Resilience,Leadership and Work Engagement: The Mediating Role of Positive Affect

This study examines the influence of resilience and transformational leadership on work engagement, and it investigates the mediating effect of positive affect. A total of 422 employees at a large IT company participated the survey. Participants completed established measures of resilience, transformational leadership, positive affect, and work engagement. The results indicate that resilience and transformational leadership are positively related to work engagement. Structural equation modeling analysis shows that positive affect partially mediates the relationships between resilience, transformational leadership, and work engagement. Theoretical contributions, practical implications, and future research directions are discussed.     

Zur Bedeutung von Faultlines als Grenzen bei der Teamarbeit

This report focuses on the divisions within and between teams. Such research is often associated with the phenomenon of “diversity”. Empirical evidence regarding the effects of diversity on teamwork is inconsistent, partly due to methodological limitations. We propose to use the concept of “faultlines” as hypothetical lines that divide teams into homogenous subgroups. Measuring faultlines as an objective indicator of team composition represents an innovative method for integrating several diversity attributes (e.?g., age, gender, and ethnicity). We provide an overview of the current research on faultlines and show detrimental effects of faultlines on team performance. Additionally, we present the results of two studies from the automotive industry, focusing on the relation between faultlines and organizational outcomes such as employee health, team performance and team identification. Finally, we conclude that the analysis of objective faultlines offers relevant implications for research and practice. Future research should also consider the role of subjectively experienced faultlines.     

Transient detailed balance and product form for reaction networks

This paper explores the boundary of the set of reaction networks that have an exact transient (truncated) multidimensional Poisson or product-form distribution for the number of particles of different types. Motivated by the birth–death process, we introduce the notions of transient detailed balance and delay functions, and use these notions to obtain the novel transient product-form distribution in a coagulation-fragmentation process for polymers with a tree-like structure from that of the pure coagulation process.     

MIMCA: multiple imputation for categorical variables with multiple correspondence analysis

We propose a multiple imputation method to deal with incomplete categorical data. This method imputes the missing entries using the principal component method dedicated to categorical data: multiple correspondence analysis (MCA). The uncertainty concerning the parameters of the imputation model is reflected using a non-parametric bootstrap. Multiple imputation using MCA (MIMCA) requires estimating a small number of parameters due to the dimensionality reduction property of MCA. It allows the user to impute a large range of data sets. In particular, a high number of categories per variable, a high number of variables or a small number of individuals are not an issue for MIMCA. Through a simulation study based on real data sets, the method is assessed and compared to the reference methods (multiple imputation using the loglinear model, multiple imputation by logistic regressions) as well to the latest works on the topic (multiple imputation by random forests or by the Dirichlet process mixture of products of multinomial distributions model). The proposed method provides a good point estimate of the parameters of the analysis model considered, such as the coefficients of a main effects logistic regression model, and a reliable estimate of the variability of the estimators. In addition, MIMCA has the great advantage that it is substantially less time consuming on data sets of high dimensions than the other multiple imputation methods.     

Dose‐Response Models Incorporating Aerosol Size Dependency for Francisella tularensis

The effect of bioaerosol size was incorporated into predictive dose‐response models for the effects of inhaled aerosols of Francisella tularensis (the causative agent of tularemia) on rhesus monkeys and guinea pigs with bioaerosol diameters ranging between 1.0 and 24 μm. Aerosol‐size‐dependent models were formulated as modification of the exponential and β‐Poisson dose‐response models and model parameters were estimated using maximum likelihood methods and multiple data sets of quantal dose‐response data for which aerosol sizes of inhaled doses were known. Analysis of F. tularensis dose‐response data was best fit by an exponential dose‐response model with a power function including the particle diameter size substituting for the rate parameter k scaling the applied dose. There were differences in the pathogen's aerosol‐size‐dependence equation and models that better represent the observed dose‐response results than the estimate derived from applying the model developed by the International Commission on Radiological Protection (ICRP, 1994) that relies on differential regional lung deposition for human particle exposure.     

The impact of child support on child health

The broad goals of child support policy are to keep children in single-parent families out of poverty and to make sure that their material needs are met. One potentially important, but relatively understudied, set of measures of child well-being are health outcomes. A fixed-effects analysis of data from the Child and Young Adult file of the 1979 National Longitudinal Survey of Youth shows that, conditional upon receipt of some amount of child support, higher payment levels are associated with significantly greater odds of having private health insurance coverage and significantly lower odds of poor or declining health status. These effects persist even after controlling for other factors that are likely to be correlated with child support payments, including total family income and paternal visitation patterns.     

An Extended Single‐index Model with Missing Response at Random

An extended single‐index model is considered when responses are missing at random. A three‐step estimation procedure is developed to define an estimator for the single‐index parameter vector by a joint estimating equation. The proposed estimator is shown to be asymptotically normal. An algorithm for computing this estimator is proposed. This algorithm only involves one‐dimensional nonparametric smoothers, thereby avoiding the data sparsity problem caused by high model dimensionality. Some simulation studies are conducted to investigate the finite sample performances of the proposed estimators.     

Multivariate log-skewed distributions with normal kernel and their applications

We introduce two classes of multivariate log-skewed distributions with normal kernel: the log canonical fundamental skew-normal (log-CFUSN) and the log unified skew-normal. We also discuss some properties of the log-CFUSN family of distributions. These new classes of log-skewed distributions include the log-normal and multivariate log-skew normal families as particular cases. We discuss some issues related to Bayesian inference in the log-CFUSN family of distributions, mainly we focus on how to model the prior uncertainty about the skewing parameter. Based on the stochastic representation of the log-CFUSN family, we propose a data augmentation strategy for sampling from the posterior distributions. This proposed family is used to analyse the US national monthly precipitation data. We conclude that a high-dimensional skewing function lead to a better model fit.     

Relations Between Alphabetized Name Order and Nomination Counts in Peer Nomination Measures

Peer nominations, a central method for measuring peer relationships in developmental research, typically involve asking children or adolescents to choose peers who fit various criteria from an alphabetized roster of classmates or grade‐mates. Although such measures have been used for decades, very little research has investigated the effects of alphabetical name order on the number of nominations received by peers. This study collected peer nominations for 20 items among 607 eighth grade participants in two schools. Regression analyses showed that earlier name order significantly predicted higher nomination counts for eight of the items, and explained over 5 percent of the variance in four affective variables (friendship, acceptance, acquaintanceship, and received liking). Across variables, name order effects were negatively correlated with internal reliability of nominations, implying that order effects may be related to the consensus of the peer group. Name order also had a minimal effect on inter‐correlations among a subset of variables. Implications and concrete recommendations for controlling and reducing name order effects in future research are discussed.