Emotional Experience as a Function of Social Context: The Role of the Other

Inconsistencies in previous findings concerning the relationship between emotion and social context are likely to reflect the multi-dimensionality of the sociality construct. In the present study we focused on the role of the other person by manipulating two aspects of this role: co-experience of the event and expression of emotion. We predicted that another's co-experience and expression would affect emotional responses and that the direction of these effects would depend upon the manipulated emotion and how the role of the other person is appraised. Participants read vignettes eliciting four different emotions: happiness, sadness, anxiety, and anger. As well as an alone condition, there were four conditions in which a friend was present, either co-experiencing the event or merely observing it, and either expressing emotions consistent with the event or not showing any emotion. There were significant effects of co-experience in the case of anger situations, and of expression in the case of happiness and sadness situations. Social appraisal also appeared to influence emotional response. We discuss different processes that might be responsible for these results.     

Statistical consulting interactions: a personal view

There are several components to every consulting project. The most important ones are the scientific/industrial partners, the background and goals of the project, and data. A statistician has to interact successfully with every component for the project to be a success and an essential step is to encourage the project partners to interact with their own data. There is no better way to ensure that domain knowledge is fully integrated into any analysis. Partners are not always explicit about what they know, about what is possible and about what they want. It is not always clear to them either. Consulting in projects is more of a process than the accomplishment of a task, so continual interaction is needed. Sometimes this is easy, sometimes it is more difficult. This does not always have much to do with the intrinsic difficulty of the subject matter.     

Degree and Direction of Paid Employee/Volunteer Interchange in Nonprofit Organizations

This study builds on an earlier one (Handy, Mook, and Quarter 2008) that examined the degree of interchangeability between volunteers and paid employees in nonprofit organizations. In the current study, we surveyed 836 nonprofits in Canada to understand what factors determine the degree of interchange between paid employees and volunteers, bearing in mind that this interchange can go in two directions: paid employees for volunteers and volunteers for paid employees. We found the degree of interchange to be widespread and in both directions. One of the most important predictors of the likelihood of interchange was the number of full‐time employees, suggesting that interchangeability occurs less frequently in organizations with a larger number of employees than in those with fewer employees. Religious congregations and organizations with workload increases were more likely to interchange tasks among volunteers and paid staff.     

Ethical issues in tracking cellular telephones at an event

Early in 2007, the CSIR conducted an experiment to track the cellular telephones of a small group of people as they moved to and from an event, to test the viability of using such tracking to provide the participants with useful traffic information. This project raised a number of ethical issues, which prompted this paper and which we discuss here. These include:

• the ethics of modelling data, including the treatment of research participants;

• privacy and surveillance issues related to tracking the movement of people;

• the risks inherent in being tracked vs the benefits of being tracked; and

• the ethics related to sending messages to drivers.

We have reviewed the literature on ethics and used the results to assemble a check list of relevant ethical issues, adding a few of our own (i.e. a deontological ethics approach), against which the conduct of this research project was assessed. We also provide an overview of the experiment and the results obtained.     

Collaboration outcomes in a public sector: impact of governance,leadership, interdependence and relational capital

Collaboration between public agencies is critical to address social issues effectively. The main objective of this research was to identify the factors that lead to successful achievement of desired collaboration outcomes that could eventually lead to societal outcomes. The factors identified were: transformational leadership, governance, interdependence, and relational capital. An integrated framework was developed based on collaboration governance framework (CGF) of Ansell and Gash (J Public Adm Res Theory 18:543–571, 2008). The CGF framework has integrated the following four broad variables that are responsible for achieving the desired collaboration outcomes: starting conditions, institutional design, leadership, and collaborative process. The study was conducted in Malaysia by sending questionnaires to 500 officers in various ministries who were involved in planning, formulating, and implementing public policies. The main findings of this study are: (1) transformational leadership influences governance, interdependence, relational capital, and collaboration outcomes; (2) governance has impact on relational capital, interdependence, and collaboration outcomes; (3) relational capital and interdependence result in positive collaboration outcomes; (4) dimensions of governance are inter-related; and (5) interdependence and relational capital are strongly correlated. This research adds significantly to the literature on collaborative governance. The implications and limitations of the study are discussed.     

An approach for finding fully Bayesian optimal designs using normal-based approximations to loss functions

The generation of decision-theoretic Bayesian optimal designs is complicated by the significant computational challenge of minimising an analytically intractable expected loss function over a, potentially, high-dimensional design space. A new general approach for approximately finding Bayesian optimal designs is proposed which uses computationally efficient normal-based approximations to posterior summaries to aid in approximating the expected loss. This new approach is demonstrated on illustrative, yet challenging, examples including hierarchical models for blocked experiments, and experimental aims of parameter estimation and model discrimination. Where possible, the results of the proposed methodology are compared, both in terms of performance and computing time, to results from using computationally more expensive, but potentially more accurate, Monte Carlo approximations. Moreover, the methodology is also applied to problems where the use of Monte Carlo approximations is computationally infeasible.