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Routes to economic development attract considerable attention in community and rural sociology. Social scientists draw increasingly on studies of social capital and environmental surroundings as they examine the factors that facilitate and inhibit economic development. However, few empirical analyses exist that analyze the impact of the combination of social infrastructure and natural capital on different forms of economic development such as on industrial recruitment and self‐development. Using data collected from six communities in Washington State, the interaction of a community's social infrastructure and natural capital on industrial recruitment and self‐development efforts is examined. Results suggest that while natural capital positively impacts a community's successful recruitment of outside industries, it is not significant for a community's level of self‐development. However, a community's social infrastructure, measured by the existence of active civic organizations, local businesses that support local community projects, community‐wide fund‐raising capacity, and extra‐local linkages to nearby communities, state, and national agencies, positively affects both industrial recruitment and self‐development. These findings illustrate the need for communities and local activists to carefully weigh their advantages and potential shortcomings when deciding on an economic development strategy.

In this study, we test the effect of in-class student peer review on student learning outcomes using a quasiexperimental design. We provide an assessment of peer review in a quantitative research methods course, which is a traditionally difficult and technical course. Data were collected from 170 students enrolled in four sections of a quantitative research methods course, two in-class peer review sections, and two sections that did not incorporate in-class peer review over two semesters. For the two sections with peer review,

This paper links the micro and macro levels of analysis by examining how different aspects of community sentiment are affected by one's personal ties to the community compared with the organizational network structure of the community. Using data collected from residents of six communities in Washington State, network analysis combined with negative binomial regression is used to determine the effect of personal networks and community networks on community attachment and satisfaction. Findings suggest that while individual‐level variables, such as length of residence and individual ties, affect one's attachment to community, a community's network structure does not significantly affect community attachment. However, a community's network structure significantly affects one's evaluation of community. Regardless of one's ties to the community, residents of cohesive communities are more likely to evaluate the community's social and physical environments more positively. © 2010 Wiley Periodicals, Inc.

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