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.     

College and university compliance with a required meningococcal vaccination law

OBJECTIVE: Maryland became the first state to pass a vaccination law requiring college and university students living on campus to obtain a meningococcal vaccination or to sign a waiver refusing vaccination because college students are at increased risk for disease. The authors sought to identify how Maryland colleges addressed the law and determine whether schools were in full compliance. PARTICIPANTS: The authors surveyed 32 college/university administrators via a self-administered questionnaire. METHODS: The authors calculated vaccination and waiver rates and assessed compliance with the law overall and with specific law components. RESULTS: Among 28 participating schools, annual vaccination rates and waiver rates among students during 2000-2004 ranged from 66%-76% and 12%-17%, respectively. Two (7%) schools were compliant with all components of the law. CONCLUSIONS: Mandatory vaccination laws do not ensure compliance at the college and university level. Mandatory reporting, increased education, and collaboration between colleges and universities and public health agencies are needed.     

OLS and IV estimation of regression models including endogenous interaction terms

We analyze a class of linear regression models including interactions of endogenous regressors and exogenous covariates. We show how to generate instrumental variables using the nonlinear functional form of the structural equation when traditional excluded instruments are unknown. We propose to use these instruments with identification robust IV inference. We furthermore show that, whenever functional form identification is not valid, the ordinary least squares (OLS) estimator of the coefficient of the interaction term is consistent and standard OLS inference applies. Using our alternative empirical methods we confirm recent empirical findings on the nonlinear causal relation between financial development and economic growth.