Analyze MaxDiff — clear preferences, not scale bias

Analyze your MaxDiff survey (best-worst scaling) with count, aggregate logit and random-parameter logit — on the R engine, directly on your data. A clean ranking of what really matters. No export.

MaxDiff result as a preference ranking in a DataLion dashboard

DataLion analyzes MaxDiff surveys (best-worst scaling) with three procedures on the R engine: count analysis (best−worst), aggregate logit and random-parameter logit. This gives you a clear, scale-free ranking of how important individual items really are to respondents — directly on your data.

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Measure preferences without everything being "important"

Classic importance scales suffer from acquiescence bias: in the end (almost) everything is "very important". MaxDiff (best-worst scaling) forces respondents into real trade-offs — for each set they pick the most and the least important item.

The result is a scale-free, clear ranking that compares cleanly across countries and audiences. DataLion computes the analysis right in the tool.

  • Forced trade-offs instead of "everything important"
  • Scale-free, comparable ranking
  • Robust across countries and audiences
  • Analysis directly in DataLion
Scale-free MaxDiff ranking in DataLion

Count, aggregate logit and random-parameter logit

DataLion ships three MaxDiff procedures on the R engine: count analysis (best−worst) for a fast, intuitive ranking, aggregate logit for a robust overall estimate, and random-parameter logit, which accounts for heterogeneity across respondents.

You pick the procedure to match your need — from a quick count analysis to a nuanced estimate of individual preference differences.

  • Count analysis (best−worst) for fast rankings
  • Aggregate logit for robust overall values
  • Random-parameter logit for heterogeneity
  • Choose the procedure to match your need

From feature prioritization to claim tests

MaxDiff answers prioritization questions: which features, messages, benefits or claims matter most to respondents? Which can you drop? The ranking can be broken down by segment and analyzed further via crosstabs.

This page describes the analysis technique. For the full use case — from study setup to analysis — see the Conjoint & MaxDiff solution page.

  • Feature, benefit and claim prioritization
  • Break the ranking down by segment
  • Process further in crosstabs and charts
  • Full use case under Conjoint & MaxDiff
MaxDiff preferences by segment in a DataLion dashboard

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More analysis features

Common questions about MaxDiff analysis

Which MaxDiff procedures does DataLion offer?
Three on the R engine: count analysis (best−worst) for a fast ranking, aggregate logit for a robust overall estimate, and random-parameter logit, which accounts for heterogeneity across respondents.
What is the advantage of MaxDiff over importance scales?
MaxDiff (best-worst scaling) forces real trade-offs instead of respondents rating everything as "important". The result is a scale-free, clear ranking that compares cleanly across audiences and countries.
What is the difference from the Conjoint & MaxDiff page?
This page describes the analysis technique — the three procedures. The Conjoint & MaxDiff solution page shows the full use case from study setup through fielding to analysis.
Can I analyze MaxDiff results by segment?
Yes. The preference ranking can be broken down by segment and processed further via the crosstabs and charts in DataLion.
Do I need to export data or know R?
No. The MaxDiff procedures run on the R engine in the background, directly on your data. You pick the procedure with a click, with no R code and no data export.

Analyze your MaxDiff study

Try DataLion free: count, aggregate logit and random-parameter logit directly on your data — with no export. Or book a personal demo.