Forecasting presidential elections: A comparison of naive models

Our primary aim is to forecast, rather than explain, presidential election results, using aggregate time series data from the post-World War II period. More particularly, we seek prediction of the presidential winner well before the election actually occurs. After comparing the performance of several naive blvariate models based on economic performance, international involvement, political experience, and presidential popularity, we go on to formulate a multivariate model. This economy-popularity regression model rather accurately forecasts the winner 6 months in advance of the election, by employing spring measures of presidential popularity and the growth rate in real GNP per capita. Furthermore, the model's performance, both ex post facto and prior to the election, compares favorably with the Gallup final preelection poll taken only a few days before the election.     

... And justice for all

African American, Hispanic American, Asian American, and European American students rated their procedural preferences in response to a hypothetical conflict scenario and then recalled a real dispute in which they had been involved. Subjects of all four ethnicities and of both genders preferred persuasion and negotiation to other options. There were significant, ethnic and gender differences in preferences, as well as differences for the nature of the relationship and the nature of the issue, but these differences were small in comparison to the overall pattern of procedural preferences. Reports of actual procedure use also showed differences in procedure use across genders, ethnicities, and relationship type, but the differences were relatively small. Procedural fairness was the strongest predictor of both procedural preference and affect toward actual procedure use.