Fatigue and workability in Brazilian textile companies in different corporate social responsibility score groups

The aim of this study was to evaluate working conditions in the textile industry for different stages of Corporate Social Responsibility (CSR) development, and workers` perception of fatigue and workability. A cross-sectional study was undertaken with 126 workers in the production areas of five Brazilian textile plants. The corporate executive officers and managers of each company provided their personal evaluations of CSR. Companies were divided into 2 groups (higher and lower) of CSR scores. Workers completed questionnaires on fatigue, workability and working conditions. Ergonomic job analysis showed similar results for working conditions, independent of their CSR score. Multivariate analysis models were developed for fatigue and workability, indicating that they are both associated to factors related to working conditions and individual workers` characteristics and life styles. Work organization, (what, how, when, where and for how long the work is done), is also an associated factor for fatigue. This study suggests that workers` opinions should be taken into greater consideration when companies develop their CSR programs, in particular for those relating to working conditions. Relevance to industry: This paper underlines the importance of considering working conditions and workers` opinions of them, work organization and individual workers` characteristics and life styles in order to restore or to maintain workability and to reduce fatigue, independently of how developed a company may be in the field of Corporate Social Responsibility. (C) 2010 Elsevier B.V. All rights reserved.

Improved score tests in symmetric linear regression models

The class of symmetric linear regression models has the normal linear regression model as a special case and includes several models that assume that the errors follow a symmetric distribution with longer-than-normal tails. An important member of this class is the t linear regression model, which is commonly used as an alternative to the usual normal regression model when the data contain extreme or outlying observations. In this article, we develop second-order asymptotic theory for score tests in this class of models. We obtain Bartlett-corrected score statistics for testing hypotheses on the regression and the dispersion parameters. The corrected statistics have chi-squared distributions with errors of order O(n(-3/2)), n being the sample size. The corrections represent an improvement over the corresponding original Rao`s score statistics, which are chi-squared distributed up to errors of order O(n(-1)). Simulation results show that the corrected score tests perform much better than their uncorrected counterparts in samples of small or moderate size.

Small-Sample Corrections for Score Tests in Birnbaum-Saunders Regressions

In this article, we deal with the issue of performing accurate small-sample inference in the Birnbaum-Saunders regression model, which can be useful for modeling lifetime or reliability data. We derive a Bartlett-type correction for the score test and numerically compare the corrected test with the usual score test and some other competitors.

Three Bartlett-type corrections for score statistics in symmetric nonlinear regression models

We present simple matrix formulae for corrected score statistics in symmetric nonlinear regression models. The corrected score statistics follow more closely a chi (2) distribution than the classical score statistic. Our simulation results indicate that the corrected score tests display smaller size distortions than the original score test. We also compare the sizes and the powers of the corrected score tests with bootstrap-based score tests.