Effects of the EuroFit program

Mijanović, Mihajlo

doi:10.15406/mojsm.2022.05.00129

Cited by 4 publications

(2 citation statements)

References 4 publications

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“…The aim of the work refers to the theoretical description and presentation of various algorithms of cluster methods. 29 The primary problem of the paper is an empirically real, realistic presentation of the possibility of applicability and efficiency of Cluster methods in the function of objective selection.…”

Section: The Aim and Problem Of The Workmentioning

confidence: 99%

Cluster methods in function better selection

Mijanović

2024

MOJSM

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Cluster analysis methods, also known as taxonomic methods, are intended for grouping objects and subjects according to certain characteristics, attributes and properties. Cluster analysis looks at relevant objects and attributes, classifying them into two or more independent groups. Cluster analysis supplemented with discriminant analysis is used in confirmatory and fundamental research. In numerous statistical-methodological procedures, these methods are applied when setting up and testing various hypotheses. Grouping methods are particularly useful in the process of different selections with the aim of forming coherent groups, which may or may not necessarily be statistically different. There are several models of clustering (grouping), always with one goal, which is greater proximity (similarity) of an entity belonging to a group compared to an entity belonging to another group. Two basic grouping models are recognizable, Hierarchical and Non-Hierarchical. Both models have the same goal, which is the formation of several independent homogeneous groups from one common group of entities. The hierarchical approach does not define the number of clusters in advance (a priori), in contrast to the Non-Hierarchical Model which defines in advance number of clusters. The grouping model is chosen depending on the specific problem and the set goal of grouping. In the process, several different models are often applied, and then one is chosen as in this research. It is important to point out that the theoretical number of clusters (groups) is often not realistically applicable in practice. Using the example of this research, it was proven that the first grouping was not a good solution. Through the subsequent, second and third iteration, as well as the application of additional discriminative methods, three optimal clusters were determined in the population of girls and boys. Satisfactory optimal grouping was obtained on the basis of gender criteria and achieved results on psycho-motor tests.

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Section: The Aim and Problem Of The Workmentioning

confidence: 99%

Cluster methods in function better selection

Mijanović

2024

MOJSM

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“…The effectiveness of exercise is greater than a thousand medicines! Subject and goal of the research 10 The subject of the research is the transformation and discrimination between the sexes based on the results in psychomotor abilities during one school year. The applied tests are nothing new, they have long been in mass use in Europe and beyond, the tests are recognized under the pseudonym "EuroFit" battery of tests.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2023

MOJSM

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Differences between girls and boys based on basic psychomotor skills

Mijanović¹

2023

MOJSM

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According to the aim of the research and the set hypotheses, the scientific methodology was consistently implemented with the application of adequate classical and neoclassical statistical methods and tests. Following the set hypotheses, the following conclusions can be drawn. The first hypothesis H1 assumes that boys will have better results than girls at the beginning of the school year (first measurement) and that these differences will be statistically significant at the Sig.≤.05 error level. One-factor analysis of variance "Anova" shows that there is a statistically significant difference between boys and girls in favor of boys (Chi-square=29.36, df=8, Sig.<.00). Statistically significant correlation with the discriminative function had variables: (SHR, BAH, SBJ and SAR). Tables 1&2. Differences between girls and boys were not established for the variables: (FTR, PLT, SUP and HIG). That there is a difference at the general level is confirmed by the centroid distance. In accordance with according to the results in Tables 3–6 as well as Graphs 1&2, hypothesis H1 remains valid, the probability of statistical error is less than 1%. The second hypothesis H2 assumes that even at the end of the school year (second measurement) boys will also have better results than their female peers, as well as that the difference will be statistically significant. Comparing the arithmetic means (Tables 1&2), it is evident that in the second measurement the results were better, as if on average the girls were slightly higher. Table 7 shows the results of the one-factor analysis of variance "Anova" and the statistical significance of the differences for each test, including body height. Values in Table 7 column Sig. shows and shows that there is no statistically significant difference between girls and boys in any test. There is a difference between the arithmetic means, but these differences are not large enough to be statistically significant. Discriminative coefficients: Eigen value .071, Shi-sque 6.16, centroid distance (.242:-.286) and finally Sig.=.63 confirm that the differences are not statistically significant. Considering the results of the research, hypothesis H2 can be rejected, noting that girls have improved more.

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Effects of the EuroFit program

Cited by 4 publications

References 4 publications

Cluster methods in function better selection

Cluster methods in function better selection

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Differences between girls and boys based on basic psychomotor skills

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