Spss statistical software v 22

Statistical analyses of these data were conducted with SPSS (IBM SPSS Statistical Software V 22, Chicago, IL). Homogeneity of the between-group data was evaluated with Levene's test of equality of variances reporting no significant differences. Independent samples t-test was completed for between sex group differences. The within group differences were conducted using analysis of variance (ANOVA) between each time point, 0 M, 3 M, and 6 M. Linear regression modeling was used with all the time points combined (n = 30) to determine the primary noninvasive predictors of the FLI which included total fat mass, trunk fat mass, nontrunk fat mass, body fat percentage, and total weight. Leptin and soluble leptin receptor were excluded from the linear regression model testing as they are central to the independent variable we set out to predict. Significance was set at a two-tailed alpha of p ≤ 0.05.

To estimate educational inequalities in the relative differences in labour force participation among persons with a chronic illness, a counterfactual scenario was introduced. In this scenario, it is assumed that the relative difference in labour force participation between those with a chronic illness and those without in the lower educational group can be reduced to the relative difference observed in the higher educational group.26 (link) The estimation of the excess relative difference in paid employment for chronic illness status in lower educated workers is a fair reflection of educational inequalities between low and high-educated persons. The Cox proportional hazard model was used to estimate this excess relative difference by the formula: (bèta for relative difference in the low-educated group—beta for relative difference in the high-educated group)/(bèta of the relative difference in the low-educated group).
The main analysis was performed using Stata statistical software V.14 (StataCorp). The estimates of paid employment per country and the trend analysis were conducted in IBM SPSS statistical software V.22 (SPSS).

All statistical analyses were performed using IBM SPSS statistical software (v22). Descriptive statistics, Chi-square tests, 2-sample independent t-tests and one way ANOVAS were used to examine how representative the current sample was compared to previously published population-based samples. The association between manager psychosocial safety climate (MPSC), manager behaviour (MB), and each of the mental health outcomes was examined using linear regression. Scores for MPSC and MB were each divided into five evenly sized groups (quintiles). Multiple hierarchical linear regression analyses were conducted to assess whether either workplace variable (MPSC and MB) were significant independent predictors of employee mental health outcomes (symptoms of CMD and mental well-being) after adjusting for relevant sociodemographic variables (age range, gender, location, type of employment).