University of North Carolina at Wilmington

About: University of North Carolina at Wilmington is a education organization based out in Wilmington, North Carolina, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 3329 authors who have published 6797 publications receiving 186308 citations.

Transformational Leadership and Public Service Motivation: Driving Individual and Organizational Performance

Abstract: Despite growing evidence about prosocial motivations and their effects on employee behavior, how can new public service motivation research translate into more effective management practices—which, so far, regrettably remain underdeveloped? Increasingly, public service motivation studies have moved from understanding what motivates public servants to exploring how public service motives influence performance. Similarly, greater attention is now paid to the practices of transformational leadership. Drawing on concepts from transformational leadership, this essay explores how managers can harness the positive aspects of public service motivation to enhance employee and organizational performance and outlines strategies that can help managers incorporate public service motivation values across management systems.

North Atlantic Right Whales in Crisis

Abstract: The survival of North Atlantic right whales ( Eubalaena glacialis ) is seriously threatened by mortality caused by ships and entanglements in fishing gear. Demographic modeling indicates that the population is declining despite efforts to reduce anthropogenic mortalities, and in spite of recent increases in calving. The authors of this Policy Forum recommend immediate emergency management actions to reduce shipping and entanglement mortalities in right whales, so as to avoid a catastrophic population decline and inevitable extinction.

Religion and crime reexamined: the impact of religion, secular controls, and social ecology on adult criminality*

Abstract: Since Hirschi and Stark's (1969) surprising failure to find religious (“hellfire”) effects on delinquency, subsequent research has generally revealed an inverse relationship between religiosity and various forms of deviance, delinquency, and crime. The complexity of the relationship and conditions under which it holds, however, continue to be debated. Although a few researchers have found that religion's influence is noncontingent, most have found support—especially among youths—for effects that vary by denomination, type of offense, and social and/or religious context. More recently the relationship has been reported as spurious when relevant secular controls are included. Our research attempts to resolve these issues by testing the religion-crime relationship in models with a comprehensive crime measure and three separate dimensions of religiosity. We also control for secular constraints, religious networks, and social ecology. We found that, among our religiosity measures, participation in religious activities was a persistent and noncontingent inhibiter of adult crime.

Ecologists should not use statistical significance tests to interpret simulation model results

Abstract: Simulation models are widely used to represent the dynamics of ecological systems. A common question with such models is how changes to a parameter value or functional form in the model alter the results. Some authors have chosen to answer that question using frequentist statistical hypothesis tests (e.g. ANOVA). �怀is is inappropriate for two reasons. First, p-values are determined by statistical power (i.e. replication), which can be arbitrarily high in a simulation context, producing minuscule p-values regardless of the effect size. Second, the null hypothesis of no difference between treatments (e.g. parameter values) is known a priori to be false, invalidating the premise of the test. Use of p-values is troublesome (rather than simply irrelevant) because small p-values lend a false sense of importance to observed differences. We argue that modelers should abandon this practice and focus on evaluating the magnitude of differences between simulations. A growing number of authors in the ecological literature use statistical methods common to experimental ecology to analyze the output of ecological simulation models. For example, authors may use analysis of variance (ANOVA) to test whether model runs with different parameter values or different functional forms produce statistically different outputs. We view significance testing applied to simulation model output as a misuse of statistical theory. In this article we explain our reasoning with the goals of discouraging the practice, encouraging instead a focus on the magnitude of differences between simulations (i.e. effect sizes), and sparking discussion regarding when ‐ if ever ‐ statistical significance tests could be appropriate. �怀e perils of placing too much emphasis on statistical