2.7. Statistical Analyses
The descriptive data on the variables were presented as mean ± SD. The dependent variables included Height, Take-off, Average Power, and RFD, as measured in the CMJ and the SLCMJ with right and left leg tests. The other dependent variables were the scores awarded by the expert judges for the performance of scissors, stag jump, and split leap under different conditions.Linear mixed models for repeated measure designs were employed to analyze the changes and differences between groups when all assumptions were met. A normality analysis of the residuals was carried out using the Kolmogorov–Smirnov test for each variable, and no deviations from a normal distribution were found. Homoscedasticity was examined by plotting the residuals-predicted value. This analysis indicated that the residuals were constant for all the predicted values. An alpha level of GAMLj was used to carry out the linear mixed model analyses. This module relies on the R formulation of random effects. Specifically, it was done using the lme4 R package in Jamovi software (Concomitantly, when no significant interaction effect was found, Cohen’s d effect size was calculated on all scores awarded by the expert judges (scissors, stag jump, and split leap), in the pre- and post-tests, to measure small (0.2), moderate (0.5), and large (0.8) effect sizes [