120 Chapter 6 Longitudinal multilevel models were created to describe development of rST, rStart, rTurn, rSprint, rSI and rLBP (dependent variables) as a function of (chronological) age, using the lmer4 package in R (R version 3.6.0). The age effect (which was used as measure for development over time) was not imposed to be identical between high- and lowerperforming seniors. Therefore, a nested interaction between age and performance level group at early senior age was included. To represent these two performance level groups in the statistical models, one dummy variable (high-level performance group) was included and the lower-level performance group functioned as reference level. A random intercept model was selected as the most appropriate variance structure, allowing the inclusion of each swimmer’s individual trajectory that randomly deviates from the average population trajectory. In sum, the following multilevel model was adopted: Yis was the dependent variable (e.g., rSprint) for swimming season s of swimmer i, αi the intercept of swimmer i, Ageis the corresponding age value and High-level performance groupi the dummy variable indicating whether or not swimmer was in the high-level performance group. The unexplained information was the sum of ui (between-subject variance) and εis (residual variance). The models were validated by using visible patterns in residual plots to check violations of homogeneity, normality and independence. Predictor variables were considered significant if the p value of the estimated mean coefficient is smaller than 0.05.
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