| Cultural consensus theory (CCT) as a formal mathematical/statistical model can effectively handle statistical issues related to opinion aggregation, in which individual competencies are weighted to estimate the consensus answers for questions. This dissertation research extended and customized the CCT model to a cross-cultural situation with respondents from two cultures. Respondents were experts with expertise in school psychology, and it is assumed that the experts within same culture share much common knowledge on understanding culture-related concepts. A proposed cross-cultural consensus model (CCCM) was analyzed with Markov Chain Monte Carlo method and illustrated with an empirical study of experts' understanding of respect. This method gives marginal posterior density estimates for parameters, namely, competency and consensus in the study of understanding respect. Then by comparing competency and consensus answers across cultures, we were able to infer the cultural differences. It was documented that the CCCM model provided new perspectives on experts' opinion aggregation and cross-culture comparison. |
