Mavin Score Segmentation for Likert Scale Analysis Precision
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Solution Overview
Problem
Conventional methods for analyzing Likert scale data in surveys are inadequate as they fail to differentiate between response options, ignore the difference between satisfied and very satisfied customers, and lack a defensible standard for judging performance, leading to inaccurate and unreliable results.
Innovation Solution
The Mavin system assigns unique segment values to each response option in a fully anchored Likert scale, calculates a Mavin Score based on these values, and uses the mean score for comparison, allowing for valid and interpretable results that differentiate between respondent groups and set a defensible standard for evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to analyze Likert scale data, then the analysis process is simple, but the results are inaccurate and fail to differentiate between response options
Solution Approach 1:
The patent segments the Likert scale response options into distinct categories (e.g., Strongly Disagree, Disagree, Neutral, Agree, Strongly Agree) and assigns unique segment values to each option. This segmentation allows the system to differentiate between response options and calculate meaningful scores that reflect the intensity of respondent feelings, thereby improving measurement precision without requiring overly complex analysis methods.
Solution Approach 2:
The patent changes the parameter of response value representation by assigning numerical segment values (e.g., 1-5 or 1-7) to each Likert scale option. This parameter transformation enables quantitative analysis of ordinal data, allowing for accurate calculation of mean scores and statistical comparisons while maintaining system simplicity through straightforward mathematical operations.
2Measurement precision
If Likert scale responses are combined into a composite score, then quantitative measurement is achieved, but the difference between satisfied and very satisfied customers is ignored
Solution Approach 1:
The patent applies local quality by assigning different weights or segment values to different response options within the Likert scale. For example, 'Very Satisfied' might be assigned a higher segment value than 'Satisfied', preserving the local distinction between satisfaction levels in the composite score calculation. This allows the system to maintain discrimination between satisfaction levels while still producing an overall quantitative measure.
Solution Approach 2:
The patent introduces dynamic scaling where the segment values assigned to Likert scale options can be adjusted based on the specific survey context, industry standards, or organizational goals. This dynamic approach allows the scoring system to adapt to different situations while maintaining the ability to distinguish between satisfaction levels, balancing precision with flexibility.
3Reliability
If traditional survey analysis is used, then data collection is straightforward, but no defensible standard for judging performance is established
Solution Approach 1:
The patent establishes defensible performance standards through preliminary action by pre-defining segment values, scoring criteria, and interpretation guidelines before conducting the survey analysis. This preliminary framework ensures that performance evaluations are consistent, reliable, and defensible, as the standards are set in advance based on theoretical considerations and best practices rather than being determined ad hoc after data collection.
Data Source
AI summary
The Mavin systems and computer-implemented processes of the invention analyze, score, and report the results from Likert scale survey questions. The systems and methods address three weaknesses in traditional Likert scale analyses by providing: (1) a scoring procedure that is sensitive to all levels of response; (2) a determination and designation of a standard score used to determine whether the results meet that standard; and (3) a scoring process used to determine the degree to which a given score exceeds or fails to meet this standard. In addition, the Mavin systems and methods support recalculation and adjustment to the scoring model when available data support such adjustments. Further, the Mavin systems and methods incorporate flexible non-linear segment intervals and determine evidence-based adjusted response segment values to determine adjusted Mavin scores and provide actionable survey results.