## 4 Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reliability** & **Kendall’s Coefficient of Concordance** \\ \hline Cronbach’s Alpha & W & Sig. \\ \hline
0.981 & 1 & **0.000** \\ \hline \hline \end{tabular}  
* Sig—level of statistical significance; W—Kendall’s coefficient of concordance.  
\end{table} 
Table 8: Results of the reliability with Kendall’s coefficient of concordance. series of test performances, and only then carry out further measurements. Similar results were determined for the FR test, where a difference was determined between the first and third series of the test performance (Wilks's Lambda = 0.278, F = 5.206, \(p\) = 0.023, Partial Eta Squared = 0.722), where the process of motor learning also had an impact during the first two series of repetitions, which is why better performances of the participants were noted after the third series. Significant differences in the results of the SC test were determined between the first and fourth series of performances (Wilks's Lambda = 0.188, F = 8.664, \(p\) = 0.005, Partial Eta Squared = 0.812). For objective insight into the current performance of the semicircular agility of the participants, when preparing for measurement, it was necessary to perform four series of tests. Based on the results, it was confirmed that for a higher-quality performance of complex motor tasks, both the creation of a mental image of movement and practical training are needed [29]. Predetermined motor programs for a certain task during the initial series are further developed by additional performance of the task, up to a certain stage when the stabilization of the results is achieved [30]. Attempts to determine the constructive validity of tests for the evaluation of agility were made in different sports [31], along with the standardization of tests of agility and other motor skills of male and female basketball players [32; 33; 34; 35; 36; 37]. In regard to determining the constructive validity of the applied tests of LA, UN, FR, SC, following the division of the participants into two groups (HPA and LPA), and based on data compiled from the standardized tests (the Agility \(t\)-test and the Sprint 9-3-6-3-9 m with 180\({}^{\circ}\) Turns test), hierarchical clustering analyses were used, which have already been applied in the previous literature [38]. First, a statistically significant difference was noted in the performance of agility in favor of one group of female basketball players (HPA) for all applied tests (LA, \(p\) = 0.001; UN, \(p\) = 0.004; FR, \(p\) = 0.005; SC, \(p\) = 0.002), after which a strong positive correlation was determined (\(p\) = 0.01) between the results achieved by the participants on all tests. High values of the correlation coefficient were determined both between the results achieved on the tests whose validity was being assessed and the Agility \(t\)-test (LA, r = 0.969; UN, r = 0.936; FR, r = 0.948; SC, r = 0.973), and for the results of the Sprint 9-3-6-3-9 m With 180\({}^{\circ}\) Turns test (LA, r = 0.987; UN, r = 0.962; FR, r = 0.956; SC, r = 0.982). These findings clearly indicate that the constructive validity of the aforementioned tests of non-reactive agility was confirmed, and that the spatial design of the movement in these tests is in accordance with the specificities and nature of movement in a sport such as basketball. This is confirmed by the results of the applied canonical discriminant analysis, which determined a strong correlation coefficient (canonical correlation = 82%), which due to the high quality of the sample, i.e., the small number of female participants, is still not significant, but is very close to the level of statistical significance (Sig. = 0.062). It confirms a high match between the new and the existing tests. A considerable overlap between the new tests and the existing ones is indicated by an exceptionally strong correlation (LA = 0.961; SC = 0.895; UN = 0.825; FR = 0.777), which is statistically significant (Sig. = 0.000), along with the maximum value of [PERSON]'s coefficient of concordance (W = 1).