Dataset for Number Line Estimation Patterns and their Relationship with Mathematical Performance

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 mathematical skills. Children were assessed on a computer-based 0-100 number line task and on the Mathematical Reasoning and Numerical Operations subtests from the Wechsler Individual Achievement Test II (WIAT II). The initial variables recorded on the dataset were children's estimates on each of the target numbers included on the 0-100 number line task, and their accuracy for both subtests of the WIAT II. Several more variables were created based on these original ones.
The variables included in the dataset are:
Age = Child’s age (in months)
Sex = Boy/Girl (parent reported; boy=1, girl=2)
Maths_reason = Mathematical reasoning (Math Reasoning subtest from the Wechsler Individual Achievement Test II)
Num_Ops = Numerical Operations (Numerical Operations subtest from the Weschler Individual Achievement Test II)
Mathematical_achievement = Mathematical achievement (Composite score created by adding the raw scores from the Numerical Operations and Mathematical Reasoning subtests from the Weschler Individual Achievement Test II)
P3 to P96 = Placement of the estimate on the 0-100 number line for each respective target number (i.e., P3 corresponds to the placement of the estimate provided when the target number was 3)
NLE100PAE = 0-100 number line (Percent absolute error)
NP100_Corr = Correlation of individual estimates to target numbers (Spearman’s correlation; p > .05= 0, p < .05 = 1)
NP100LinAICc = AICc value obtained for the linear model (9999 = model cannot be fitted)
NP100LogAICc = AICc value obtained for the logarithmic model (9999 = model cannot be fitted)
NP100PowerAICc = AICc value obtained for the unbounded power model (9999 = model cannot be fitted)
NP1001cycleAICc = AICc value obtained for the one-cycle power model (9999 = model cannot be fitted)
NP1002cycleAICc = AICc value obtained for the two-cycle power model (9999 = model cannot be fitted)
Best_fit_NP100_repshift = Best fitting model based on the representational shift account (0 = model cannot be fitted, 1 = linear, 2 = logarithmic)
AICc_bestmodel_repshift = AICc value of the best fitting model based on the representational shift account 
AICc_diff_repshift = AICc difference (ΔAICc) between both models (i.e, linear and logarithmic) based on the representational shift account
AICc_diff_cat_repshift = categorical value created based on AICc_diff_repshift (0 = model cannot be fitted, 1= best fitting model does not have strong support (ΔAIcc < 2), 2 = best fitting model has strong support (ΔAIcc > 2))
Best_fit_NP100_propjudg = Best fitting model based on the proportional judgment account (0 = model cannot be fitted, 3 = unbounded power model, 4 = one-cycle power model, 5 = two-cycle power model)
AICc_bestmodel_propjudg = AICc value of the best fitting model based on the proportional judgment account 
AICc_diff_propjudg_unb = AICc difference (ΔAIcc) between the best fitting model based on the proportional judgment account and the unbounded power model
AICc_diff_propjudg_1cyc = AICc difference (ΔAIcc) between the best fitting model based on the proportional judgment account and the one-cycle power model
AICc_diff_propjudg_2cyc = AICc difference (ΔAIcc) between the best fitting model based on the proportional judgment account and the two-cycle power model
AICc_diff_cat_propjudg = categorical value created based on AICc differences between the best fitting model and the following one based on the proportional judgment account (0 = model cannot be fitted, 1= best fitting model does not have strong support (ΔAIcc < 2), 2 = best fitting model has strong support (ΔAIcc > 2))
Best_fit_NP100_between = Best fitting model when comparing all models to each other (0= model cannot be fitted, 1 = linear, 2 = logarithmic, 3 = unbounded power model, 4 = one-cycle power model, 5 = two-cycle power model)
AICc_bestmodel_between = AICc value of the best fitting model from comparing all models to each other 
AICc_diff_linear_NP100 =AICc difference (ΔAIcc) between the best fitting model based on comparing all models to each other and the linear model
AICc_diff_log_NP100 =AICc difference (ΔAIcc) between the best fitting model based on comparing all model to each other and the logarithmic model
AICc_diff_power_NP100 =AICc difference (ΔAIcc) between the best fitting model based on comparing all models to each other and the unbounded power model
AICc_diff_1cycle_NP100 =AICc difference (ΔAIcc) between the best fitting model based on comparing all models to each other and the one-cycle power model
AICc_diff_2cycle_NP100 =AICc difference (ΔAIcc) between the best fitting model based on comparing all models to each other and the two-cycle power model
AICc_diff_cat_between = categorical value created based on AICc differences between the best fitting model and the following one based on the comparison of all models to each other (0 = model cannot be fitted, 1= best fitting model does not have strong support (ΔAIcc < 2), 2 = best fitting model has strong support (ΔAIcc > 2))