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MaxDiff is a statistical method invented by Jordan Louviere in 1987 while on the faculty at the University of Alberta. The first working papers and publications occurred in the early 1990s. With MaxDiff, survey respondents are shown a set of the possible items and are asked to indicate the best and worst items (or most and least important, or most and least appealing , etc.). MaxDiff represents an extension of Thurstone's Law of Comparative Judgement. According to Louviere, MaxDiff assumes that respondents evaluate all possible pairs of items within the displayed set and choose the pair that reflects the maximum difference in preference or importance. MaxDiff may be thought of as a more sophisticated extension of the method of Paired Comparisons. Consider a set in which a respondent evaluates four items: A, B, C and D. If the respondent says that A is best and D is worst, these two responses inform us on five of six possible implied paired comparisons:

A>B, A>C, A>D, B>D, C>D

The only paired comparison that cannot be inferred is B vs. C. In a choice among five items, MaxDiff questioning informs on seven of ten implied paired comparisons. MaxDiff questionnaires are relatively easy for most respondents to understand. Furthermore, humans are much better at judging items at extremes than in discriminating among items of middling importance or preference. And since the responses involve choices of items rather than expressing strength of preference, there is no opportunity for scale use bias.

Process

The basic steps are:

select products to be tested
show products to potential consumers (textually or visually)
respondents choose the best and worst from each task
input the data from a representative sample of potential customers into a statistical software program and choose the MaxDiff analysis procedure. The software will produce utility functions for each of the features. In addition to utility scores, you can also request raw counts which will simply sum the total number of times a product was selected as best and worst.

Why Use Maximum Difference Scaling?

Max Diff is an antidote to standard rating scales or importance scales. Respondents find these ratings scales very easy but they do tend to deliver results which indicate that everything is "quite important", making the data not especially actionable. Max Diff on the other hand forces respondents to make choices between options, whilst still, at the end of the day delivering rankings showing the relative importance of the items being rated.

Analysis

Any number of algorithms may be used to estimate utility functions. One of the most popular methods is the Hierarchical Bayesian procedures that operate on choice data. These utility functions indicate the perceived value of the product on an individual level and how sensitive consumer perceptions and preferences are to changes in product features. The Hierarchical Bayes model is beneficial because it allows for borrowing across the data.

Finding related topics

External sources

Cohen, Steve and Paul Markowitz (2002), “Renewing Market Segmentation: Some New Tools to Correct Old Problems,” ESOMAR 2002 Congress Proceedings, 595-612, ESOMAR: Amsterdam, The Netherlands.
Cohen, Steve (2003), “Maximum Difference Scaling: Improved Measures of Importance and Preference for Segmentation,” 2003 Sawtooth Software Conference Proceedings, Sequim, WA.
Louviere, J. J. (1991), “Best-Worst Scaling: A Model for the Largest Difference Judgments,” Working Paper, University of Alberta.
Thurstone, L. L. (1927), “A Law of Comparative Judgment,” Psychological Review, 4, 273-286.
The MaxDiff System Technical Paper

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 The basic steps are:
 * select products to be tested
-* show product products to potential consumers (textually or visually)
+* show products to potential consumers (textually or visually)
 * respondents choose the best and worst from each task
 * input the data from a representative sample of potential customers into a [[List of statistical packages|statistical software]] program and choose the MaxDiff analysis procedure. The software will produce utility functions for each of the features. In addition to utility scores, you can also request raw counts which will simply sum the total number of times a product was selected as best and worst.