The Efficacy of the Protection Motivation Theory in Predicting Cruise Ship Passengers\u27 Intentions Regarding Norovirus Disease Incidence

The cruise industry is the fastest growing segment of the travel industry. Concurrent with its growth is the challenge of mitigating the risk associated with shipboard outbreaks. Norovirus is the leading cause of shipboard outbreaks. This study examined the efficacy of the protection motivation theory for predicting passengers’ intentions towards healthy behaviors in regard to norovirus disease incidence. Outbreaks of norovirus have serious health and economic consequences. Presently there is no vaccination available; however, handwashing and social distancing can have significant impact upon the course of an outbreak. The respondents of this study completed a scenario-based questionnaire regarding norovirus disease incidence in response to a simulated outbreak while at sea. The results indicated that the protection motivation theory (PMT) explained 58% of the variability in handwashing intention and 46% of the variability in social distancing intention. The findings found that PMT was a useful framework for understanding intention to engage in handwashing and social distancing behaviors. Furthermore, this study revealed a need for continued educational efforts directed at cruisers because almost one third of respondents indicated that they had no prior knowledge of norovirus. The findings also revealed that the cruising public has low levels of perceived severity and susceptibility towards norovirus

A randomized trial in a massive online open course shows people don't know what a statistically significant relationship looks like, but they can learn

Scatterplots are the most common way for statisticians, scientists, and the public to visually detect relationships between measured variables. At the same time, and despite widely publicized controversy, P-values remain the most commonly used measure to statistically justify relationships identified between variables. Here we measure the ability to detect statistically significant relationships from scatterplots in a randomized trial of 2,039 students in a statistics massive open online course (MOOC). Each subject was shown a random set of scatterplots and asked to visually determine if the underlying relationships were statistically significant at the P < 0.05 level. Subjects correctly classified only 47.4% (95% CI: 45.1%-49.7%) of statistically significant relationships, and 74.6% (95% CI: 72.5%-76.6%) of non-significant relationships. Adding visual aids such as a best fit line or scatterplot smooth increased the probability a relationship was called significant, regardless of whether the relationship was actually significant. Classification of statistically significant relationships improved on repeat attempts of the survey, although classification of non-significant relationships did not. Our results suggest: (1) that evidence-based data analysis can be used to identify weaknesses in theoretical procedures in the hands of average users, (2) data analysts can be trained to improve detection of statistically significant results with practice, but (3) data analysts have incorrect intuition about what statistically significant relationships look like, particularly for small effects. We have built a web tool for people to compare scatterplots with their corresponding p-values which is available here: http://glimmer.rstudio.com/afisher/EDA/.Comment: 7 pages, including 2 figures and 1 tabl

ACross-Sectional Analysis of CapRates by MSA

Much attention has been paid to capitalization rates or “cap rates?defined as the net operating income over transaction price, also known as a “going-in?current yield on commercial real estate when calculated at the time of purchase. We know that there are a number of global factors that drive capital markets and required rates of return that help to explain observed cap rates over time, but we know little about factors driving the geographical cross-sectional variation of these cap rates. Why are cap rates for similar sized and type property so much lower or higher in one metropolitan statistical area than another? Using data from Real Capital Analytics for multifamily properties we explore several models that combine the expected influences from housing demand growth, supply constraints, liquidity risk and the interaction of these. We document a very strong and robust relation between supply constraints and cap rates as well as evidence of capital flowing from larger markets to smaller markets in recent years. We also find weak but generally supportive evidence of influences from expected growth rates, liquidity and other risk factors.

Development of a Task Force to Provide Education and Leadership to an Emerging Industry

The Ohio Meat Goat Task Force is a model for engaging resources and building leadership capacity to generate income and enhance sustainability of farm businesses. The collaboration of multi-disciplinary faculty, producers, allied industry, ethnic cultures, and various agencies combines expertise and leadership with applied experience to foster entrepreneurship. Grants have been secured to research ethnic market preferences, processing infrastructure and capacity, and economically viable production systems. Education provides farm businesses capacity to build leadership, share knowledge, and network resources to capture value-added marketing opportunities

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