Displaying results 1 - 7 of 7 (Go to Advanced Search)
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: This meta-analysis aims at examining whether statistically significant differences in EF exist between bilingual and monolingual individuals, as well as estimating the potential moderators of such differences.
Project
Description: There are consistent correlations between mathematics achievement, attitudes, and anxiety, but the longitudinal relations among these constructs are not well understood nor are sex differences in these relations.
Project
Description: Sex differences in the strength of the relations between mathematics anxiety, mathematics attitudes, and mathematics achievement were assessed concurrently in sixth grade (n = 1,091, 545 boys) and longitudinally from sixth to seventh grade (n = 190, 97 boys).
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: Sex Differences in Mathematics Anxiety and Attitudes
Description: Sex differences in the strength of the relations between mathematics anxiety, mathematics attitudes, and mathematics achievement were assessed concurrently in sixth grade (n = 1,091, 545 boys) and longitudinally from sixth to seventh grade (n = 190, 97 boys).
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
