Identifying Domain-General and Domain-Specific Predictors of Low Mathematics Performance: A Classification and Regression Tree Analysis
Authors
David J. Purpura
Department of Human Development and Family Studies, Purdue University, West Lafayette, IN, USA
Elizabeth Day
Department of Human Development and Family Studies, Purdue University, West Lafayette, IN, USA
Amy R. Napoli
Department of Human Development and Family Studies, Purdue University, West Lafayette, IN, USA
Sara A. Hart
Department of Psychology, Florida State University, Tallahassee, FL, USA; Florida Center for Reading Research, Tallahassee, FL, USA
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.
