The Organization of Factions: Interest Mobilization and the Group Theory of Politics

Theories of interest group mobilization are central to political science but current research on interest organizations has not proven useful for scholars in related fields. I argue that, by adapting organizational theory to account for the particular function of interest organizations, scholars can build a widely applied theoretical framework. The key step is an analysis of the role that organizations play in the mobilization of influence: they are intermediaries, reliant on their constituents and their lobbying targets. Reviewing research on ethnic politics and political economy, I demonstrate that this intermediary view of interest organizations encourages theoretical ideas to travel between subfields.

Analyses of multiple types of online gambling within one provider: an extended evaluation framework of actual online gambling behaviour

The aim of this article is to extend previous publications of actual online gambling behaviour that neglected involvement across multiple types of gambling and did not provide levels of at-risk involvement. Behavioural data from 27,653 subscribers of an online gambling provider (bwin) in February 2005 were reanalysed across eight products over seven months. Established involvement levels of offline gambling segregated possible online at-risk gamblers. Forty-seven percent of the sample exceeded at least one of the two most conservative thresholds. Each additionally used gambling product increased the risk of transgressing involvement cut-offs by 3.06 to 4.27 times, but type-specific risks decreased strongly after adjusting for involvement in multiple gambling types. Only Poker and Live-action betting remained significant risk factors after adjustment. Taken together, cross-product analyses of gambling patterns lay the groundwork for an extended understanding of individual online gambling behaviour and overcome the methodological artefacts of isolated analyses.     

Refusal Bias in the Estimation of HIV Prevalence

In 2007, UNAIDS corrected estimates of global HIV prevalence downward from 40 million to 33 million based on a methodological shift from sentinel surveillance to population-based surveys. Since then, population-based surveys are considered the gold standard for estimating HIV prevalence. However, prevalence rates based on representative surveys may be biased because of nonresponse. This article investigates one potential source of nonresponse bias: refusal to participate in the HIV test. We use the identity of randomly assigned interviewers to identify the participation effect and estimate HIV prevalence rates corrected for unobservable characteristics with a Heckman selection model. The analysis is based on a survey of 1,992 individuals in urban Namibia, which included an HIV test. We find that the bias resulting from refusal is not significant for the overall sample. However, a detailed analysis using kernel density estimates shows that the bias is substantial for the younger and the poorer population. Nonparticipants in these subsamples are estimated to be three times more likely to be HIV-positive than participants. The difference is particularly pronounced for women. Prevalence rates that ignore this selection effect may be seriously biased for specific target groups, leading to misallocation of resources for prevention and treatment.     

Access to and exclusion from housing over time: Refugees' experiences in rural areas

Taking the example of recognized refugees in rural Germany, and following the housing pathways approach, the paper addresses the complex interplay of individual and family-related residential preferences over time as well as structural factors regarding access to housing and associated settlement and integration. Results are from a long-term empirical study that encompassed both refugees’ and local actors’ views. Mechanisms and practices of exclusion that prevent refugees from accessing appropriate private housing are related to the negotiation of residential preferences about where and how to live, as well as to structural aspects such as the pattern of local housing markets, accessibility of infrastructures or the unwillingness of landlords to let to refugees. The paper concludes to highlight the role of place in housing trajectories and the significance of social resources within refugees' practices and local intermediaries' strategies to overcome exclusion and provision of access to rural housing.     

Win statistics (win ratio,win odds,and net benefit) can complement one another to show the strength of the treatment effect on time-to-event outcomes

Conventional analyses of a composite of multiple time-to-event outcomes use the time to the first event. However, the first event may not be the most important outcome. To address this limitation, generalized pairwise comparisons and win statistics (win ratio, win odds, and net benefit) have become popular and have been applied to clinical trial practice. However, win ratio, win odds, and net benefit have typically been used separately. In this article, we examine the use of these three win statistics jointly for time-to-event outcomes. First, we explain the relation of point estimates and variances among the three win statistics, and the relation between the net benefit and the Mann–Whitney U statistic. Then we explain that the three win statistics are based on the same win proportions, and they test the same null hypothesis of equal win probabilities in two groups. We show theoretically that the Z-values of the corresponding statistical tests are approximately equal; therefore, the three win statistics provide very similar p-values and statistical powers. Finally, using simulation studies and data from a clinical trial, we demonstrate that, when there is no (or little) censoring, the three win statistics can complement one another to show the strength of the treatment effect. However, when the amount of censoring is not small, and without adjustment for censoring, the win odds and the net benefit may have an advantage for interpreting the treatment effect; with adjustment (e.g., IPCW adjustment) for censoring, the three win statistics can complement one another to show the strength of the treatment effect. For calculations we use the R package WINS, available on the CRAN (Comprehensive R Archive Network).