Contact - axyzanalytics.ch

Contact us

Our Offices

Australia

HWT Tower,  
40 City Road 6F, Southbank
VIC 3006, Australia  

+61 3 7053 1232

Switzerland

Voie du Chariot 3, 1003 Lausanne,
Switzerland

+41 21 547 00 69

United States of America

420 Throckmorton St Suite 200,
Fort Worth, TX 76102, United
States

+1 682 428 0203

MaxDiff, or Best-Worst Scaling (BWS), is a remarkably insightful and productive measurement method used widely by researchers and practitioners across social science, business, and other disciplines. It is often preferred over traditional rating scales because it is effective at capturing preferences with known measurement properties.

Here is a comprehensive explanation of Best-Worst Scaling (BWS) and its origins as MaxDiff:

Definition and Core Mechanism

Best-Worst Scaling (BWS) is defined as an extension of the method of paired comparison to multiple choices. The fundamental task requires participants to evaluate a collection, or “set,” containing three or more items or options. From this set, the participant must choose:

The best (or most attractive/most preferred) option.
The worst (or least attractive/least preferred) option.

The terms “best” and “worst” are considered metaphors representing the extremes of an underlying, latent, subjective continuum of interest.

By collecting both extreme choices from a set, BWS obtains extra choice information. Crucially, this method gathers valuable information about both the attractive options (via the “best” choices) and the less attractive options (via the “worst” choices), providing richer insights into the respondent’s utility function.

Nomenclature and History (MaxDiff vs. BWS)

The terms MaxDiff and BWS refer to the same technique, though BWS is now the generally accepted name, particularly within academic circles.

Origin as MaxDiff: The approach was pioneered by Jordan J. Louviere, who developed it in 1987. He initially called it “maximum difference scaling” (or “MaxDiff”).
Hypothesized Process: This original name reflected Louviere’s hypothesis that the underlying decision process involved choosing the pair of options that exhibited the largest subjective difference on the latent scale of interest. The two chosen options (best and worst) would thus be maximally different.
Shift to BWS: The name was subsequently changed to Best-Worst Scaling because subsequent academic research demonstrated that individuals generally do not use a maximum difference choice process to perform the task. BWS is considered a more inclusive general term, as MaxDiff only represents one of several possible psychological processes a person might use to provide best-worst data.

Advantages over Rating Scales

A significant benefit of using BWS is its ability to avoid many problems inherent to traditional rating scales.

Forced Discrimination: BWS forces respondents to discriminate between items by selecting only one best and one worst option, thereby reducing response biases (such as acquiescence bias or consistently using only mid-points or end-points of a scale).
Measurement Properties: BWS offers a theoretical framework to measure latent, subjective quantities. The derived measurement values possess known properties—specifically, they often produce ratio-level scales (if specific conditions regarding choice probabilities are met).
Simplicity and Quality: The task itself is simple and undemanding for respondents, which typically improves data quality.

Theoretical and Analytical Foundation

The conceptual framework underpinning BWS is random utility theory (RUT). RUT assumes that when people choose repeatedly, their observed choice frequencies indicate how much they value the items under consideration.

Analytically, BWS often involves straightforward calculations, making it accessible even for practitioners with minimal prior knowledge. A simple way to summarize the data is by calculating best-minus-worst scores for each option, which is the count of times an item was chosen as “best” minus the count of times it was chosen as “worst”.

These best-minus-worst scores empirically correlate highly with estimates from more sophisticated models, often serving as a sufficient statistic for conditional logit regression models if the MaxDiff model holds.

Categorization of Applications

BWS applications are typically divided into three main classes, differing based on the complexity of the items being evaluated:

Case	Name	Description of Items	Choice Task Focus
Case 1	The Object Case	Simple items, objects, statements, or people.	Measuring a list of items on a single underlying subjective scale (e.g., measuring priority, importance, or agreement).
Case 2	The Profile Case	Attribute levels described/displayed as a single profile (a combination of attribute levels).	Choosing the best and worst attribute levelswithin a single profile.
Case 3	The Multi-Profile Case	Sets of three or more profiles (each profile being a complex combination of attributes and levels, akin to a traditional DCE).	Choosing the best and worst whole profiles (options) among multiple choices.

In summary, BWS is an invaluable choice-based measurement method that leverages the finding that individuals are highly reliable and accurate when identifying extreme options. It moves beyond the MaxDiff hypothesis to provide a robust framework for measuring subjective preferences.

Reference: Louviere, J. J., Flynn, T. N. & Marley, A. A. J. Best–Worst Scaling:Theory, Methods, and Applications (Cambridge Univ. Press, 2015).

Contact us

Our Offices

Australia

Switzerland

United States of America

About Us

OUR SOLUTIONS

Useful Links

Contact us

Our Offices

Australia

Switzerland

United States of America

About Us

OUR SOLUTIONS

Useful Links

How does it work?

1. Reach out

2.⁠ ⁠Free Consultation

3.⁠ ⁠Approval

4. Results