Breaking the Ceiling: Mitigating Extreme Response Bias in Surveys Using an Open-Ended Adaptive-Testing System and LLM-Based Response Analysis

Assessments of extreme psychological constructs often face a persistent challenge: the ceiling effect, in which a significant proportion of respondents select the highest score on a scale, thus obscuring meaningful variation within the population. This effect may have profound consequences in studies of extreme psychological constructs. To address this limitation, we present a novel framework that integrates Multistage Testing (MST) with open-ended questions that are automatically analyzed by large language models (LLMs). This hybrid approach adapts the survey questions to the respondent while leveraging LLMs to efficiently and reliably interpret free-text answers from large-scale online surveys. Using a case study on aversion toward cockroaches, we show how our method can effectively eliminate extreme ceiling effects, revealing hidden data distributions that are often obscured by extreme responses to conventional Likert-type survey questions. We also validate our method by comparing LLM performance to expert human annotations. This demonstrates the consistency and reliability of LLMs in evaluating free-text answers. This framework offers a generalizable methodology that enables more precise and sensitive quantitative measurement of extreme psychological constructs, allowing researchers to study topics that until now were inaccessible due to significant, inherent ceiling effects.