Socially organized sentiments: Exploring the link between religious density and protest mobilization, 1960-1995.

Extensive research has shown individual religiosity to have an impact upon U.S. protest participation. But very little work has examined the role of religious density in a community on the likelihood of protest mobilization. Our research links the religious density across 62 counties in New York State to various protest mobilization issues during the period of 1960-1995. In this research, we develop a theory of socially organized sentiments to examine religious influences on overall protest event mobilizations in local communities, a specific example of a more general theory that can link community structure to multiple forms of civic engagement. The impact of various religious traditions is assessed by using measures for the density of religious population per congregation of three religious traditions-Mainline Protestantism, Evangelical Protestantism and Catholicism. The analysis also assesses the likelihood of mobilization concerning four specific issues-African-American civil rights, gender, anti-nuclear/peace, and anti-poverty movements.

Understanding selection bias, time-lags and measurement bias in secondary data sources: Putting the Encyclopedia of Associations database in broader context.

Secondary data gathered for purposes other than research play an important role in the social sciences. A recent data release has made an important source of publicly available data on associational interests, the Encyclopedia of Associations (EA), readily accessible to scholars (www.policyagendas.org). In this paper we introduce these new data and systematically investigate issues of lag between events and subsequent reporting in the EA, as these have important but under-appreciated effects on time-series statistical models. We further analyze the accuracy and coverage of the database in numerous ways. Our study serves as a guide to potential users of this database, but we also reflect upon a number of issues that should concern all researchers who use secondary data such as newspaper records, IRS reports and FBI Uniform Crime Reports.

Using ZIP code business patterns data to measure alcohol outlet density.

Some states maintain high-quality alcohol outlet databases but quality varies by state, making comprehensive comparative analysis across US communities difficult. This study assesses the adequacy of using ZIP Code Business Patterns (ZIP-BP) data on establishments as estimates of the number of alcohol outlets by ZIP code. Specifically we compare ZIP-BP alcohol outlet counts with high-quality data from state and local records surrounding 44 college campus communities across 10 states plus the District of Columbia. Results show that a composite measure is strongly correlated (R=0.89) with counts of alcohol outlets generated from official state records. Analyses based on Generalized Estimation Equation models show that community and contextual factors have little impact on the concordance between the two data sources. There are also minimal inter-state differences in the level of agreement. To validate the use of a convenient secondary data set (ZIP-BP) it is important to have a high correlation with the more complex, high quality and more costly data product (i.e., datasets based on the acquisition and geocoding of state and local records) and then to clearly demonstrate that the discrepancy between the two to be unrelated to relevant explanatory variables. Thus our overall findings support the adequacy of using a conveniently available data set (ZIP-BP data) to estimate alcohol outlet densities in ZIP code areas in future research.

Understanding Activist Leadership Effort in the Movement Opposing Drinking and Driving.

Why do some social movement leaders work harder than others? And, how does gender affect the patterns we uncover? Utilizing historical case study evidence of local chapters in the emerging movement opposing drinking and driving we are able to develop and test theoretical expectations about predictors of weekly effort among MADD and RID leaders. Taken together, our model explains 45 percent of the variation in leadership effort. We find bureaucratic complexity and victim support activities are more powerful predictors of effort than are individual leader characteristics, although all are important. Further analysis reveals that gender almost wholly conditions the strong effect of bureaucratic complexity on leadership effort so that increasingly complex chapter structures are associated with substantial increases in work hours for women but not men.