Article ; Online: Anthropometric and Motor Competence Classifiers of Swimming Ability in Preschool Children—A Pilot Study
International Journal of Environmental Research and Public Health, Vol 17, Iss 6331, p
2020 Volume 6331
Abstract: Swimming is a form of physical activity and a life-saving skill. However, only a few studies have identified swimming ability classifiers in preschool children. This pilot cross-sectional study aimed to find anthropometric (AM) and motor competence (MC) ... ...
| Abstract | Swimming is a form of physical activity and a life-saving skill. However, only a few studies have identified swimming ability classifiers in preschool children. This pilot cross-sectional study aimed to find anthropometric (AM) and motor competence (MC) predictors of swimming ability in preschool children, by building classifiers of swimming ability group (SAG) membership. We recruited 92 children (girls n = 45) aged 5–6 years and took the AM and MC measurements in accordance with the reference manual and using the KTK battery test (motor quotient, MQ), respectively. A linear discriminant analysis tested a classification model of preschoolers’ swimming ability (SAG: POOR, GOOD, EXCELLENT) based on gender, age, AM, and MC variables and extracted one significant canonical discriminant function (model fit: 61.2%) that can differentiate (group centroids) POOR (−1.507), GOOD (0.032), and EXCELLENT (1.524). The MQ total was identified as a significant classifier, which absolutely contributed to the discriminant function that classifies children’s swimming ability as POOR (standardized canonical coefficient: 1.186), GOOD (1.363), or EXCELLENT (1.535) with an accuracy of 64.1%. Children with higher MQ total ought to be classified into higher SAG; thus, the classification model of SAG based on the MQ total is presented. |
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| Keywords | kindergarten children motor ability ; swimming ability ; motor competence ; preschool children ; classification model ; Medicine ; R |
| Subject code | 796 |
| Language | English |
| Publishing date | 2020-08-01T00:00:00Z |
| Publisher | MDPI AG |
| Document type | Article ; Online |
| Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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