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Dataset for The Relationship between Number Line Estimation and Mathematical Reasoning: A Quantile Regression Approach
The sample included in this dataset represents children who participated in a cross-sectional study, a smaller cohort of which was followed up as part of a longitudinal study reported elsewhere (Bull et al., 2021). In the original study, 347 children were recruited.
As data was found to be likely missing completely at random (χ2 = 29.445, df = 24, p = .204, Little, 1998), listwise deletion was used, and 23 observations were deleted from the original dataset.
This dataset includes three hundred and twenty-four participants that composed the final sample of this study (162 boys, Mage = 6.2 years, SDage = 0.3 years). Children in this sample were in their second year of kindergarten (i.e., the year before starting primary school) in Singapore.
The dataset includes children's sociodemographic information (i.e., age and sex) and performance on different general cognitive and mathematical skills.
- Computer-based 0-10 and 0-100 number line task
- Mathematical Reasoning and Numerical Operations subtests from the Wechsler Individual Achievement Test II (WIAT II). Though the Numerical operations subtest is not used in this study.
General cognitive tasks:
- Peabody Picture Vocabulary Test (Vocabulary)
- Raven’s Progressive Matrices Test (Non-verbal reasoning)
The variables included in this dataset are:
Age = Child’s age (in months)
Sex = Boy/Girl (parent reported; boy=1, girl=2)
Ravens = Non-verbal reasoning (Raven’s Progressive Matrices test)
Ppvt = Vocabulary raw score (Peabody Picture Vocabulary Test)
Maths_reason = Mathematical reasoning raw score (Math Reasoning subtest from the Wechsler Individual Achievement Test II)
Num_Ops = Numerical Operations raw score (Numerical Operations subtest from the Wechsler Individual Achievement Test II, not used in this study)
NLE10 = 0-10 number line (Percent absolute error)
NLE100 = 0-100 number line (Percent absolute error)
This dataset overlaps with the dataset that is the basis for: Ruiz, C., Kohnen, S., & Bull, R. (2023) Number Line Estimation Patterns and Their Relationship with Mathematical
Performance. Journal of Numerical Cognition. Advance Online Publication https://doi.org/10.23668/psycharchives.12698
The manuscript associated with this dataset was supported by an International Macquarie University Research Excellence Scholarship "iMQRES" Allocation No. 2020005 to Carola Ruiz.
The original study was funded by the Singapore Ministry of Education (MOE) under the Education Research Funding Programme (OER 16/12RB) and administered by the National Institute of Education (NIE), Nanyang Technological University, Singapore.
- FAIR assessment completed
FAIR Self Assessment SummaryThis text has been generated from a tool that has been adapted from the ARDC FAIR Assessment Tool Findable -------- Does the dataset have any identifiers assigned? Global Is the dataset identifier included in all metadata records/files describing the data? Yes How is the data described with metadata? Comprehensively (see suggestion) using a recognised formal machine-readable metadata schema What type of repository or registry is the metadata record in? Data is in one place but discoverable through several registries Accessible ---------- How accessible is the data? Unspecified conditional access e.g. contact the data custodian for access Is the data available online without requiring specialised protocols or tools once access has been approved? By individual arrangement Will the metadata record be available even if the data is no longer available? Yes Interoperable ------------- What (file) format(s) is the data available in? In a structured, open standard, machine-readable format What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements? No standards have been applied in the description of data elements How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)? The metadata record includes URI links to related metadata, data and definitions Reusable -------- Which of the following best describes the license/usage rights attached to the data? Standard machine-readable license (e.g. Creative Commons) How much provenance information has been captured to facilitate data reuse? Partially recorded
FAIR Self Assessment Rating
- 3 Stars