The relationship between presenteeism and job satisfaction: A mediated moderation model using work engagement and perceived organizational support

Research on presenteeism has largely focused on its prevalence, determinants and consequences on the health of both employees and organizations. To date, few studies have examined the influence of presenteeism on workers’ attitudinal and motivational responses. Based on the Effort–Recovery Theory, this study evaluates the mediating effect of work engagement in the relationship between presenteeism and job satisfaction. Building on previous work, this study also proposes to consider perceived organizational support as a moderator of the work engagement–job satisfaction relationship. Presented in the form of a mediated moderation model, the results of bootstrapped regression analyses show three main results. First, presenteeism is negatively associated with work engagement and job satisfaction. Secondly, work engagement is one of the factors through which presenteeism influences job satisfaction. Thirdly, perceived organizational support moderates the association between work engagement and job satisfaction so that, at a low level of work engagement, feeling supported by the organization makes a difference on job satisfaction. Overall, this research is part of the limited number of studies that have focused on the interplay existing between presenteeism and its consequences in terms of job attitudes.     

Isotone additive latent variable models

For manifest variables with additive noise and for a given number of latent variables with an assumed distribution, we propose to nonparametrically estimate the association between latent and manifest variables. Our estimation is a two step procedure: first it employs standard factor analysis to estimate the latent variables as theoretical quantiles of the assumed distribution; second, it employs the additive models’ backfitting procedure to estimate the monotone nonlinear associations between latent and manifest variables. The estimated fit may suggest a different latent distribution or point to nonlinear associations. We show on simulated data how, based on mean squared errors, the nonparametric estimation improves on factor analysis. We then employ the new estimator on real data to illustrate its use for exploratory data analysis.     

Impact of physical activity and sedentary behaviour on fall risks in older people: a systematic review and meta-analysis of observational studies

The objective of this work was to summarise and evaluate the evidence showing that physical activity is a protector factor as regards falls in older people. Relevant studies were identified through a systematic search in the MEDLINE and Cochrane Library, under the keywords of accidental fall/numerical data and risk factors, and with the bibliographies of retrieved papers. The combined odds ratio (OR) [95% confidence interval] for physical activity was 0.75 [0.64, 0.88] with moderate heterogeneity (I ²?=?33%). For fall injury, it was 0.59 [0.47, 0.74] and, for falls in general, it rose to 0.94 [0.76, 1.17] with nil heterogeneity. The combined OR for sedentary factors was 1.14 [1.10, 1.82] with moderate heterogeneity (I ²?=?36%). Regular physical activity in daily life yields significant reduction in falls in older people, especially falls with injuries.     

Adaptive shrinkage of singular values

To recover a low-rank structure from a noisy matrix, truncated singular value decomposition has been extensively used and studied. Recent studies suggested that the signal can be better estimated by shrinking the singular values as well. We pursue this line of research and propose a new estimator offering a continuum of thresholding and shrinking functions. To avoid an unstable and costly cross-validation search, we propose new rules to select two thresholding and shrinking parameters from the data. In particular we propose a generalized Stein unbiased risk estimation criterion that does not require knowledge of the variance of the noise and that is computationally fast. A Monte Carlo simulation reveals that our estimator outperforms the tested methods in terms of mean squared error on both low-rank and general signal matrices across different signal-to-noise ratio regimes. In addition, it accurately estimates the rank of the signal when it is detectable.     

Estimation for seasonal fractional ARIMA with stable innovations via the empirical characteristic function method

Seasonal fractional ARIMA (ARFISMA) model with infinite variance innovations is used in the analysis of seasonal long-memory time series with large fluctuations (heavy-tailed distributions). Two methods, which are the empirical characteristic function (ECF) procedure developed by Knight and Yu [The empirical characteristic function in time series estimation. Econometric Theory. 2002;18:691–721] and the Two-Step method (TSM) are proposed to estimate the parameters of stable ARFISMA model. The ECF method estimates simultaneously all the parameters, while the TSM considers in the first step the Markov Chains Monte Carlo–Whittle approach introduced by Ndongo et al. [Estimation of long-memory parameters for seasonal fractional ARIMA with stable innovations. Stat Methodol. 2010;7:141–151], combined with the maximum likelihood estimation method developed by Alvarez and Olivares [Méthodes d'estimation pour des lois stables avec des applications en finance. Journal de la Société Française de Statistique. 2005;1(4):23–54] in the second step. Monte Carlo simulations are also used to evaluate the finite sample performance of these estimation techniques.     

How choice proliferation affects revealed preferences

Theory and Decision - Whereas the literature on choice overload has shown that people tend to defer their choice or experience less satisfaction under choice proliferation, this paper aims to test...     

Application of the Bootstrap Approach to the Choice of Dimension and the α Parameter in the SIRα Method

To reduce the dimensionality of regression problems, sliced inverse regression approaches make it possible to determine linear combinations of a set of explanatory variables X related to the response variable Y in general semiparametric regression context. From a practical point of view, the determination of a suitable dimension (number of the linear combination of X) is important. In the literature, statistical tests based on the nullity of some eigenvalues have been proposed. Another approach is to consider the quality of the estimation of the effective dimension reduction (EDR) space. The square trace correlation between the true EDR space and its estimate can be used as goodness of estimation. In this article, we focus on the SIR_α method and propose a naïve bootstrap estimation of the square trace correlation criterion. Moreover, this criterion could also select the α parameter in the SIR_α method. We indicate how it can be used in practice. A simulation study is performed to illustrate the behavior of this approach.     

Sexual Health Services for Homeless Youth: A Qualitative Analysis of their Experiences

The sexual health problems experienced by homeless youth bring into question their use of available sexual health services. Using a qualitative typological analysis, this study aims to identify sexual health services utilization profiles for homeless youth, and to understand the role of the homeless experience on the utilization of sexual health services. Individual interviews were conducted with 33 homeless youth (17 men, 16 women) between 18 and 25?years of age. Typological analysis identified four profiles: (1) a targeted use of sexual health services to determine one’s serological status following a relational change during the homeless experience; (2) a limited use of sexual health services to manage sexual emergencies that arise during the homeless experience; (3) a regular use of sexual health services to avoid the risks associated with the homeless experience; (4) a frequent use of sexual health services to obtain support when engaging in prostitution during the homeless experience. This study shows that the precarious and unstable conditions within the homeless experience incite youth to utilize sexual health services in different ways. These findings point to the importance of rethinking sexual health services to better adapt them to the different utilization profiles of homeless youth.