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.     

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.     

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 Explanatory Model of Dating Violence Risk Factors in Spanish Adolescents

Dating violence is a serious public health issue that needs further understanding in terms of risk factors that may be involved in it. The main goal of this study was to test a mediational model of dating violence risk factors. The sample was composed of 477 secondary and college students from Spain (59% females). A dynamic developmental explanatory model considering aggressiveness, insecure attachment, interparental conflict, and peer dating violence was tested using a multigroup structural equation model. Aggressiveness partially mediated the relation between anxious attachment and dating violence and fully mediated the association between interparental conflict resolution and dating violence. Furthermore, perceived peer dating violence was a direct predictor of dating violence. Implications for prevention and intervention plans are discussed.     

Multilevel maximum likelihood estimation with application to covariance matrices

The asymptotic variance of the maximum likelihood estimate is proved to decrease when the maximization is restricted to a subspace that contains the true parameter value. Maximum likelihood estimation allows a systematic fitting of covariance models to the sample, which is important in data assimilation. The hierarchical maximum likelihood approach is applied to the spectral diagonal covariance model with different parameterizations of eigenvalue decay, and to the sparse inverse covariance model with specified parameter values on different sets of nonzero entries. It is shown computationally that using smaller sets of parameters can decrease the sampling noise in high dimension substantially.     

How Moroccan‐Dutch parents learn in communities of practice: Evaluating a bottom‐up parenting programme

Research shows that top‐down‐designed parenting programmes do not always meet the needs of postmigration parents. Bottom‐up programmes by migrant organizations hold a promise to fill this gap; however, research about these programmes and appropriate evaluation methodologies is scarce. Drawing upon Wenger's ( 2010 ) “communities of practice,” this paper explores an alternative perspective on parenting programme evaluation. Findings are presented from a study looking into social learning processes of postmigration parents who participated in a bottom‐up programme about raising teenagers in urban areas. Using an ethnographically inspired method combined with a preprogramme and postprogramme design, 115 Moroccan‐Dutch mothers and fathers from 15 programme groups participated. Results show that the programme provided a social learning space in which parents used themselves as resources to learn collectively about parenting. Moreover, parents consciously engaged in learning interactions across learning spaces stretched into their social networks. These analyses showed how parents' development of “learning citizenship” (Wenger, 2009 ) provides us with insight in collective learning dimensions present in a bottom‐up parenting programme, which is often not included in evaluation studies. Implications for practitioners as facilitators of parents' collective learning are presented.     

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.     

The Relation Between Therapeutic Alliance and Treatment Motivation in Residential Youth Care: A Cross-Lagged Panel Analysis

This study investigated the longitudinal relation between therapeutic alliance and treatment motivation in a sample of 174 adolescents receiving residential treatment in the Netherlands. Structural equation modeling with a cross-lagged panel design was used to examine the relation between therapeutic alliance and treatment motivation up to 9 months of treatment. Results revealed that autoregressive associations between initial therapeutic alliance and alliance at subsequent time points were significant, whereas for treatment motivation a significant association was found after 6 months, but not after 9 months. Results also showed that a higher level of therapeutic alliance after 3 months was predictive of a higher level of treatment motivation after 6 months. Furthermore, a higher level of therapeutic alliance after 6 months was predictive of a higher level of treatment motivation after 9 months. Implications for research and clinical practice are discussed.