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

Katerina Asonitou, Gerasimos Prodromitis and Dimitra Koutsouki

Background: There is increasing evidence that children with DCD have been classified into distinguishable ‘subtypes’ mainly based on perceptual-motor, fine and gross motor skills. Previous research efforts define and describe in detail subgroups of DCD using the methods of cluster analysis. The hierarchical agglomerative cluster analysis seems to be an effective statistical method to identify homogeneous subtypes in the developmental disorder literature.

Methods: The present study investigated the nature of possible cognitive-motor profiles of DCD using clustering methods. Dependent variables were selected on the basis of the characteristics of children with DCD and the specific difficulties observed in cognitive- motor domain according to the DCD literature. For the purpose of the study we adopted “PASS” neurocognitive theory (Planning, Attention, Simultaneous, Successive) and the norm-referenced Cognitive Assessment System.

Results: Based on this hierarchical agglomerative cluster analysis six (6) statistical sub-groups emerged with number of participants ranged from 5-43 students with or without DCD. Internal and external validity of the clustering solution was controlled by different clustering methods (Wards method analysis, Complete Linkage method, Centroid method, K-Means iterative partitioning method and split-sample replication), as well as other parametric methods (MANOVA, ANOVA and discriminant analyses).

Conclusions: Future research examining the impact of DCD classification is warranted and it could be apply for other developmental disorders. The impact of different DCD profiles may provide larger benefits for alternative and effective instructional methods and early intervention programs in order to avoid motor learning disabilities and low academic achievement.