09.05.2022 Neuer Preprint: Cultural Consensus Theory for Two-Dimensional Data

Cultural Consensus Theory for Two-Dimensional Data:
Expertise-Weighted Aggregation of Location Judgments

Maren Mayer & Daniel W. Heck

Cultural consensus theory is a model-based approach for analyzing responses of informants when correct answers are unknown. The model provides aggregate estimates of the latent consensus knowledge at the group level while accounting for heterogeneity both with respect to informants’ competence and items’ difficulty. We develop a specific version of cultural consensus theory for two-dimensional continuous judgments as obtained when asking informants to locate a set of unknown sites on a geographic map. The new model is fitted using hierarchical Bayesian modeling, with a simulation study indicating satisfactory parameter recovery. We also assess the accuracy of the aggregate location estimates by comparing the new model against simply computing the unweighted average of the informant’s judgments. A simulation study shows that, due to weighting judgments by the inferred competence of the informants, cultural consensus theory provides more accurate location estimates than unweighted averaging. This result is also supported in an empirical study in which individuals judged the location of European cities on maps.

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