Nice to Know You? Testing Contact,Cultural, and Group Threat Theories of Anti‐Black and Anti‐Hispanic Stereotypes*

Objective. Many racial/ethnic policies in the United States—from desegregation to affirmative action policies—presume that contact improves racial/ethnic relations. Most research, however, tests related theories in isolation from one another and focuses on black‐white contact. This article tests contact, cultural, and group threat theories to learn how contact in different interactive settings affects whites' stereotypes of blacks and Hispanics, now the largest minority group in the country. Method. We use multi‐level modeling on 2000 General Social Survey data linked to Census 2000 metropolitan statistical area/county‐level data. Results. Net of the mixed effects of regional culture and racial/ethnic composition, contact in certain interactive settings ameliorates anti‐black and anti‐Hispanic stereotypes. Conclusions. Cultural and group threat theories better explain anti‐black stereotypes than anti‐Hispanic stereotypes, but as contact theory suggests, stereotypes can be overcome with relatively superficial contact under the right conditions. Results provide qualified justification for the preservation of desegregation and affirmative action policies.     

Inclusion of binary proxy variables in logistic regression improves treatment effect estimation in observational studies in the presence of binary unmeasured confounding variables

We present a simulation study and application that shows inclusion of binary proxy variables related to binary unmeasured confounders improves the estimate of a related treatment effect in binary logistic regression. The simulation study included 60,000 randomly generated parameter scenarios of sample size 10,000 across six different simulation structures. We assessed bias by comparing the probability of finding the expected treatment effect relative to the modeled treatment effect with and without the proxy variable. Inclusion of a proxy variable in the logistic regression model significantly reduced the bias of the treatment or exposure effect when compared to logistic regression without the proxy variable. Including proxy variables in the logistic regression model improves the estimation of the treatment effect at weak, moderate, and strong association with unmeasured confounders and the outcome, treatment, or proxy variables. Comparative advantages held for weakly and strongly collapsible situations, as the number of unmeasured confounders increased, and as the number of proxy variables adjusted for increased.