Workgroup interdependence and satisfaction from a developmental perspective: the moderating role of the group’s life cycle stage / Interdependencia y satisfacción en el grupo de trabajo desde la perspectiva del desarrollo: el rol moderador de la fase del ciclo de vida grupal

From a developmental perspective, workgroups adjust and modify the way they function over time. The present study aims to test, at the group level, the moderating role of the group’s life phase in the relationship between workgroup interdependence ties in terms of task, outcome and functions, assessed by social network density and group satisfaction. A sample of 84 teams from various companies participated in this research. The results showed that the group’s development phase determines the way that outcome interdependence density in teams is associated with satisfaction and reveal the existence of differences between the second and fourth development stages. Therefore, the group’s life cycle stage should be considered as a relevant variable in research and intervention with groups in the workplace.     

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.     

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.     

Disruption in Parental Co-habitation and its Effects on Short-Term,Medium-Term,and Long-Term Outcomes of Adolescents

In this paper the relationships between a disruption in parental co-habitation and various categories of adolescent outcomes over multiple time horizons are explored. Using data from the National Longitudinal Study of Adolescent Health (Add Health), we estimated the effects of a change from living with both parents to just one, on academic and employment outcomes, the likelihood to indulge in risky behaviors, mental health outcomes and body mass index measures, from less than 1 year to over 14 years after the change. Propensity score matching methods were used to control for individual characteristics and pre-existing differences in the family environment that may increase the chances of separation, and the results are compared to those obtained using ordinary least squares or probit methods. Results showed evidence of adverse effects of living with one parent in the short term, medium term and long term. Adolescents living with one parent had lower academic achievement in all term lengths, poor mental health in the short to medium term, and were more likely to engage in risky behaviors in the medium to long term.     

The FastHCS algorithm for robust PCA

Principal component analysis (PCA) is widely used to analyze high-dimensional data, but it is very sensitive to outliers. Robust PCA methods seek fits that are unaffected by the outliers and can therefore be trusted to reveal them. FastHCS (high-dimensional congruent subsets) is a robust PCA algorithm suitable for high-dimensional applications, including cases where the number of variables exceeds the number of observations. After detailing the FastHCS algorithm, we carry out an extensive simulation study and three real data applications, the results of which show that FastHCS is systematically more robust to outliers than state-of-the-art methods.