Best–worst scaling: An introduction and initial comparison with monadic rating for preference elicitation with food products Sara R. Jaeger a, * ,1 , Anne S. Jørgensen b , Margit D. Aaslyng c , Wender L.P. Bredie b a HortResearch, Mt. Albert Road, Private Bag 92169, Auckland 1142, New Zealand b University of Copenhagen, Faculty of Life Sciences, Department of Food Science, Rolighedsvej 30, DK-1958 Frederiksberg C, Denmark c Danish Meat Research Institute, Maglegaardsvej 2, DK-4000 Roskilde, Denmark article info Article history: Received 7 September 2006 Received in revised form 9 March 2008 Accepted 12 March 2008 Available online 18 March 2008 Keywords: Maximum difference scaling Discrete choice experiment Unstructured bi-polar line scale Research methodology Preference mapping Pork Young adults Denmark abstract This paper introduces the use of best–worst scaling to elicit taste-based preferences and presents a com- parison of this scaling methodology with monadic preference ratings elicited on an unstructured line scale. Best–worst scaling (BWS) is a discrete choice task that forces respondents to make a discriminating choice among the samples under investigation by requiring them to select both the best and the worst option in an available (sub)set of samples. In an empirical case study that concerns consumer preferences for minced pork patties, it is found that the results from the two methods are highly correlated. However, there is some evidence to suggest that preference data elicited using best–worst scaling may better enable discovery of differences in sample preferences without being a more difficult test for consumers to take part in. Some of the advantages and disadvantages associated with the application of best–worst scaling in hedonic measurement are noted and discussed. Ó 2008 Elsevier Ltd. All rights reserved. 1. Introduction 1.1. Motivation for the research In consumer research, many of us rely heavily on scaling of mul- tiple ‘‘entities” (e.g., agreement or disagreement with a set of Likert statements, importance of various factors for food choice, or liking of product samples) and the monadic rating of such multiple ‘‘enti- ties” on category or line scales is standard practice. In fact, the use of monadic rating for the measurement of multiple items is so well established that consideration is rarely given to whether alterna- tive methodologies exist and warrant use. The purpose of this pa- per is to introduce one alternative and propose that the methodology of best–worst scaling (also known as maximum dif- ference scaling or maxdiff) warrants inclusion in the Sensory and Consumer Scientist’s toolbox. This is achieved through two aims: (1) presenting the methodology of best–worst scaling and begin- ning to assess its pros and cons in food-related consumer research, and (2) presenting a case study that compares consumer prefer- ence data elicited using best–worst scaling to acceptance ratings elicited on an unstructured line scale. The reminder of this paper is structured as follows: Section 1.2 introduces the methodology of best worst scaling, considering some theoretical and applied aspects, previous applications and approaches to data analysis. The empirical case study that elicits and compares consumer acceptability data using monadic rating and best–worst scaling is introduced in Section 1.3. Briefly, accept- ability for six samples of minced pork patties varying in terms of how they were cooked by pan frying is measured in a convenience sample of young adult Danish consumers. The methodology is de- tailed in Section 2 and results are presented in Section 3. This pre- sents the findings of a series of analyses performed to compare preference data elicited using best–worst scaling and monadic rat- ing, and explore whether these two data elicitation methods differ: (i) in ability to uncover differences in sample acceptability, and (ii) perceived test difficulty. The potential for wider application of best–worst scaling in food-related consumer research is discussed in Section 4, which also outlines some of the many avenues for re- search relating to best–worst scaling. 1.2. The methodology of best–worst scaling 1.2.1. Introductory example The methodology of best–worst scaling (BWS) is most easily introduced by way of an example, and for simplicity, consider a study involving four samples (A–D). An experimental design is 0950-3293/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodqual.2008.03.002 * Corresponding author. E-mail address: sjaeger@hortresearch.co.nz (S.R. Jaeger). 1 This work was initiated while S.R. Jaeger was Senior Lecturer at the Department of Marketing (University of Auckland, NZ). Food Quality and Preference 19 (2008) 579–588 Contents lists available at ScienceDirect Food Quality and Preference journal homepage: www.elsevier.com/locate/foodqual