Maximum Difference Scaling (MaxDiff) and Q-Sort methodologies provide the most accurate ways to measure relative importance of features, benefits, or concepts by forcing respondents to make clear trade-off decisions. Below are details regarding the different types of MaxDiff scaling and Q-Sort used by PROOF Insights, depending on your unique research needs.
Traditional best-worst scaling where respondents choose the most and least important items from sets, providing clean ratio-level importance scores.
Offers the ability to evaluate the importance of attributes in the context of a profile that includes multiple attributes and respondents rate best and worst profiles.
Similar to Choice-Based Conjoint (CBC), respondents compare multiple profiles and rate best and worst for each set of profiles.
Structured ranking approach that forces distribution of items across importance categories, revealing nuanced preference patterns and priorities.
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These methodologies provide superior measurement precision by eliminating common biases found in traditional rating scales and survey approaches.
Forces clear trade-offs between options, eliminating scale bias, acquiescence bias, and the tendency to rate everything as important.
MaxDiff produces interval data which can be recoded to ratio-level data (for anchored MaxDiff), both allowing for meaningful insights and comparisons.
Provides superior ability to distinguish between items of similar importance, revealing subtle but meaningful preference differences.
Intuitive task that mimics natural decision-making processes, resulting in higher engagement and better data quality.
Adaptable to various research contexts from product features to brand attributes, messaging elements, and strategic priorities.
Generates clear, prioritized rankings that directly inform strategic decisions and resource allocation across business functions.
Our systematic approach ensures optimal study design and accurate measurement of relative importance across all your key attributes or concepts.
Collaborate to identify and refine the specific items, features, or concepts to be ranked, ensuring comprehensive coverage of decision factors.
Create balanced, efficient experimental designs that maximize information while minimizing respondent burden and task complexity.
Apply Hierarchical Bayes modeling and advanced analytics to generate precise importance scores and segment-specific rankings.