Subtypes of Students with Learning Disabilities Based upon Gross Motor Functions

Abstract

The purpose of this study was to identify possible subtypes of students with learning disabilities based upon gross motor functions. Subjects in a private school for learning difficulties were divided into a group of students with learning disabilities and a comparison group. Gross motor subtests from the Bruininks-Oseretsky Test of Motor Proficiency (Bruininks, 1978) were administered to both learning-disabled and comparison groups. The four subtypes yielded by the K-means iterative partitioning method demonstrated distinct profiles. Cluster membership was shown to be fairly stable by internal validation techniques. The external validity of the four subtypes was verified by a teacher’s ratings of students’ physical behaviors. It was recommended that the outcome of type-specific remediation and the longitudinal stability of gross motor subtypes be evaluated.

Hierarchical Agglomerative Cluster Analysis as a Trial Method Revealing Developmental Coordination Disorder (DCD) Subtypes

“…A similar subtype with good (static) balance was found in different studies [1,3,4,8]. The balance was one of the few variables measured consistently across all relevant studies.…”

“…Based on such methodological clustering approach the occupational could determine the specific domain in which students had difficulties on how to learn new motor and cognitive academic skills or how to execute and perform properly the tasks that require or do not require a motor response. The subgrouping highlights the need for "type-specific remediation" [8].…”

“…Previous studies have shown that running is a crucial gross motor test for children with DCD [3,4,8]. Hoare [3] found that clumsy children had the greatest difficulties in tasks required gross movement of the body.…”

“…Factor analyses were used before clustering methods, in order to reject redundant variables could obscure the cluster analysis and minimize the number of variables. It is very important to include the smallest possible number of variables [29,30,8].…”

“…Macnab et al [4] examined in detail three studies [1,3,8], which used the hierarchical agglomerative cluster analysis. The aim of the study was to examine the use of cluster analysis as a method of exploration subgroups of DCD to better understand how different samples and different measures affect the interpretation of results.…”