Motives and Decisions for and Against Having Children Among Nonheterosexuals and the Impact of Experiences of Discrimination,Internalized Stigma,and Social Acceptance

Same-sex parents are increasingly a topic of public discourse. A growing number of homosexuals openly speak about their desire to have children or are already living together in different family constellations. The current study examined the decisions for or against having children and the motivations behind those decisions among nonheterosexuals living in Germany. A sample of 1,283 nonheterosexuals participated by means of an online survey. As some nonheterosexual individuals do not identify themselves with a male or female gender identity, a third category, “gender different,” was generated. Motives for (not) having children, perceptions of social acceptance, experiences of discrimination in relation to one's sexual orientation, and levels of internalized stigma were taken into account regarding their influence on the decision about parenthood. Most respondents (80%) reported that they did not have children. However, among this group, 43% stated that they had decided to have children later in their lives, 24% were undecided, and 11% had already decided against having children. The most important influences on the decision of whether to have children were respondents’ age and their desire for emotional stabilization. Negative experiences as a result of sexual orientation and internalized stigma had no impact on the decisions regarding parenthood.     

A Common Rationale for Global Sensitivity Measures and Their Estimation

Measures of sensitivity and uncertainty have become an integral part of risk analysis. Many such measures have a conditional probabilistic structure, for which a straightforward Monte Carlo estimation procedure has a double‐loop form. Recently, a more efficient single‐loop procedure has been introduced, and consistency of this procedure has been demonstrated separately for particular measures, such as those based on variance, density, and information value. In this work, we give a unified proof of single‐loop consistency that applies to any measure satisfying a common rationale. This proof is not only more general but invokes less restrictive assumptions than heretofore in the literature, allowing for the presence of correlations among model inputs and of categorical variables. We examine numerical convergence of such an estimator under a variety of sensitivity measures. We also examine its application to a published medical case study.     

Scale Economies and the Dynamics of Recurring Auctions

We analyze the dynamics of a game of sequential bidding in the presence of stochastic scale effects, either economies or diseconomies of scale. We show that economies of scale give rise to declining expected equilibrium prices, whereas the converse is not generally true. Moreover, first- and second-price auctions are not always revenue equivalent. Economies of scale make second-price auctions more profitable for the seller, whereas revenue equivalence may be preserved in the case of diseconomies.     

Fighting the Curse of Sparsity: Probabilistic Sensitivity Measures From Cumulative Distribution Functions

Quantitative models support investigators in several risk analysis applications. The calculation of sensitivity measures is an integral part of this analysis. However, it becomes a computationally challenging task, especially when the number of model inputs is large and the model output is spread over orders of magnitude. We introduce and test a new method for the estimation of global sensitivity measures. The new method relies on the intuition of exploiting the empirical cumulative distribution function of the simulator output. This choice allows the estimators of global sensitivity measures to be based on numbers between 0 and 1, thus fighting the curse of sparsity. For density-based sensitivity measures, we devise an approach based on moving averages that bypasses kernel-density estimation. We compare the new method to approaches for calculating popular risk analysis global sensitivity measures as well as to approaches for computing dependence measures gathering increasing interest in the machine learning and statistics literature (the Hilbert–Schmidt independence criterion and distance covariance). The comparison involves also the number of operations needed to obtain the estimates, an aspect often neglected in global sensitivity studies. We let the estimators undergo several tests, first with the wing-weight test case, then with a computationally challenging code with up to $\text{◂,▸}k=30,000$ inputs, and finally with the traditional Level E benchmark code.     

Das Potenzial der Video-Interaktionsanalyse als Beratungskonzept von Gr��ndungsteams

Consulting for entrepreneurs is an ambitious and multidimensional consulting business. Beside formal operations and guidelines, this consulting should be the focus of collaboration and learning processes in groups and teams, which is necessary for integration into the consulting process. What happens in the face-to-face interaction in these entrepreneur groups and teams? What do they negotiate and solve on the interactional level to increase their chances of success? The authors describe video interaction analysis as an optional relevant practice for consulting entrepreneurs. This analysis is the groundwork for developing a learning workshop design which focuses on the dynamic team-learning aspect and facilitates the learning process of the team.     

Konsensuale Effizenzbewertung und -verbesserung mittels DEA

This contribution is a new DEA approach in as much as it permits the decision making units (DMUs) to improve their respective efficiencies from the view of a peer rather than form their own self-appraisal attitudes. These authors study the output oriented model under constant scale efficiencies and first develop how a DMU can improve its performance by radial output increase or even by free output variation??in the light of the weight system of an arbitrary peer. The results are an improved cross-efficiency matrix and a maximum cross-efficiency matrix. Either of these matrices may serve as an appropriate instrument for a consensual choice of a peer??consensual among all DMUs. Input oriented models as well as simultaneous input/output considerations amend the so far developed results. A suitable example demonstrates all aspects of the new approach.     

Dimensional Latent Structure of Relationship Quality: Results of Three Representative Population Samples

A fundamental question facing every latent construct (e.g., relationship quality) is whether the construct is categorical or dimensional in nature. The authors analyzed the latent status of relationship quality in 3 large general German population samples using the Partnership Questionnaire (PFB), the Quality of Relationship Inventory, and the short form of the PFB (PFB‐K; N₁ = 1,330, N₂ = 1,494, N₃ = 1,390). They applied 3 widely used taxometric methods: (a) MAXEIG, (b) MAMBAC, and (c) L‐Mode. Simulation data were created to evaluate the comparison curve fit index values, which were below 0.5 across the 3 German population samples, supporting a dimensional solution. Hence, in the current study the latent structure of relationship quality encompassed differences in degree rather than kind. Implications of the dimensional latent status of relationship quality, as well as causes of the discrepant findings with previous taxometric studies, are discussed.