Data that did not pass normality test were log transformed and normality test was repeated.
After data testing for normality using the D'Agostino & Pearson omnibus normality test, a repeated measures ANOVA (analysis of variance) was used to compare the effect of time on sample stability.
After exploring the change in scores from baseline to follow-up for each group with a paired t test or Wilcoxon's signed-rank test in case of non-normality, repeated-measures ANOVA analyses using general linear modeling in SPSS 14.0 were used.
Assumptions of normality were met for repeated measures ANOVA which revealed a significant group (high vs low competitiveness) × time (pre- to post-competitors) interaction F 1,12) = 13.1, P = 0.003.
The background-subtracted intensity values were analyzed for normality using Lilliefors test, and either repeated measures ANOVA RMANOVAA) (for normal distribution) or Freidman tests (for non-normal distribution) were used to estimate the statistical significance.
Following testing for normality, Neuroscore was analyzed by repeated measures ANOVA followed by Newman Keuls multiple comparison.
The data of the 2 control periods were compared by repeated-measures ANOVA or Friedman's ANOVA (when data deviated from normality), with the period as the repeated factor.
The mean RTs and the drift (self-location) measures (calculated relative to initial position = 0) were normally distributed Kolmogorov-Smirnovv test for normality) and were analyzed using two-tailed repeated measures analyses of variance (ANOVA) and two-tailed t-tests, respectively.
In order to assess the effect of possible non-normality on the analyses, they were repeated using non-parametric 2-factor orthogonal Kruskal-Wallis tests [ 11].
After normality was confirmed [ 15, 16] repeated measures analysis of variance with repeated contrasts were computed for acute and long-term data for both WBV and SWBV with the Statistical Package for Social Scientists SPSS Versionn 19.0, Chicago IL™).
A total of 1999 bootstrap samples were obtained by re-sampling men from the study sample with replacement, so accommodating the repeated measures design and any non-normality in the distributions of outcome measure.
