Working at night and work ability among nursing personnel: when precarious employment makes the difference

To test the association between night work and work ability, and verify whether the type of contractual employment has any influence over this association. Permanent workers (N = 642) and workers with precarious jobs (temporary contract or outsourced; N = 552) were interviewed and filled out questionnaires concerning work hours and work ability index. They were classified into: never worked at night, ex-night workers, currently working up to five nights, and currently working at least six nights/2-week span. After adjusting for socio-demography and work variables, current night work was significantly associated with inadequate WAI (vs. day work with no experience in night work) only for precarious workers (OR 2.00, CI 1.01-3.95 and OR 1.85, CI 1.09-3.13 for those working up to five nights and those working at least six nights in 2 weeks, respectively). Unequal opportunities at work and little experience in night work among precarious workers may explain their higher susceptibility to night work.

Modifications and Alternatives to the Tests of Levene and Brown & Forsythe for Equality of Variances and Means

The usual tests to compare variances and means (e. g. Bartlett`s test and F-test) assume that the sample comes from a normal distribution. In addition, the test for equality of means requires the assumption of homogeneity of variances. In some situation those assumptions are not satisfied, hence we may face problems like excessive size and low power. In this paper, we describe two tests, namely the Levene`s test for equality of variances, which is robust under nonnormality; and the Brown and Forsythe`s test for equality of means. We also present some modifications of the Levene`s test and Brown and Forsythe`s test, proposed by different authors. We analyzed and applied one modified form of Brown and Forsythe`s test to a real data set. This test is a robust alternative under nonnormality, heteroscedasticity and also when the data set has influential observations. The equality of variance can be well tested by Levene`s test with centering at the sample median.