Religious Orthodoxy and Entrepreneurial Risk-Taking

The relationship between entrepreneurship and religion is complex (Dana 2010 Dana, Leo Paul. 2010. “Entrepreneurship and Religion”. In ed, Northampton, MA: Edward Elgar Publishing.  [Google Scholar]), and religion can help or hamper the entrepreneurial process, depending on the context and culture (Dana 2009 Dana, Leo and Paul. 2009. Religion as an Explanatory Variable for Entrepreneurship. International Journal of Entrepreneurship and Innovation, 10(2): 87–100. [Crossref] , [Google Scholar]; Dodd and Gotsis 2007 Dodd, Sarah Drakopoulou and George, Gotsis. 2007. The Interrelationships between Entrepreneurship and Religion. International Journal of Entrepreneurship and Innovation, 8(2): 93–104. [Crossref] , [Google Scholar]). This article examines religious orthodoxy and its connection to one aspect of the entrepreneurial process—risk-taking within existing organizations. Using multinomial logistic regression to analyze data from the 2010 Baylor Religion Survey, the researchers find that religious orthodoxy is negatively associated with entrepreneurial risk-taking, even after controlling for traditional variables associated with entrepreneurism. It supports the idea that beliefs—and not just adherence or behaviors—are important to understanding the connection between entrepreneurial processes and religion.     

Wavelet modeling of functional random effects with application to human vision data

In modern statistical practice, it is increasingly common to observe a set of curves or images, often measured with noise, and to use these as the basis of analysis (functional data analysis). We consider a functional data model consisting of measurement error and functional random effects motivated by data from a study of human vision. By transforming the data into the wavelet domain we are able to exploit the expected sparse representation of the underlying function and the mechanism generating the random effects. We propose simple fitting procedures and illustrate the methods on the vision data.     

Paradoxes of Online Investing: Testing the Influence of Technology on User Expectancies*

At an increasing rate, individual investors are taking personal control over their financial destinies by investing their money online. Compared to offline do‐it‐yourself approaches, evidence suggests that investors exhibit lofty expectations and perform significantly worse after going online. However, little is understood about the mechanisms fueling expectancies, the role technologies play in their formation, or how technologies shape investment decisions. Therefore, this article explores the paradoxical nature of online investing technologies, which can give rise to a heightened state of conviction in one's capability to invest successfully. Drawing on Social Cognitive Theory, the concepts of encapsulation and combination are introduced to develop a research model describing how functional and technical self‐efficacy judgments independently and collectively shape and influence outcome expectancies. The results suggest that perceptions about what one can accomplish through online investing technologies can lead investors to exaggerate their capabilities, which, in turn, produces elevated expectancies of financial payoffs and nonmonetary rewards. These findings carry important implications. In tasks requiring both computing and functional skills, the principals of encapsulation and combination highlight the importance of comprehensively capturing self‐efficacy beliefs across skill domain boundaries. Moreover, online investing represents a paradoxical case that challenges the traditional assumption that fostering a robust sense of efficacy represents a purely noble enterprise. In fact, strong self‐efficacy beliefs can prove counterproductive, leading to severe, irreversible, and unintended consequences. Going forward, these discoveries provide a solid foundation to enhance systems designs and facilitate a deeper understanding of user psychology.     

Assessment of the information theory approach to evaluating time-to-event surrogate and true endpoints in a meta-analytic setting

In many disease areas, commonly used long-term clinical endpoints are becoming increasingly difficult to implement due to long follow-up times and/or increased costs. Shorter-term surrogate endpoints are urgently needed to expedite drug development, the evaluation of which requires robust and reliable statistical methodology to drive meaningful clinical conclusions about the strength of relationship with the true long-term endpoint. This paper uses a simulation study to explore one such previously proposed method, based on information theory, for evaluation of time to event surrogate and long-term endpoints, including the first examination within a meta-analytic setting of multiple clinical trials with such endpoints. The performance of the information theory method is examined for various scenarios including different dependence structures, surrogate endpoints, censoring mechanisms, treatment effects, trial and sample sizes, and for surrogate and true endpoints with a natural time-ordering. Results allow us to conclude that, contrary to some findings in the literature, the approach provides estimates of surrogacy that may be substantially lower than the true relationship between surrogate and true endpoints, and rarely reach a level that would enable confidence in the strength of a given surrogate endpoint. As a result, care is needed in the assessment of time to event surrogate and true endpoints based only on this methodology.     

Quantitative Risk Assessment: Developing a Bayesian Approach to Dichotomous Dose–Response Uncertainty

Model averaging for dichotomous dose–response estimation is preferred to estimate the benchmark dose (BMD) from a single model, but challenges remain regarding implementing these methods for general analyses before model averaging is feasible to use in many risk assessment applications, and there is little work on Bayesian methods that include informative prior information for both the models and the parameters of the constituent models. This article introduces a novel approach that addresses many of the challenges seen while providing a fully Bayesian framework. Furthermore, in contrast to methods that use Monte Carlo Markov Chain, we approximate the posterior density using maximum a posteriori estimation. The approximation allows for an accurate and reproducible estimate while maintaining the speed of maximum likelihood, which is crucial in many applications such as processing massive high throughput data sets. We assess this method by applying it to empirical laboratory dose–response data and measuring the coverage of confidence limits for the BMD. We compare the coverage of this method to that of other approaches using the same set of models. Through the simulation study, the method is shown to be markedly superior to the traditional approach of selecting a single preferred model (e.g., from the U.S. EPA BMD software) for the analysis of dichotomous data and is comparable or superior to the other approaches.     

Exploration of the impact of organisational context on a workplace safety and health intervention

ABSTRACT

The Safety and Health Improvement Program (SHIP) was designed to increase workers’ safety and health using supervisor/leadership training. SHIP was implemented and evaluated in a cluster randomized controlled trial with 20 supervisors and 292 construction crew members representing a high-risk industry. The intervention had three components: (1) computer-based training to teach supervisors ways to better support worker safety and work-life challenges; (2) supervisor behavioural self-monitoring to facilitate transfer of training to practice; and (3) team-based discussions with supervisors and work crew members to identify challenges and opportunities for improvement with 30, 60, and 90 day follow-up check-in meetings. Main effects for the intervention on perceptions of family supportive supervisor behaviors, team effectiveness, and work-life effectiveness were not found, suggesting that the pre-intervention context could help explain the lack of intervention effects. We found that the intervention was more beneficial for work crew members who had poorer pre-intervention perceptions of their supervisor (lower leader-member exchange) and lower perceived team cohesion, suggesting the important impact of the organisational context on intervention effects. We argue that perhaps these work crews were more ready for change and improvements in functioning than were the crews that were already functioning well.     

Women’s views about the timing of birth

Background

Estimated date of birth (EDB) is used to guide the care provided to women during pregnancy and birth, although its imprecision is recognised. Alternatives to the EDB have been suggested for use with women however their attitudes to timing of birth information have not been examined.

Validation and Use of a Predictive Modeling Tool: Employing Scientific Findings to Improve Responsible Conduct of Research Education

Although recent evidence suggests ethics education can be effective, the nature of specific training programs, and their effectiveness, varies considerably. Building on a recent path modeling effort, the present study developed and validated a predictive modeling tool for responsible conduct of research education. The predictive modeling tool allows users to enter ratings in relation to a given ethics training program and receive instantaneous evaluative information for course refinement. Validation work suggests the tool’s predicted outcomes correlate strongly (r = 0.46) with objective course outcomes. Implications for training program development and refinement are discussed.