Article Version of Record

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 trees

Persistent 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
  • Author(s) / Creator(s)
    Purpura, David J.
  • Author(s) / Creator(s)
    Day, Elizabeth
  • Author(s) / Creator(s)
    Napoli, Amy R.
  • Author(s) / Creator(s)
    Hart, Sara A.
  • PsychArchives acquisition timestamp
    2018-11-21T11:42:46Z
  • Made available on
    2018-11-21T11:42:46Z
  • Date of first publication
    2017-12-22
  • 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.
    en_US
  • Publication status
    publishedVersion
  • Review status
    peerReviewed
  • 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
    en_US
  • ISSN
    2363-8761
  • Persistent Identifier
    https://hdl.handle.net/20.500.12034/1262
  • Persistent Identifier
    https://doi.org/10.23668/psycharchives.1454
  • Language of content
    eng
  • Publisher
    PsychOpen GOLD
  • Is version of
    https://doi.org/10.5964/jnc.v3i2.53
  • Keyword(s)
    math
    en_US
  • Keyword(s)
    numeracy
    en_US
  • Keyword(s)
    preschool
    en_US
  • Keyword(s)
    risk status
    en_US
  • Keyword(s)
    classification and regression trees
    en_US
  • Dewey Decimal Classification number(s)
    150
  • Title
    Identifying domain-general and domain-specific predictors of low mathematics performance: A classification and regression tree analysis
    en_US
  • DRO type
    article
  • Issue
    2
  • Journal title
    Journal of Numerical Cognition
  • Page numbers
    365–399
  • Volume
    3
  • Visible tag(s)
    Version of Record