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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).
Project
Description: The Western Reserve Reading and Math Project (WRRMP) is a NIH funded longitudinal study on child development. The project has collected data annually for 15 years, with data on approximately 450 twin pairs collected during this time. The project has had several focal points throughout its history.
Project
Description: In an attempt to bridge the opportunity gap that exists prior to schooling, understanding the specific activities, resources, and interactions that take place in the home that encourage higher academic achievement has become a primary concern in the field.
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.
Dataset
Part of Project: Western Reserve Reading and Math Project
Description: This dataset contains all longitudinal data for the entirety of the WRRMP. This includes 10 waves worth of twin data, with extensive reading, math, behavioral, and environmental measures. Due to the twin nature of the data, data is presented as both long and wide, with each twin represented twice within the dataset.
Dataset
Part of Project: Early Home Learning Environment
Description: These data are parent reports of survey questions about their beliefs and their children's home learning environments. Data are cross-sectional and include responses from parents with children ranging from 0 - 13 years of age.
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
Document
Part of Project: Early Home Learning Environment
Description: This is the codebook accompanying the full EHLE data set. It includes descriptions of variables and data collection processes.
Document Type: Codebook
