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 (<em>χ</em>2 = 29.445, <em>df </em>= 24, <em>p</em> = .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. Mathematical tasks: - 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: <strong>Age</strong> = Child’s age (in months) <strong>Sex</strong> = Boy/Girl (parent reported; boy=1, girl=2) <strong>Ravens</strong> = Non-verbal reasoning (Raven’s Progressive Matrices test) <strong>Ppvt</strong> = Vocabulary raw score (Peabody Picture Vocabulary Test) <strong>Maths_reason</strong> = Mathematical reasoning raw score (Math Reasoning subtest from the Wechsler Individual Achievement Test II) <strong>Num_Ops</strong> = Numerical Operations raw score (Numerical Operations subtest from the Wechsler Individual Achievement Test II, not used in this study) <strong>NLE10</strong> = 0-10 number line (Percent absolute error) <strong>NLE100</strong> = 0-100 number line (Percent absolute error) This dataset overlaps with the dataset that is the basis for: Ruiz, C., Kohnen, S., &amp; Bull, R. (2023) Number Line Estimation Patterns and Their Relationship with Mathematical Performance. <em>Journal of Numerical Cognition</em>. Advance Online Publication https://doi.org/10.23668/psycharchives.12698 That project’s corresponding OSF page can be found here: https://osf.io/jat5h/ and the dataset is stored under embargo here: https://doi.org/10.25949/22558528.v1