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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: Project Goals:
The first goal will fill a CUREs gap by creating self-supporting and sustainable protein-centric
CUREs. The second goal will use this protein-centric CUREs community to examine two critical
aspects of a CURE: 1) the impact of the length of CUREs (course long CUREs (cCUREs) or shorter, modular
Project
Description: The Meta-analysis of Educational RCTs with Follow-up (MERF) dataset was created to investigate critical questions about educational intervention fadeout and persistence. The sample is comprised of interventions targeting a diverse array of child outcomes across development (e.g.
Project
Description: The following contains the analytic code used to generate the findings reported in "Delay of Gratification and Adult Outcomes: The Marshmallow Test Does Not Reliably Predict Adult Functioning" in Child Development. We also report the means, standard deviations, and correlations among the key variables used in this analysis here.
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: Meta-analysis of Educational RCTs with Follow-up
Description: See README file(s) and MERF protocol for more details about this supplemental dataset.
Dataset
Part of Project: Meta-analysis of Educational RCTs with Follow-up
Description: See README file(s) and MERF protocol for more details about this supplemental dataset.
Dataset
Part of Project: Meta-analysis of Educational RCTs with Follow-up
Description: See README file(s) and MERF protocol for more details about this supplemental dataset.
Dataset
Part of Project: Meta-analysis of Educational RCTs with Follow-up
Description: See README file(s) and MERF protocol for more details about this dataset.
Dataset
Part of Project: Meta-analysis of Educational RCTs with Follow-up
Description: See README file(s) and MERF protocol for more details about this dataset.
Dataset
Part of Project: Malate Dehydrogenase CURE Community
Description: This is the pre and post test TOSLS data for the undergraduate students in this project.
Dataset
Part of Project: Malate Dehydrogenase CURE Community
Description: This contains institutional data gathered from some of the participating institutions including GPA, retention, and graduation data.
Dataset
Part of Project: Malate Dehydrogenase CURE Community
Description: This includes the variables from the STEM Career Interest Scale.
Dataset
Part of Project: Malate Dehydrogenase CURE Community
Description: This is the student data from the LCAS taken at the end of the semester.
Dataset
Part of Project: Malate Dehydrogenase CURE Community
Description: The faculty reported the CURE elements that were incorporated into their course after the semester was finished.
Dataset
Part of Project: Malate Dehydrogenase CURE Community
Description: This is the results of the faculty CURE survey given after the faculty taught the course.
Dataset
Part of Project: Malate Dehydrogenase CURE Community
Description: EDAT data from undergraduate students participating in a protein-centric CURE.
Dataset
Part of Project: Malate Dehydrogenase CURE Community
Description: Data are from ~1,500 undergraduate students participating in a protein-centric CURE across 19 institutions. This is the data from Lopatto's CURE survey.
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
Code
Part of Project: Meta-analysis of Educational RCTs with Follow-up
Description: R code for analyses in Hart et al. (2024). See README for Hart et al. (2024) to orient to the organization of data and code.
Code Type: Analysis
