Response Style Removal via Hierarchical Bayes Parameter Learning
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Solution Overview
Problem
Existing methods for removing response style components from questionnaires fail to account for the content independence property of response styles, leading to inconsistent results across different questionnaires.
Innovation Solution
A response style component removal device that learns rater, item, and response style parameters using a hierarchical Bayes framework, allowing for the identification and removal of response style components that are independent of questionnaire content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing RS removal methods are used, then RS can be removed from a single questionnaire, but the content independence property of RS cannot be utilized and results vary depending on questionnaire content
Solution Approach 1:
The patent applies universality by constructing a unified evaluation process model that simultaneously analyzes multiple different questionnaires. The model treats response style as a common latent variable across all questionnaires, enabling the system to identify and remove RS components while respecting the content independence property. This allows the same RS removal mechanism to effectively process various questionnaire types (psychological, satisfaction, etc.) without being tailored to each specific questionnaire content.
2Ease of manufacture
If RS is removed from each questionnaire separately, then the removal process is simple, but it is difficult to distinguish between dispositional and situational response styles
Solution Approach 1:
The patent merges multiple questionnaire analyses into a single unified evaluation process model. By combining data from multiple questionnaires and analyzing them simultaneously through a common latent variable framework, the model can distinguish between dispositional RS (consistent across questionnaires) and situational RS (varying by questionnaire). This merging approach preserves information about response style characteristics while maintaining analytical simplicity through the unified model structure.
Data Source
AI summary
A response style component removal device capable of removing a response style that does not depend on content of a questionnaire is provided. The response style component removal device generates a probability of rating values for question items from raters who have rated questionnaires. Specifically, learning data for training the device include rating values for a plurality of question items from a plurality of raters who have rated a plurality of types of questionnaires. The device is configured to learn a rater parameter θk that indicates a tendency of each rater, an item parameter βk that indicates a tendency of each question item, and a response style parameter γ indicates a tendency of a response style, the rater parameter θk, the item parameter βk. The device is further configured to remove the response style parameter γ to generate a probability distribution of the rating value.