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Author and presenter Francis, Brian Authors Brian Francis; Lancaster <strong>University</strong>, 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
provides an alternative interpretation of the fitted model. The approach is also suitable for paired comparison data. Using age and sex as covariates, we found that a six class solution gave the best solution. Both age and sex were important in explaining the ranked responses. The six classes are interpretable in terms of different response profiles, and may be explained through omitted covariates such as degree of urbanisation and country.
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The ASA Spring Methodology Conferen
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the interplay of social background
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greatly inefficient. Only very few
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Data We use matched employer-employ
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KEY NOTE SPEAKER Snijders, Tom A.B.
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