Stochastic media theory is a class of stochastic models of persuasion developed by Falmagne and his colleagues. These models assume that personal preferences are represented by rankings (e.g., (strict) weak orders, semiorders) that may change over time, under the influence of "tokens" of information in the environment. Empirical applications of some weak order implementations to the U.S. presidential election panel data have been discussed in Regenwetter, Falmagne, and Grofman (1999) and Hsu, Regenwetter, and Falmagne (2005). Recently, Hsu and Regenwetter (in press) also tried out a simple semiorder implementation to the same data sets. The election panel data were recorded using the Feeling Thermometer ratings, which have a natural transformation into weak orders. No such transformation is available in the case of semiorders, because each respondent may have a personal threshold. To deal with such situation, we investigate a 'random threshold' probabilistic response mechanism for the semiorder model. This response mechanism, along with the semiorder model, is applied to the 1992, 1996, and 2000 U.S. presidential election panel data. |