The Inability of Self-affirmations to Decrease Defensive Bias Toward an Alcohol-Related Risk Message Among High-Risk College Students

Abstract

Objective: To examine the efficacy of a self-affirmation task in deterring college alcohol misuse and the importance of preexisting beliefs in predicting subsequent behavior change. Participants: Heavy-drinking undergraduates (N = 110) participated during the 2011–2012 academic year. Methods: Participants were randomized to complete an affirmation or control task before reading an alcohol risk message. Alcohol-related beliefs and behaviors were assessed. Participants completed a 2-week online follow-up assessing alcohol-related behaviors. Results: Both groups reported increased perceived problem importance, but neither group displayed changes in personal risk. Follow-up assessment revealed similar, significant declines in peak consumption in both groups, with no significant between-group differences. Preexisting beliefs accounted for 5% to 10% of variance in drinking outcomes. Conclusions: An affirmation task does not seem to decrease defensive processing or alter high-risk drinking behaviors among college students and should not be utilized in lieu of more effective strategies.     

Ensemble Habitat Mapping of Invasive Plant Species

Ensemble species distribution models combine the strengths of several species environmental matching models, while minimizing the weakness of any one model. Ensemble models may be particularly useful in risk analysis of recently arrived, harmful invasive species because species may not yet have spread to all suitable habitats, leaving species‐environment relationships difficult to determine. We tested five individual models (logistic regression, boosted regression trees, random forest, multivariate adaptive regression splines (MARS), and maximum entropy model or Maxent) and ensemble modeling for selected nonnative plant species in Yellowstone and Grand Teton National Parks, Wyoming; Sequoia and Kings Canyon National Parks, California, and areas of interior Alaska. The models are based on field data provided by the park staffs, combined with topographic, climatic, and vegetation predictors derived from satellite data. For the four invasive plant species tested, ensemble models were the only models that ranked in the top three models for both field validation and test data. Ensemble models may be more robust than individual species‐environment matching models for risk analysis.     

The limits of inequality: Public support for social policy across rich democracies

Does public opinion react to inequality, and if so, how? The social harms caused by increasing inequality should cause public opinion to ramp up demand for social welfare protections. However, the public may react to inequality differently depending on institutional context. Using ISSP and WID data (1980?2006), we tested these claims. In liberal institutional contexts (mostly English‐speaking), increasing income inequality predicted higher support for state provision of social welfare. In coordinated and universalist contexts (mostly of Europe), increasing inequality predicted less support. Historically higher income concentration predicted less public support, providing an account of the large variation in inequality within the respective liberal and coordinated contexts. The results suggest opinions in liberal societies – especially with higher historical inequality – reached the limits of inequality, reacting negatively; whereas in coordinated/universalist societies – especially with lower historical inequality – opinions moved positively, as if desiring more inequality.     

Feeding,growth, and trophic position of redbreast sunfish (Lepomis auritus) in watersheds of differing land cover in the lower Piedmont,USA

One pervasive outcome in the urbanization of an ecosystem is the proliferation and numerical dominance of select tolerant organisms that are often native to the system yet with reduced relative abundances in less-disturbed conditions. As a result of high variation in environmental conditions between urbanized and non-urbanized systems, it is possible that the functional role of a ubiquitous organism is context dependent. Such is the case for redbreast sunfish (Lepomis auritus) in small streams in many parts of the Piedmont of the southeastern USA. To investigate this hypothesis, we evaluated the feeding, growth, and trophic position of redbreast sunfish in 3 streams of increasing levels of watershed urbanization (forested, suburban, urban) in the Lower Piedmont of western Georgia, USA. Through gut contents analysis, we found that sunfish consumed primarily Chironomidae (Diptera) larvae across all streams. However, fish in the suburban stream consumed more terrestrial prey than fish in the forest and urban streams, which corresponded to lower aquatic prey abundances in the suburban stream. Although there was no difference in mean fish age among streams, otolith analysis revealed that fish in the urban stream were larger at age than those in the forest stream. Last, stable isotope analysis revealed that fish in the urban stream occupied a lower trophic position than the other 2 streams. These results suggest that despite the fact that the primary prey resource was similar for sunfish in each stream, their potential functional role, as evidenced by size at age and trophic position, is context dependent.

     

Secondary observer effects: idiosyncratic errors in small-N secondary data analysis

In this paper, I extend the concept of observer effect into the realm of country-level secondary data analysis. When analyzing what appear to be the same secondary data using the same methods, macro-comparative researchers arrive at different results. I argue that this is a product of idiosyncratic variation directly or indirectly produced by the researchers. Even when this bias produces only small perturbations in results, the consequences may be very large. Using an influential study by Brooks and Manza I analyze this secondary observer effect (SOE). Two seemingly identical replications of their data by different researchers produced surprising variations. Reanalysis of these divergent values produces similar but not identical results. A rough calculation of the size of the SOEs suggests that they are about .32 standardized standard deviations across variable scores. Simulations of this size of error show that significant changes in findings occur as a result.