Engaging leader – Engaged employees? A cross-lagged study on employee engagement

The goal of this study is to provide a cross-lagged examination of the relationships between engaging leadership, job resources and employee work engagement. We propose a mediation model and we postulate that engaging leadership can increase perceptions of three specific job resources (i.e. autonomy, support from colleagues and opportunities for learning and development) which theoretically correspond to the three facets of engaging leadership (i.e., inspiring, connecting and strengthening, respectively). Subsequently, in keeping with the extant body of Job Demands-Resources (JD-R) research, we link job resources to employee work engagement. Our hypotheses were tested on data collected at two time-points – T1 (N = 759) and T2 (N = 273) –from employees working for a hotel chain in the Netherlands. In line with our expectations, engaging leadership showed a significant cross-lagged relationship with autonomy and support from colleagues, but did not predict learning opportunities and work engagement across time. While we formulated specific hypotheses, we also tested reversed causation relationships. We found no direct effect from engaging leadership on employee work engagement, however, the reversed effect was significant; employee perceptions of engaging leadership were shaped by their own engagement experiences. Importantly, engaged employees at T1 reported more job resources at T2. By providing a cross-lagged examination of our model, we showed that engaging leaders as well as employees’ positive affective state of being engaged, are essential to shaping a resourceful work context. A comprehensive view on the triggers and outcomes of work engagement and engaging leadership is needed, as the traditional unidirectional cause-effect rationale fails to explain how these concepts relate to one another and to employee experiences of job resources.     

Real nonparametric regression using complex wavelets

Summary.  Wavelet shrinkage is an effective nonparametric regression technique, especially when the underlying curve has irregular features such as spikes or discontinuities. The basic idea is simple: take the discrete wavelet transform of data consisting of a signal corrupted by noise; shrink or remove the wavelet coefficients to remove the noise; then invert the discrete wavelet transform to form an estimate of the true underlying curve. Various researchers have proposed increasingly sophisticated methods of doing this by using real-valued wavelets. Complex-valued wavelets exist but are rarely used. We propose two new complex-valued wavelet shrinkage techniques: one based on multiwavelet style shrinkage and the other using Bayesian methods. Extensive simulations show that our methods almost always give significantly more accurate estimates than methods based on real-valued wavelets. Further, our multiwavelet style shrinkage method is both simpler and dramatically faster than its competitors. To understand the excellent performance of this method we present a new risk bound on its hard thresholded coefficients.     

Regularization through variable selection and conditional MLE with application to classification in high dimensions

It is often the case that high-dimensional data consist of only a few informative components. Standard statistical modeling and estimation in such a situation is prone to inaccuracies due to overfitting, unless regularization methods are practiced. In the context of classification, we propose a class of regularization methods through shrinkage estimators. The shrinkage is based on variable selection coupled with conditional maximum likelihood. Using Stein's unbiased estimator of the risk, we derive an estimator for the optimal shrinkage method within a certain class. A comparison of the optimal shrinkage methods in a classification context, with the optimal shrinkage method when estimating a mean vector under a squared loss, is given. The latter problem is extensively studied, but it seems that the results of those studies are not completely relevant for classification. We demonstrate and examine our method on simulated data and compare it to feature annealed independence rule and Fisher's rule.     

A ‘nondecimated’ lifting transform

Classical nondecimated wavelet transforms are attractive for many applications. When the data comes from complex or irregular designs, the use of second generation wavelets in nonparametric regression has proved superior to that of classical wavelets. However, the construction of a nondecimated second generation wavelet transform is not obvious. In this paper we propose a new ‘nondecimated’ lifting transform, based on the lifting algorithm which removes one coefficient at a time, and explore its behavior. Our approach also allows for embedding adaptivity in the transform, i.e. wavelet functions can be constructed such that their smoothness adjusts to the local properties of the signal. We address the problem of nonparametric regression and propose an (averaged) estimator obtained by using our nondecimated lifting technique teamed with empirical Bayes shrinkage. Simulations show that our proposed method has higher performance than competing techniques able to work on irregular data. Our construction also opens avenues for generating a ‘best’ representation, which we shall explore.     

The meaning of frailty according to Dutch older frail and non-frail persons

Frailty is a term often used by researchers and clinicians to describe a state in which older persons are at risk for adverse outcomes such as falls, disability, institutionalization and mortality. However, no study so far examined what frailty means to older persons. Therefore the aim of this study was to describe the meaning that older community-dwelling persons attach to frailty. Twenty-five semi-structured interviews were conducted. The interviews were analyzed using the grounded theory method. Frailty was described as being in poor health, having walking difficulties, feeling down, being anxious, having few social contacts and not being able to do the things one likes to do. Men described in more detail the physical dimension whereas women elaborated in more depth on the psychological and social component. Existing definitions of frailty should be adjusted to better reflect the meaning of frailty for older persons.     

On the margins of the child protection system: creating space for relational social work practice

In the UK, a threshold divides between two categories of children, child protection (CP) and child in need. Each category tends to be treated as a homogeneous entity, despite containing heterogeneous levels and forms of risk and need. CP practice, accompanied by regulation, protocols and procedures, aspires to achieve a coordinated multi‐agency response to identified concerns with available resources targeted towards this category. However, it is well known that those children assessed as falling just below the CP threshold can still have high levels of need and risk, requiring a level of social work involvement beyond the low‐resource and low‐oversight model that generally accompanies a child in need categorisation. This paper probes an approach to practice, which divides levels of risk within the child in need category enabling adequate, coordinated support and oversight to be provided for children and families with complex needs. Evidence from our study evaluating this approach suggests that a simple protocol provided a clear process within, which social workers and agency partners felt confident and safe to practice outside of the formal CP framework. The protocol prevented drift and helped to create a space within, which relational social work practice flourished.