Here - Tilburg University

Author and presenter
Francis, Brian
Authors
Brian Francis; Lancaster University, Regina Dittrich and Reinhold Hatzinger;
Wirtschaftsuniversitaet, Vienna
Title
Modelling ranked survey data - a new approach accounting for covariates and
latent heterogeneity.
Abstract
This talk focuses on the analysis of ranked survey response data and 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 examined only the
first two rank positions, and the percentage of times a source was mentioned in
either the first or second position was reported. This failed to use all the
information available in the dataset.
Another issue concerns the heterogeneity of ranked responses. We might
suppose that there is variability in the ranks across individuals which can be
explained either through known covariates or through a random effects
formulation which would incorporate the effect of unknown and unmeasured
covariates.
In this talk we 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. This formulation also allows respondent covariates to
be incorporated. The model can be interpreted through the worths of each item,
and the effects of covariates on the worths.
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 and masspoint. 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