A Unified Framework for the Comparison of Treatments with Ordinal Responses

Different latent variable models have been used to analyze ordinal categorical data which can be conceptualized as manifestations of an unobserved continuous variable. In this paper, we propose a unified framework based on a general latent variable model for the comparison of treatments with ordinal responses. The latent variable model is built upon the location-scale family and is rich enough to include many important existing models for analyzing ordinal categorical variables, including the proportional odds model, the ordered probit-type model, and the proportional hazards model. A flexible estimation procedure is proposed for the identification and estimation of the general latent variable model, which allows for the location and scale parameters to be freely estimated. The framework advances the existing methods by enabling many other popular models for analyzing continuous variables to be used to analyze ordinal categorical data, thus allowing for important statistical inferences such as location and/or dispersion comparisons among treatments to be conveniently drawn. Analysis on real data sets is used to illustrate the proposed methods.     

Does Social Protest Shake People’s Justice Beliefs? It Depends on the Level of Group Identification

We examined whether social protests will shake people's justice belief, and its potential mechanisms. Participants were reminded of an occurrence of a social protest (vs. an ordinary event) and their justice belief toward social system as well as attitudes toward the protest was measured. Moreover, participants’ perceived group identification with the protesters was measured in Experiment 1 and manipulated in Experiment 2. The results revealed that participants with greater group identification perceived the social system as more just, and were less supportive of the protest than those with low group identification. The implications of these findings were discussed.