EconPapers    
Economics at your fingertips  
 

Homogeneous versus heterogeneous designs for stated choice experiments: Ain't homogeneous designs all bad?

Roselinde Kessels

Journal of choice modelling, 2016, vol. 21, issue C, 2-9

Abstract: Some recent attempts on constructing heterogeneous designs for stated choice experiments where different respondents or groups of respondents get different subdesigns have proven successful. Compared to homogeneous designs where all respondents get the same choice sets, heterogeneous designs allow for more variation in the attribute levels resulting in a larger amount of information on the respondents' preferences. Homogeneous designs have remained popular, however, because they are easier to generate and implement. In this paper, the question is raised about when homogeneous designs perform almost as well as heterogeneous designs under the Bayesian multinomial logit design framework. A simulation study is presented to identify the situations where the losses in estimation efficiency from using a homogeneous design are small and where they are large. When the residual degrees of freedom from using a homogeneous design are large and, to a lesser extent, the number of attributes and attribute levels are small, the efficiency losses are negligible and the use of a homogeneous design can be justified.

Keywords: Stated choice experiments; Homogeneous design; Heterogeneous design; Different subdesigns; Bayesian D-optimality; DB-efficiency (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1755534515300889
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:eejocm:v:21:y:2016:i:c:p:2-9

DOI: 10.1016/j.jocm.2016.08.001

Access Statistics for this article

Journal of choice modelling is currently edited by S. Hess and J.M. Rose

More articles in Journal of choice modelling from Elsevier
Bibliographic data for series maintained by Catherine Liu ().

 
Page updated 2025-03-19
Handle: RePEc:eee:eejocm:v:21:y:2016:i:c:p:2-9