In this paper, we describe a new technique for identifying cultural consensus called Cultural Mixture Modeling (CMM). This technique adopts finite mixture modeling, and introduces a new probabilistic formulation of agreement, which we call thestrongconsensusmodel. Weusethistechniquetoexamine the cultural belief data from Weller (1983; 1984) and social network data from Krackhardt (1987). We show that CMM can go beyond classic models of consensus and identify situations in which multiple distinct but disagreeing beliefs exist between subgroups of individuals. By identifying groups of shared belief, CMM offers a practical and useful technique for understanding and characterizing how socio-cultural factorsinfluence our beliefs and attitudes
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