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Project
Description: Project KIDS aimed to rigorously combine data from several independent RCTs to explore individual differences in response to intervention, focused on cognitive, behavioral, contextual, and family history correlates of intervention response.
Project
Description: Mathematical thinking is in high demand in the global market, but approximately six percent of school-age children across the globe experience math difficulties (Shalev, et al., 2000).
Dataset
Part of Project: Project KIDS
Description: These data are for each item on all achievement and behavioral assessments completed during the original intervention projects. The dataset is wide, with separate variable names for each wave of assessment. All participants (n= 4,038) are included. These data can be linked to the "total level" data using the PK_ID variable.
Dataset
Part of Project: Project KIDS
Description: This data set includes all total scores, demographics, home literacy environment, etc. for Project KIDS. Data are in wide format, with separate variables for each wave of assessments. All 4038 participants are represented in the data.
Dataset
Part of Project: Project KIDS
Description: These data include information on family demographics, home environment, health information, child diet and nutrition, BRIEF, SWAN, all at the item level. This is cross-sectional data. Data can be linked to other Project KIDS data through the PK_ID variable.
Dataset
Part of Project: The Home Math Environment and Children's Math Achievment: A Meta-Analysis
Description: The data are in long form, with some studies having multiple lines and includes a sample of children ranging from 3.54 to 13.75 years old. The main effect size is the r, correlation coefficient, and the accompanying sample size is also included.
Code
Home Math Environment and Children's Math Achievement Meta-Analysis R code using the Metafor package
Part of Project: The Home Math Environment and Children's Math Achievment: A Meta-Analysis
Description: This code was written in R version 3.5.3 using the metafor package (Viechtbauer, 2010). First the dataset is called in, then the variables are converted to the correct formats for analysis, then the escalc() function is used to calculate an overall Fisher's Z effect size, which is then converted to an R correlation coefficient.
Code Type: Analysis
