Identifying the presence of psychopathy in the community

Aim: Researchers have suggested that approximately 1% of individuals with psychopathic tendencies can successfully function within the community, although there has been a lack of research to support this claim. The current study aimed to identify individuals with psychopathic tendencies within a community sample and furthermore the socio-demographic correlates of this community integrated psychopath (e.g. relationship stability, substance use, and employment status). Procedure: 300 participants completed the Self-Reported Psychopathy scale – version 3 which contains four core psychopathy subfactors: (a) Interpersonal Manipulation, (b) Callous Affect, (c) Erratic Lifestyle and (d) Criminal Tendencies as well as the Paulhus Deception Scales to explore the effect of impression management and self-deception on the identification of psychopathy. Findings: Results indicated that at least 1% of the current community displayed characteristics consistent with psychopathic tendencies. A series of bivariate and multivariate statistical analyses were conducted which indicated that gender, age and alcohol misuse were predictive of psychopathy scores for this sample. More specifically, younger males who tend to misuse alcohol were found to be most likely to have psychopathic tendencies. Interestingly, impression management and self-deception was not associated with such tendencies. Discussion: The results provide some support for the assertion that individuals with psychopathic tendencies can be identified within the community (regardless of impression management techniques) and that such tendencies are associated with specific socio-demographic characteristics.

Expert elicitation and its interface with technology : a review with a view to designing Elicitator

Expert knowledge is valuable in many modelling endeavours, particularly where data is not extensive or sufficiently robust. In Bayesian statistics, expert opinion may be formulated as informative priors, to provide an honest reflection of the current state of knowledge, before updating this with new information. Technology is increasingly being exploited to help support the process of eliciting such information. This paper reviews the benefits that have been gained from utilizing technology in this way. These benefits can be structured within a six-step elicitation design framework proposed recently (Low Choy et al., 2009). We assume that the purpose of elicitation is to formulate a Bayesian statistical prior, either to provide a standalone expert-defined model, or for updating new data within a Bayesian analysis. We also assume that the model has been pre-specified before selecting the software. In this case, technology has the most to offer to: targeting what experts know (E2), eliciting and encoding expert opinions (E4), whilst enhancing accuracy (E5), and providing an effective and efficient protocol (E6). Benefits include: -providing an environment with familiar nuances (to make the expert comfortable) where experts can explore their knowledge from various perspectives (E2); -automating tedious or repetitive tasks, thereby minimizing calculation errors, as well as encouraging interaction between elicitors and experts (E5); -cognitive gains by educating users, enabling instant feedback (E2, E4-E5), and providing alternative methods of communicating assessments and feedback information, since experts think and learn differently; and -ensuring a repeatable and transparent protocol is used (E6).

Conducting statistical tests of hypotheses : five common misconceptions found in transportation research

Most statistical methods use hypothesis testing. Analysis of variance, regression, discrete choice models, contingency tables, and other analysis methods commonly used in transportation research share hypothesis testing as the means of making inferences about the population of interest. Despite the fact that hypothesis testing has been a cornerstone of empirical research for many years, various aspects of hypothesis tests commonly are incorrectly applied, misinterpreted, and ignored—by novices and expert researchers alike. On initial glance, hypothesis testing appears straightforward: develop the null and alternative hypotheses, compute the test statistic to compare to a standard distribution, estimate the probability of rejecting the null hypothesis, and then make claims about the importance of the finding. This is an oversimplification of the process of hypothesis testing. Hypothesis testing as applied in empirical research is examined here. The reader is assumed to have a basic knowledge of the role of hypothesis testing in various statistical methods. Through the use of an example, the mechanics of hypothesis testing is first reviewed. Then, five precautions surrounding the use and interpretation of hypothesis tests are developed; examples of each are provided to demonstrate how errors are made, and solutions are identified so similar errors can be avoided. Remedies are provided for common errors, and conclusions are drawn on how to use the results of this paper to improve the conduct of empirical research in transportation.

The mobile phone as a multi OTP device using trusted computing

The rapid growth in the number of online services leads to an increasing number of different digital identities each user needs to manage. As a result, many people feel overloaded with credentials, which in turn negatively impact their ability to manage them securely. Passwords are perhaps the most common type of credential used today. To avoid the tedious task of remembering difficult passwords, users often behave less securely by using low entropy and weak passwords. Weak passwords and bad password habits represent security threats to online services. Some solutions have been developed to eliminate the need for users to create and manage passwords. A typical solution is based on giving the user a hardware token that generates one-time-passwords, i.e. passwords for single session or transaction usage. Unfortunately, most of these solutions do not satisfy scalability and/or usability requirements, or they are simply insecure. In this paper, we propose a scalable OTP solution using mobile phones and based on trusted computing technology that combines enhanced usability with strong security.