Quotation Francis, Brian, Dittrich, Regina, Hatzinger, Reinhold. 2010. Modeling heterogeneity in ranked responses by non-parametric maximum likelihood: How do Europeans get their scientific knowledge?. The Annals of Applied Statistics 4 (4): 2181-2202.


RIS


BibTeX

Abstract

This paper is motivated by a Eurobarometer survey on science knowledge. As part of the survey, respondents were asked to rank sources of science information in order of importance. The official statistical analysis of these data however failed to use the complete ranking information. We instead propose a method which treats ranked data as a set of paired comparisons which places the problem in the standard framework of generalized linear models and also allows respondent covariates to be incorporated. An extension is proposed to allow for heterogeneity in the ranked responses. The resulting model uses a nonparametric formulation of the random effects structure, fitted using the EM algorithm. Each mass point is multivalued, with a parameter for each item. The resultant model is equivalent to a covariate latent class model, where the latent class profiles are provided by the mass point components and the covariates act on the class profiles. This provides an alternative interpretation of the fitted model. The approach is also suitable for paired comparison data.

Tags

Press 'enter' for creating the tag

Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Annals of Applied Statistics
Citation Index SCI
WU-Journal-Rating new FIN-A, VW-B
Language English
Title Modeling heterogeneity in ranked responses by non-parametric maximum likelihood: How do Europeans get their scientific knowledge?
Volume 4
Number 4
Year 2010
Page from 2181
Page to 2202
URL http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.aoas/1294167815

Associations

People
Dittrich, Regina (Former researcher)
Hatzinger, Reinhold (Former researcher)
External
Francis, Brian (Fylde College, Lancaster University, United Kingdom)
Organization
Institute for Statistics and Mathematics IN (Details)
Google Scholar: Search