Person:

Winship, Christopher

Among the nation’s elite colleges and universities, black graduation rates vary dramatically from institution to institution. Many sociologists have suggested that this is due not to differences in the student bodies but to institutional factors; however, this “institutional hypothesis” has not been recently examined empirically. We test the institutional hypothesis for a set of elite institutions using College and Beyond, a restricted dataset containing data for the entire 1989 cohort of 27 elite institutions, matched to institution-level data, employing HLM techniques. We ask three questions: Do institutional factors affect black students’ probability of graduation? Do they account for between-institution differences in black graduation? And are institutions where blacks have a high probability of graduation the same as or different from those where whites do? Testing for the effect of eight major institutional factors, we find, surprisingly, that only selectivity has a statistically significant effect. Contrary to common belief, selectivity improves black probabilities of graduation, and helps blacks more than it helps whites. It also accounts for roughly 38% of the between-institution variance in black graduation. Finally, we find that after controls, black and white probabilities of graduation across institutions are highly correlated (.909), such that institutions in which blacks are likely to graduate are those in which whites are likely to graduate, too. Findings suggest that researchers should examine other institutional factors in greater depth, as well as the role of pre-college preparation more seriously.

This article offers a new approach to the identification of age-period-cohort (APC) models that builds on Pearl's work on nonparametric causal models, in particular his front-door criterion for the identification of causal effects. The goal is to specify the mechanisms through which the age, period, and cohort variables affect the outcome and in doing so identify the model. This approach allows for a broader set of identification strategies than has typically been considered in the literature and, in many circumstances, goodness of fit tests are possible. The authors illustrate the utility of the approach by developing an APC model for political alienation.

Surface agreements about the social definition of a situation, or what Erving Goffman calls veneers of consensus, are necessary for social interaction to be coherent But why and when do social definitions change? In this article the author examines critical moments as points at which change may potentially take place. The author suggests that change is possible when a breach has occurred - an event, action, statement which is inconsistent with the current social definition. However, change depends on whether individuals ignore the breach, oppose it, or legitimize it. The author introduces the notion of an under-layment the attitudes, that is, the beliefs, knowledge, preferences, and normative commitments individuals have about a particular social situation. He argues that whether a particular veneer of consensus will change in the face of a breach is determined, in part, by the under-layment that supports that veneer.

When experimental designs are infeasible, researchers must resort to the use of observational data from surveys, censuses, and administrative records. Because assignment to the independent variables of observational data is usually nonrandom, the challenge of estimating causal effects with observational data can be formidable. In this chapter, we review the large literature produced primarily by statisticians and econometricians in the past two decades on the estimation of causal effects from observational data. We first review the now widely accepted counterfactual framework for the modeling of causal effects. After examining estimators, both old and new, that can be used to estimate causal effects from cross-sectional data, we present estimators that exploit the additional information furnished by longitudinal data. Because of the size and technical nature of the literature, we cannot offer a fully detailed and comprehensive presentation. Instead, we present only the main features of methods that are accessible and potentially of use to quantitatively oriented sociologists.

We provide a new statistical model developed from the alumni database at Northwestern University for identifying potential major gift donors. Our logit model with interactions predicts which individuals will give $10,000 or more over three years using CHAID (Chi-square Automatic Interaction Detection) combined with logit analysis. Our work goes beyond our earlier research (Lindahl and Winship, 1992) by providing a more effective means of selecting fund-raising prospects. The critical new component in our model is the inclusion of interactions between past giving and other variables. Specifically, we find that for individuals with different past giving records different variables are important. Our results show that prospects with a low past giving level will rarely give gifts over $10,000.