Conjoint Analysis Regression for Interaction Effects
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Solution Overview
Problem
Conjoint analysis methods face impracticalities in testing all possible combinations of elements, leading to reliance on intuition and imperfect results due to the inability to accurately assess how individual elements interact in combinations.
Innovation Solution
A method involving linear regression, specifically least squares regression, is used to determine utility values for individual elements and their combinations by presenting experimental designs to respondents and recording ratings, allowing for the calculation of interaction effects through the relationship between element presence/absence and combination effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all possible element combinations are tested to accurately assess interaction effects, then measurement precision is improved, but device complexity and loss of time increase significantly
Solution Approach 1:
The patent segments the analysis into two distinct parts: (1) individual element utility values obtained through traditional conjoint analysis, and (2) interaction effects obtained through regression analysis of combination data. This segmentation allows the system to handle complex interaction assessment without requiring exhaustive testing of all possible combinations, thereby reducing device complexity while maintaining measurement precision.
Solution Approach 2:
The patent introduces regression analysis as an intermediary computational method that processes combination data to extract interaction effects. Instead of directly testing all combinations, the regression model acts as a mediator that infers interaction effects from a manageable subset of combination data, resolving the contradiction between comprehensive measurement and system complexity.
2Productivity
If a subset of element combinations is tested instead of all combinations, then productivity is improved, but measurement precision deteriorates due to inability to accurately assess interaction effects
Solution Approach 1:
The patent replaces the mechanical approach of directly testing all combinations with a computational regression analysis system. The regression model substitutes for exhaustive physical testing by mathematically extracting interaction effects from a subset of combination data, thereby maintaining measurement precision while significantly improving productivity.
Solution Approach 2:
The patent changes the analytical parameters from direct combination testing to regression-based interaction effect extraction. By transforming the problem from assessing raw combination data to analyzing regression coefficients that represent interaction effects, the system achieves accurate measurement with reduced testing requirements.
3Device complexity
If intuition is used to determine element combinations for testing, then device complexity is reduced, but reliability deteriorates due to imperfect analysis producing incorrect results
Solution Approach 1:
The patent implements a feedback mechanism where regression analysis of combination data provides quantitative information about interaction effects, which then feeds back into the element selection process. This feedback loop replaces intuitive judgment with data-driven insights, significantly improving the reliability of element combination assessment while maintaining analytical simplicity.
Data Source
AI summary
In a system and method for conjoint analysis, corresponding utility values are determined for a plurality of individual concept elements and for combinations of multiple concept elements, e.g., pair wise combinations of concept elements. A regression technique, e.g., least squares linear regression, may be used to determine the utility values.