“My God,What Am I Gonna Say?”: Class Reunions as Social Control

Autobiographical occasions are perceived as an apportunity to constitute an identity, to lay claim to one's own life, to the right to tell one's own story. Using high school class reunions as a case study, I argue that those social moments that require accounts of one's life also generate a major threat to the identities constructed, by constituting an informal process of social control. The requirement to narrate one's life story and cope with its limits is a force through which the social order is confirmed and one's identity within that order defined and assigned. While reunions create a community and an opportunity to speak up, they generate at the same time classification, hierarchy, and evaluation processes that might not otherwise appear in modern society.     

Exhaustive k-nearest-neighbour subspace clustering

Cluster analysis is an important technique of explorative data mining. It refers to a collection of statistical methods for learning the structure of data by solely exploring pairwise distances or similarities. Often meaningful structures are not detectable in these high-dimensional feature spaces. Relevant features can be obfuscated by noise from irrelevant measurements. These observations led to the design of subspace clustering algorithms, which can identify clusters that originate from different subsets of features. Hunting for clusters in arbitrary subspaces is intractable due to the infinite search space spanned by all feature combinations. In this work, we present a subspace clustering algorithm that can be applied for exhaustively screening all feature combinations of small- or medium-sized datasets (approximately 30 features). Based on a robustness analysis via subsampling we are able to identify a set of stable candidate subspace cluster solutions.     

The Development and Implementation of a Multi‐Couple Therapy Model with Torture Survivors in the Democratic Republic of the Congo

In this article, we present development and feasibility of implementation of a multi‐couple group for use with torture‐surviving couples. The model was developed in the Democratic Republic of the Congo in a community that experienced widespread torture during the wars from 1998 to 2004. The Torture‐Surviving Couple Group model is a short‐term intervention designed to use few human resources to address relational difficulties resulting from exposure to traumatic stressors. The model was guided by critical and feminist epistemologies and employed an ecological lens to incorporate neurobiology and attachment processes along with narrative therapy techniques. An existing multi‐couple group model for addressing violence (Stith, Rosen, McCollum, & Thomsen, 2004 ), and a stage model for healing trauma (Herman, 1992 ) also informed the structural development of the Torture‐Surviving Couple Group model. Couple groups were conducted using a 10‐session program with 13 couples who met weekly. Session themes were incorporated into four phases emphasizing: (a) preparation; (b) safety and stabilization; (c) processing the relationship effects of trauma and grief; and (d) reintegration and rebuilding couple and family life. Couples reported and showed remarkable progress in their relationships after participating in the groups. Clinical and research implications and discussed along with the feasibility of developing and testing the model in a post‐conflict low income setting.     

Model distances for vine copulas in high dimensions

Vine copulas are a flexible class of dependence models consisting of bivariate building blocks and have proven to be particularly useful in high dimensions. Classical model distance measures require multivariate integration and thus suffer from the curse of dimensionality. In this paper, we provide numerically tractable methods to measure the distance between two vine copulas even in high dimensions. For this purpose, we consecutively develop three new distance measures based on the Kullback–Leibler distance, using the result that it can be expressed as the sum over expectations of KL distances between univariate conditional densities, which can be easily obtained for vine copulas. To reduce numerical calculations, we approximate these expectations on adequately designed grids, outperforming Monte Carlo integration with respect to computational time. For the sake of interpretability, we provide a baseline calibration for the proposed distance measures. We further develop similar substitutes for the Jeffreys distance, a symmetrized version of the Kullback–Leibler distance. In numerous examples and applications, we illustrate the strengths and weaknesses of the developed distance measures.     

Ist Marktorientierung ansteckend? Der Transfer der Marktorientierung über Hierarchieebenen – Eine empirische Mehrebenenuntersuchung

It is generally accepted that market orientation as a well-established marketing phenomenon influences the performance of companies. However, work investigating how market oriented behaviours of customer-contact employees can be influenced is conspicuous by its absence. This article is the first empirical multilevel study in this area, which simultaneously examines intralevel-effects (effects within the level of customer-contact employees, e.g. length of work experience, job satisfaction) as well as crosslevel-effects (effects which come from superordinate managers). Based on a large-scale three-level study, the results show that the transfer of top-managers’ market orientation via sales managers down to customer-contact employees is definitely not an automatism. In fact this market orientation cascade particularly succeeds, if the market orientation of superordinate managers comes along with intense charismatic leadership. All in all, the entire impact of crosslevel-variables on the market orientation of customer-contact employees is at least as comprehensive as the influence of intralevel variables.     

The small sample performance of estimators of the standard errors of structural equation models

In this paper, we compare five asymptotically, under a correctly specified likelihood, equivalent estimators of the standard errors for parameters in structural equation models. The estimators are evaluated under different conditions regarding (i) sample size, varying between N=50 and 3200, (ii) distributional assumption of the latent variables and the disturbance terms, namely normal, and heavy tailed (t), and (iii) the complexity of the model. For the assessment of the five estimators we use overall performance, relative bias, MSE and coverage of confidence intervals. The analysis reveals substantial differences in the performance of the five asymptotically equal estimators. Most diversity was found for t distributed, i.e. heavy tailed, data.