ArticleChildren (Basel, Switzerland)2022
Motor Competence Assessment (MCA) Scoring Method.
Article in Children (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Building From a Stable Foundation? Understanding Stability Skill Proficiency in British Children in the First Year of School.Child: care, health and development · 2026Article
- Adherence to different types of sports shapes motor competence development in preschool children.Scientific reports · 2025Article
- Study of body composition and motor competence in children from the 1st basic cycle and their relationship with school performance.BMC pediatrics · 2025Article
- Sports participation and sex outweigh the relative age effect in motor competence of school-aged children.Frontiers in sports and active living · 2025Article
- Can motor competence be influenced by the type of training interventions preschool children are exposed to? A randomized experimental study comparing sports games and psychomotricity activities.Frontiers in psychology · 2024Article
- Mastering movement: A Cross-sectional investigation of motor competence in children and adolescents engaged in sports.PloS one · 2024Article
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Authors and funding
5 authors.
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Abstract
The Motor Competence Assessment (MCA) is a quantitative test battery that assesses motor competence across the whole lifespan. It is composed of three sub-scales: locomotor, stability, and manipulative, each of them assessed by two different objectively measured tests. The MCA construct validity for children and adolescents, having normative values from 3 to 23 years of age, and the configural invariance between age groups, were recently established. The aim of this study is to expand the MCA's development and validation by defining the best and leanest method to score and classify MCA sub-scales and total score. One thousand participants from 3 to 22 years of age, randomly selected from the Portuguese database on MC, participated in the study. Three different procedures to calculate the sub-scales and total MCA values were tested according to alternative models. Results were compared to the reference method, and Intraclass Correlation Coefficient, Cronbach's Alpha, and Bland-Altman statistics were used to describe agreement between the three methods. The analysis showed no substantial differences between the three methods. Reliability values were perfect (0.999 to 1.000) for all models, implying that all the methods were able to classify everyone in the same way. We recommend implementing the most economic and efficient algorithm, i.e., the configural model algorithm, averaging the percentile scores of the two tests to assess each MCA sub-scale and total scores.
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