Identifying domain-general and domain-specific predictors of low mathematics performance: A classification and regression tree analysis
Author(s) / Creator(s)
Purpura, David J.
Day, Elizabeth
Napoli, Amy R.
Hart, Sara A.
Abstract / Description
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.
Keyword(s)
math numeracy preschool risk status classification and regression treesPersistent Identifier
Date of first publication
2017-12-22
Journal title
Journal of Numerical Cognition
Volume
3
Issue
2
Page numbers
365–399
Publisher
PsychOpen GOLD
Publication status
publishedVersion
Review status
peerReviewed
Is version of
Citation
Purpura, D. J., Day, E., Napoli, A. R., & Hart, S. A. (2017). Identifying domain-general and domain-specific predictors of low mathematics performance: A classification and regression tree analysis. Journal of Numerical Cognition, 3(2), 365–399. https://doi.org/10.5964/jnc.v3i2.53
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Author(s) / Creator(s)Purpura, David J.
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Author(s) / Creator(s)Day, Elizabeth
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Author(s) / Creator(s)Napoli, Amy R.
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Author(s) / Creator(s)Hart, Sara A.
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PsychArchives acquisition timestamp2018-11-21T11:42:46Z
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Made available on2018-11-21T11:42:46Z
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Date of first publication2017-12-22
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Abstract / DescriptionMany 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.en_US
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Publication statuspublishedVersion
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Review statuspeerReviewed
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CitationPurpura, D. J., Day, E., Napoli, A. R., & Hart, S. A. (2017). Identifying domain-general and domain-specific predictors of low mathematics performance: A classification and regression tree analysis. Journal of Numerical Cognition, 3(2), 365–399. https://doi.org/10.5964/jnc.v3i2.53en_US
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ISSN2363-8761
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Persistent Identifierhttps://hdl.handle.net/20.500.12034/1262
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Persistent Identifierhttps://doi.org/10.23668/psycharchives.1454
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Language of contenteng
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PublisherPsychOpen GOLD
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Is version ofhttps://doi.org/10.5964/jnc.v3i2.53
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Keyword(s)mathen_US
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Keyword(s)numeracyen_US
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Keyword(s)preschoolen_US
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Keyword(s)risk statusen_US
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Keyword(s)classification and regression treesen_US
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Dewey Decimal Classification number(s)150
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TitleIdentifying domain-general and domain-specific predictors of low mathematics performance: A classification and regression tree analysisen_US
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DRO typearticle
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Issue2
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Journal titleJournal of Numerical Cognition
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Page numbers365–399
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Volume3
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Visible tag(s)Version of Record