Displaying results 1 - 8 of 8 (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: The aim of this research is to create developmentally appropriate, play-based storytelling elicitation procedures to collect language samples of young children aged 18-48 months, tools for evaluating the magnitude and quality of narrative language produced in play-based storytelling sessions, and examine the psychometric properties of these new
Project
Description: In research, augmentative and alternative communication (AAC) interventions have primarily focused on teaching children to make requests (Logan et al., 2017); however, AAC intervention should not stop there.
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: Improving the Academic Performance of First-Grade Students with Reading and Math Difficulty
Description: This data set includes teacher identification (nesting) variable, reading and math scores, cognitive scores, and demographics .
Dataset
Part of Project: AAC Narrative Intervention for Children with Autism
Description: These are the data for story grammar scores and number of different symbols used that resulted from the AAC narrative intervention. They are organized according to multiple baseline across participants design and pseudonyms are used instead of children's names.
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: Improving the Academic Performance of First-Grade Students with Reading and Math Difficulty
Description: This is the codebook accompanying the dataset Comorbid Word Reading and Math Computation Difficulty at Start of First Grade.
Document Type: Codebook
