Abell 2384: the galaxy population of a cluster post-merger

We combine multi-object spectroscopy from the 2dF and EFOSC2 spectrographs with optical imaging of the inner 30'x30' of A2384 taken with the ESO Wide Field Imager. We carry out a kinematical analysis using the EMMIX algorithm and biweight statistics. We address the possible presence of cluster substructures with the Dressler-Shectman test. Cluster galaxies are investigated with respect to [OII] and H{\alpha} equivalent width. Galaxies covered by our optical imaging observations are additionally analysed in terms of colour, star formation rate and morphological descriptors such as Gini coefficient and M20 index. We study cluster galaxy properties as a function of clustercentric distance and investigate the distribution of various galaxy types in colour-magnitude and physical space. The Dressler-Shectman test reveals a substructure in the east of the 2dF field-of-view. We determine the mass ratio between the northern and southern subcluster to be 1.6:1. In accordance with other cluster studies, we find that a large fraction of the disk galaxies close to the cluster core show no detectable star formation. Probably these are systems which are quenched due to ram-pressure stripping. The sample of quenched disks populates the transition area between the blue cloud and the red sequence in colour-magnitude space. We also find a population of morphologically distorted galaxies in the central cluster region. The substructure in the east of A2384 might be a group of galaxies falling onto the main cluster. We speculate that our sample of quenched spirals represents an intermediate phase in the ram-pressure driven transformation of infalling field spirals into cluster S0s. This is motivated by their position in colour-magnitude space. The occurrence of morphologically distorted galaxies in the cluster core complies with the hypothesis of A2384 representing a post merger system.Comment: 14 pages, 18 figures, A&A accepte

The Effect of Incentives in Non-Routine Analytical Team Tasks - Evidence From a Field Experiment

Despite the prevalence of non-routine analytical team tasks in modern economies, little is known about how incentives influence performance in these tasks. In a field experiment with more than 3000 participants, we document a positive effect of bonus incentives on the probability of completion of such a task. Bonus incentives increase performance due to the reward rather than the reference point (performance threshold) they provide. The framing of bonuses (as gains or losses) plays a minor role. Incentives improve performance also in an additional sample of presumably less motivated workers. However, incentives reduce these workers' willingness to "explore" original solutions

The effect of incentives in non-routine analytical team tasks

Despite the prevalence of non-routine analytical team tasks in modern economies, little is known about how incentives influence performance in these tasks. In a series of field experiments with more than 5,000 participants, we investigate how incentives alter behavior in teams working on such a task. We document a positive effect of bonus incentives on performance, even among strongly intrinsically motivated teams. Bonuses also transform team organization as they enhance teams’ demand for leadership. Exogenously increasing the demand for leadership establishes a causal link that explains a large part of the observed bonus-induced performance improvemen

The effect of incentives in non-routine analytical team tasks

Despite the prevalence of non-routine analytical team tasks in modern economies, little is known about how incentives influence performance in these tasks. In a series of field experiments with more than 5,000 participants, we investigate how incentives alter behavior in teams working on such a task. We document a positive effect of bonus incentives on performance, even among strongly intrinsically motivated teams. Bonuses also transform team organization as they enhance teams’ demand for leadership. Exogenously increasing the demand for leadership establishes a causal link that explains a large part of the observed bonus-induced performance improvemen

How to deal with non-detectable and outlying values in biomarker research: Best practices and recommendations for univariate imputation approaches

Non-detectable (ND) and outlying concentration values (OV) are a common challenge of biomarker investigations. However, best practices on how to aptly deal with the affected cases are still missing. The high methodological heterogeneity in biomarker-oriented research, as for example, in the field of psychoneuroendocrinology, and the statistical bias in some of the applied methods may compromise the robustness, comparability, and generalizability of research findings. In this paper, we describe the occurrence of ND and OV in terms of a model that considers them as censored data, for instance due to measurement error cutoffs. We then present common univariate approaches in handling ND and OV by highlighting their respective strengths and drawbacks. In a simulation study with lognormal distributed data, we compare the performance of six selected methods, ranging from simple and commonly used to more sophisticated imputation procedures, in four scenarios with varying patterns of censored values as well as for a broad range of cutoffs. Especially deletion, but also fixed-value imputations bear a high risk of biased and pseudo-precise parameter estimates. We also introduce censored regressions as a more sophisticated option for a direct modeling of the censored data. Our analyses demonstrate the impact of ND and OV handling methods on the results of biomarker-oriented research, supporting the need for transparent reporting and the implementation of best practices. In our simulations, the use of imputed data from the censored intervals of a fitted lognormal distribution shows preferable properties regarding our established criteria. We provide the algorithm for this favored routine for a direct application in R on the Open Science Framework (https://osf.io/spgtv). Further research is needed to evaluate the performance of the algorithm in various contexts, for example when the underlying assumptions do not hold. We conclude with recommendations and potential further improvements for the field