Displaying results 1 - 9 of 9 (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 project relates to the published manuscript available at https://doi.org/10.1177%2F1747021820923944
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: 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: 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: 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: Listening to Speech and Non-speech Sounds Activates Phonological and Semantic Knowledge Differently
Description: This document contains all of the stimuli developed and used in the overarching project.
Document Type: Other
