pattern extends beyond this calibration setting, we additionally evaluate a latent-state simulation in which the reference decision is generated from an unobserved latent state and informant views are noisy observations. Across both settings, single-view recoverability declines as inter-rater disagreement increases, whereas combined-view representations remain more stable. In the empirical study, combined-view models achieved near-ceiling recovery performance (e.g., 90.9% for Logistic Regression and 91.3% for MLP), while Teacher-only recovery dropped from approximately 78% to 63% under higher disagreement ( (Formula presented.), Cohen’s (Formula presented.) ). Additional non-learned single-rater score-threshold baselines exhibited the same qualitative degradation pattern, indicating that the effect is not specific to fitted machine learning models. Importantly, this work is methodological: it does not propose new learning algorithms or clinical prediction models, but instead presents a conceptual–methodological framework, together with model-agnostic recoverability quantities, for quantifying missing-view information loss under incomplete, heterogeneous observations.