Carlos Luz1, Luis P Rodrigues2, Gabriela Almeida3, Rita Cordovil4. 1. Laboratory of Motor Behavior, Faculdade de Motricidade Humana, Universidade de Lisboa, Portugal; Escola Superior de Educação de Lisboa, Instituto Politécnico de Lisboa, Portugal. Electronic address: carlosmiguelluz@gmail.com. 2. Escola Superior de Desporto e Lazer, Instituto Politécnico de Viana do Castelo, Portugal; CIDESD, Portugal. 3. Faculdade de Ciências da Saúde, Universidade Fernando Pessoa, Portugal. 4. Laboratory of Motor Behavior, CIPER, Faculdade de Motricidade Humana, Universidade de Lisboa, Portugal.
Abstract
OBJECTIVES: This study was aimed at developing a quantitative model to evaluate motor competence (MC) in children and adolescents, to be applicable in research, education, and clinical contexts. DESIGN: Cross-sectional. METHODS: A total of 584 children (boys n=300) with ages between 6 and 14 years were assessed using nine well known quantitative motor tasks, divided into three major components (stability, locomotor and manipulative). Structural equation modelling through EQS 6.1 was used to find the best model for representing the structural and measurement validity of MC. RESULTS: The final MC model was composed by three latent factors closely related with each other. Each factor was best represented by two of the initial three motor tasks chosen. The model was shown to give a very good overall fit (χ(2)=12.04, p=.061; NFI=.982; CFI=.991; RMSEA=.059). CONCLUSIONS: MC can be parsimoniously represented by six quantitative motor tasks, grouped into three interrelated factors. The developed model was shown to be robust when applied to different samples, demonstrating a good structural and measurement reliability. The use of a quantitative protocol with few, simple to administer and well known, motor tasks, is an important advantage of this model, since it can be used in several contexts with different objectives. We find it especially beneficial for physical educations teachers who have to regularly assess their students.
OBJECTIVES: This study was aimed at developing a quantitative model to evaluate motor competence (MC) in children and adolescents, to be applicable in research, education, and clinical contexts. DESIGN: Cross-sectional. METHODS: A total of 584 children (boys n=300) with ages between 6 and 14 years were assessed using nine well known quantitative motor tasks, divided into three major components (stability, locomotor and manipulative). Structural equation modelling through EQS 6.1 was used to find the best model for representing the structural and measurement validity of MC. RESULTS: The final MC model was composed by three latent factors closely related with each other. Each factor was best represented by two of the initial three motor tasks chosen. The model was shown to give a very good overall fit (χ(2)=12.04, p=.061; NFI=.982; CFI=.991; RMSEA=.059). CONCLUSIONS:MC can be parsimoniously represented by six quantitative motor tasks, grouped into three interrelated factors. The developed model was shown to be robust when applied to different samples, demonstrating a good structural and measurement reliability. The use of a quantitative protocol with few, simple to administer and well known, motor tasks, is an important advantage of this model, since it can be used in several contexts with different objectives. We find it especially beneficial for physical educations teachers who have to regularly assess their students.
Authors: Ana Filipa Silva; Hadi Nobari; Georgian Badicu; Halil Ibrahim Ceylan; Ricardo Lima; Maria João Lagoa; Carlos Luz; Filipe Manuel Clemente Journal: BMC Pediatr Date: 2022-07-20 Impact factor: 2.567
Authors: Benjamin David Weedon; Francesca Liu; Wala Mahmoud; Renske Metz; Kyle Beunder; Anne Delextrat; Martyn G Morris; Patrick Esser; Johnny Collett; Andy Meaney; Ken Howells; Helen Dawes Journal: BMJ Open Sport Exerc Med Date: 2018-03-08
Authors: Carlos Luz; Rita Cordovil; Luís Paulo Rodrigues; Zan Gao; Jacqueline D Goodway; Ryan S Sacko; Danielle R Nesbitt; Rick C Ferkel; Larissa K True; David F Stodden Journal: J Sport Health Sci Date: 2019-01-14 Impact factor: 7.179
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