Displaying results 1 - 10 of 10 (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: Language sampling is a critical component of language assessments. However, there are many ways to elicit language samples that likely impact the results. The purpose of this study was to examine how different discourse types and elicitation tasks affect various language sampling outcomes.
Project
Description: This was a longitudinal study that began in 2013 with 1st and 2nd grade children. These children attended school in the SE. Any child whose parents consented were included. Children were tested each fall for the following 3 years except for the first cohort who were not tested after 4th grade.
Project
Description: This LDbase project page containes the open science materials for our meta-analysis on the reading anxiety and reading achievement.
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: Impact of Discourse Type and Elicitation Task on Language Sampling Outcomes
Description: These are the data for 1037 K-3 students who contributed oral academic language samples. https://doi.org/10.1044/2023_AJSLP-22-00365
Dataset
Part of Project: Florida Longitudinal Study
Description: This is the data set for the Florida Longitudinal Study
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: Reading Anxiety and Reading Achievement: A Meta-Analysis
Description: In this preregisteration we outline our plan for conducting this meta-analysis on the association between reading anxiety and reading achievement. We provide a description of the study, including the study aims and research questions.
Document Type: Preregistration
Document
Part of Project: Reading Anxiety and Reading Achievement: A Meta-Analysis
Description: We are conducting a meta-analysis on the association between reading anxiety and reading achievement. This document, specifically, is our preregistered coding sheets we plan to use for extraction of information from each study included in the meta-analysis.
Document Type: Preregistration
