A comparison of best-worst scaling marginal and rank methods

ABSTRACT

This study compares the marginal and the rank methods for analysing best-worst scaling case 2 data using a simulated and an empirical example dataset. Simulation results suggest the rank method improves accuracy of estimates compared to the marginal method as measured by bias and mean square error. The rank method reduced bias on average by 48% across all coefficient estimates as compared to the marginal-CL method. Results from the empirical example align with those of the simulation and added robustness to the findings.

KEYWORDS:

• Best-worst scaling
• Monte Carlo experiment
• marginal method
• rank methods

JEL CLASSIFICATION:

• C25
• C35

Acknowledgement

This research was supported by the Sparks Chair in Agricultural Sciences & Natural Resources, Oklahoma State University.

Disclosure statement

No potential conflict of interest was reported by the authors.