How leader role identity influences the process of leader emergence: A social network analysis

Contemporary theories on leadership development emphasize the importance of having a leader identity in building leadership skills and functioning effectively as leaders. We build on this approach by unpacking the role leader identity plays in the leader emergence process. Taking the perspective that leadership is a dynamic social process between group members, we propose a social network-based process model whereby leader role identity predicts network centrality (i.e., betweenness and indegree), which then contributes to leader emergence. We test our model using a sample of 88 cadets participating in a leadership development training course. In support of our model, cadets who possess a stronger leader role identity at the beginning of the course were more likely to emerge as leaders. However this relationship was only mediated by one form of network centrality, indegree centrality, reflecting one's ability to build relationships within one's group. Implications for research and practice are discussed.     

Behavior, Belonging, and Belief: A Theory of Ritual Practice

A new model of ritual based on Durkheim's ([1912] 1995) theory is developed. It is argued that ritual practices generate belief and belonging in participants by activating multiple social–psychological mechanisms that interactively create the characteristic outcomes of ritual. Specifically, the distinctive elements of ritual practice are shown to induce altered subjective states and effortful and/or anomalous behaviors, which are subsequently misattributed in such a way that belief and belonging are created or maintained around the focus of ritual attention. These processes are traced in detail, and the resulting model is shown to be empirically credible, comprehensive, and theoretically fertile.     

TEACHING PROFESSIONAL WRITING TO FAMILY THERAPISTS: THREE APPROACHES

The authors present three educational approaches designed to demystify the writing process and to support better writing skills among family therapists. One framework is an academic course in professional writing, another is a writing seminar for family therapy and physician trainees within a medical school setting, and the third is a 2-hour continuing education writing workshop for family therapists. Each is replicable within a variety of settings.     

Refined Inference on Long Memory in Realized Volatility

There is an emerging consensus in empirical finance that realized volatility series typically display long range dependence with a memory parameter (d) around 0.4 (Andersen et al., 2001; Martens et al., 2004). The present article provides some illustrative analysis of how long memory may arise from the accumulative process underlying realized volatility. The article also uses results in Lieberman and Phillips (2004, 2005) to refine statistical inference about d by higher order theory. Standard asymptotic theory has an O(n^-1/2) error rate for error rejection probabilities, and the theory used here refines the approximation to an error rate of o(n^-1/2). The new formula is independent of unknown parameters, is simple to calculate and user-friendly. The method is applied to test whether the reported long memory parameter estimates of Andersen et al. (2001) and Martens et al. (2004) differ significantly from the lower boundary (d = 0.5) of nonstationary long memory, and generally confirms earlier findings.     

“WHAT DOES SHE SEE IN HIM?”: THE EFFECT OF SHARING ON THE CHOICE OF SPOUSE

All husbands and wives share in the spoils of marriage. Sharing makes each partner a residual claimant and encourages spouses to provide marital inputs. However, because each spouse is only a partial residual claimant, each may be inclined to provide a "suboptimal" level of marital inputs. The choice of spouse influences the level of distortion, and under some circumstances the efficient mating is between individuals of equal value     

Robust confidence interval for a residual standard deviation

The residual standard deviation of a general linear model provides information about predictive accuracy that is not revealed by the multiple correlation or regression coefficients. The classic confidence interval for a residual standard deviation is hypersensitive to minor violations of the normality assumption and its robustness does not improve with increasing sample size. An approximate confidence interval for the residual standard deviation is proposed and shown to be robust to moderate violations of the normality assumption with robustness to extreme non-normality that improves with increasing sample size.