Title
|
|
|
|
Bayesian optimal designs for discrete choice experiments with partial profiles
|
|
Author
|
|
|
|
|
|
Abstract
|
|
|
|
In a discrete choice experiment, each respondent chooses the best product or service sequentially from many groups or choice sets of alternative goods. The alternatives are described by levels of a set of prede ned attributes and are also referred to as pro les. Respondents often nd it dicult to trade o prospective goods when every attribute of the oering changes in each comparison. Especially in studies involving many attributes, respondents get overloaded by the complexity of the choice task. To overcome respon- dent fatigue, it is better to simplify the choice tasks by holding the levels of some of the attributes constant in every choice set. The resulting designs are called partial pro le designs. In this paper, we construct D-optimal par- tial pro le designs for estimating main-eects models. We use a Bayesian design algorithm that integrates the D-optimality criterion over a prior dis- tribution of likely parameter values. To determine the constant attributes in each choice set, we generalize the approach that makes use of balanced incomplete block designs. Our algorithm is very exible because it produces partial pro le designs of any choice set size and allows for attributes with any number of levels and any number of constant attributes. We provide an illustration in which we make recommendations that balance the loss of statistical information and the burden imposed on the respondents. Keywords: discrete choice experiments; Bayesian D-optimal design; partial pro les; lexicographic choice behavior; attribute balance; coordinate-exchange algorithm |
|
|
Language
|
|
|
|
English
|
|
Source (journal)
|
|
|
|
Journal of Choice Modelling. - -
|
|
Publication
|
|
|
|
2011
|
|
ISSN
|
|
|
|
1755-5345
|
|
DOI
|
|
|
|
10.1016/S1755-5345(13)70042-3
|
|
Volume/pages
|
|
|
|
4
:3
(2011)
, p. 52-74
|
|
Full text (Publisher's DOI)
|
|
|
|
|
|
Full text (open access)
|
|
|
|
|
|