Please use this identifier to cite or link to this item: http://dx.doi.org/10.23668/psycharchives.1454
Title: Identifying domain-general and domain-specific predictors of low mathematics performance: A classification and regression tree analysis
Authors: Purpura, David J.
Day, Elizabeth
Napoli, Amy R.
Hart, Sara A.
Issue Date: 22-Dec-2017
Publisher: PsychOpen
Abstract: Many children struggle to successfully acquire early mathematics skills. Theoretical and empirical evidence has pointed to deficits in domain-specific skills (e.g., non-symbolic mathematics skills) or domain-general skills (e.g., executive functioning and language) as underlying low mathematical performance. In the current study, we assessed a sample of 113 three- to five-year old preschool children on a battery of domain-specific and domain-general factors in the fall and spring of their preschool year to identify Time 1 (fall) factors associated with low performance in mathematics knowledge at Time 2 (spring). We used the exploratory approach of classification and regression tree analyses, a strategy that uses step-wise partitioning to create subgroups from a larger sample using multiple predictors, to identify the factors that were the strongest classifiers of low performance for younger and older preschool children. Results indicated that the most consistent classifier of low mathematics performance at Time 2 was children’s Time 1 mathematical language skills. Further, other distinct classifiers of low performance emerged for younger and older children. These findings suggest that risk classification for low mathematics performance may differ depending on children’s age.
URI: https://hdl.handle.net/20.500.12034/1262
http://dx.doi.org/10.23668/psycharchives.1454
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