Using a Generalized Linear Mixed Model Approach to Explore the Role of Age, Motor Proficiency, and Cognitive Styles in Children's Reach Estimation Accuracy
Abstract
The purpose was to use a multi-level statistical technique to analyze how children's age, motor proficiency, and cognitive styles interact to affect
accuracy on reach estimation tasks via Motor Imagery and Visual Imagery. Results
from the Generalized Linear Mixed Model analysis (GLMM) indicated that only
the 7-year-old age group had significant random intercepts for both tasks. Motor
proficiency predicted accuracy in reach tasks, and cognitive styles (object scale) predicted accuracy in the motor imagery task. GLMM analysis is suitable to explore
age and other parameters of development. In this case, it allowed an assessment of
motor proficiency interacting with age to shape how children represent, plan, and
act on the environment.