Ordinal ranking as a method for assessing real-world proportional representations

AbstractAcross two experiments, we use ordinal ranking to examine the processing and representations involved in the estimation of large-scale, real-world proportions. Specifically, in two experiments people estimated two kinds of important real-world proportions: the demographic makeup of their communities, and spending by the U.S. Federal government. Our goal was to assess the metric scaling properties that characterize perceptions of these quantities. Previous work in numerical proportions has posited logarithmic or linear representations (Opfer & Siegler, 2007), or linear representations with task-dependent rescaling (Barth & Paladino, 2011; Cohen & Blanc-Goldhammer, 2011). The current context differs markedly from this prior work in that the values we are examining are not explicitly presented to participants, nor directly experienced, but must be estimated on the basis of masses of complex experiences. We find that people largely rely on mixed representations that emphasize log-odds transformations of these vaguely known, but socially important values.


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