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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: We sought to understand the speech and language skills of children with nonsyndromic cleft palate with or without cleft lip (NSCP/L) by meta-analyzing results of the literature.
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: Speech and language skills in children with nonsyndromic cleft palate with or without cleft lip
Description: These data are from our third meta-analysis project. The speech-vocabulary analysis represented eight samples and the speech-mlu analysis represented four samples. The ages ranged from 18-months to 39-months.
Dataset
Part of Project: Speech and language skills in children with nonsyndromic cleft palate with or without cleft lip
Description: These data represent effect sizes comparing children with NSCP/L to non-cleft peers. There are 241 effect sizes from 31 studies. Children's ages ranged from 13-months to 104-months (8;7). The data are in long format as there were multiple effect sizes extracted per study.
Dataset
Part of Project: Speech and language skills in children with nonsyndromic cleft palate with or without cleft lip
Description: These data are descriptive codes for the studies included in the meta-analysis. There are 31 studies. Data are presented in wide format. There are 34 variables.
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
