Deciding Not to Un–Do the “I Do:” Therapy Experiences of Women Who Consider Divorce But Decide to Remain Married

This study explores women's experience of marital therapy while they navigated decision making around divorce. A qualitative method was used to gain a deeper understanding of the participants' therapy and relationship decision‐making experiences. How are women's decisions whether or not to exit their marriage affected by therapy? The researchers interviewed 15 women who had considered initiating divorce before they turned 40 and had attended at least five marital therapy sessions but ultimately decided not to divorce. In general, participants reported that the therapy was helpful to them, their decision‐making process and their marriages. Five main themes emerged from the interviews: Women Initiated Therapy, Therapist Was Experienced as Unbiased, Therapy was Helpful, Importance of Extra‐therapeutic Factors, and Gradual Process.     

School Bullying as a Collective Action: Stigma Processes and Identity Struggling

The aim of this study was to investigate the collective action of bullying and its stigma processes and influences on identities. In accordance with interactionism, identity is a social process, constructed and reconstructed in everyday social interactions. Ethnographic fieldwork was conducted in four school classes, investigating six bullying cases. Grounded theory methods were used to explore and analyse data. Co‐constructing differentness was found to be a core process in bullying. Bullying often appeared to function like a self‐serving and socially inclusive ritual in which the bullies co‐constructed the ‘normal us’. Loss of belonging, self‐deprecation and identity struggling followed closely upon the sense of becoming socially discredited. Victims were trapped in the collective action. The findings highlight the significance of addressing peer cultures and the social psychology of everyday school life in anti‐bullying policies and practices.     

A Statistical Framework for Hypothesis Testing in Real Data Comparison Studies

In computational sciences, including computational statistics, machine learning, and bioinformatics, it is often claimed in articles presenting new supervised learning methods that the new method performs better than existing methods on real data, for instance in terms of error rate. However, these claims are often not based on proper statistical tests and, even if such tests are performed, the tested hypothesis is not clearly defined and poor attention is devoted to the Type I and Type II errors. In the present article, we aim to fill this gap by providing a proper statistical framework for hypothesis tests that compare the performances of supervised learning methods based on several real datasets with unknown underlying distributions. After giving a statistical interpretation of ad hoc tests commonly performed by computational researchers, we devote special attention to power issues and outline a simple method of determining the number of datasets to be included in a comparison study to reach an adequate power. These methods are illustrated through three comparison studies from the literature and an exemplary benchmarking study using gene expression microarray data. All our results can be reproduced using R codes and datasets available from the companion website http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/020_professuren/boulesteix/compstud2013.     

Human nature and economic policy: lessons for the transition economies

Both economic regulation in market economies and state ownership and control of the means of production in socialist economies have been interpreted as means of protecting people from economic exploitation by the owners of capital. In this paper, I argue against this interpretation, suggesting that both sets of institutions are more plausibly viewed as protecting people from destructive competition amongst themselves.     

How a Nonprofitness Orientation Influences Collective Civic Action: The Effects of Civic Engagement and Political Participation

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - This research addresses the question of how the institutional frame of “nonprofitness” shapes the civic...     

Spatial and Temporal Trends in Multidimensional Poverty in the United States over the Last Decade

This paper undertakes a comprehensive analysis of multidimensional poverty in the United States over the last decade. It provides estimates of multidimensional poverty over more than a decade, from 2008 to 2019, which covers the Great Recession and the recovery following the recession when major policy changes such as the Affordable Care Act were implemented. For the first time, spatial trends in estimates of multidimensional poverty are also provided. We measure annual poverty levels in 4 regions, 50 states and examine the relation between multidimensional poverty and neighborhood characteristics. We find that on average, 13 percent of the United States population was multidimensional poor. Poverty rates were high in the South and the West and among young adults, immigrants and Hispanics. Alternative indices of multidimensional poverty show consistent trends; multidimensional poverty in the United States rose between 2008 and 2010 and then gradually declined. However, more than a quarter of individuals with incomes above the poverty threshold remained multidimensional poor. This underscores the fact that income does not always capture deprivation experienced by individuals. Policies geared towards affordable housing, health insurance and higher education will help reduce multidimensional poverty in the United States.