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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: In this study, we wanted to explore the support networks of instructional coaches of beginning special education teachers and those of rural special education teachers, and how these networks relate to intent-to-stay in the field as a coach or special education teachers. We collected data across two waves.
Project
Description: The purpose of this pilot study is to explore the effects of podcast creation on empowerment of pre-service teachers (PSTs). Undergraduate students enrolled in an educator preparation program will create an original podcast episode examining one issue in education.
Project
Description: We will explore the effects of a self-monitoring self-care intervention on the resilience and self-efficacy of pre-service teachers' (PSTs). PSTs enrolled in an education preparation program will be randomly assigned to a control or treatment group. Both groups will participate in a a resilience and stress reduction training.
Project
Description: In this study, we examined if data analytics gleaned from an online literacy application could inform teachers of student reading progress above and beyond their progress monitoring scores. Participants were all K-1 students in one elementary school in a southeastern state.
Project
Description: This project page describes the creation of a latent reading score created by combining data across 12 datasets available on LDBase. This variable is the product of an Integrated Data Analysis (IDA) which was modeled using a multiple-group measurement model described below.
Dataset
Part of Project: Project FOCUS: Exploring RTI Implementation with a Focus on Students Receiving Tier 3 and Special Education
Description: These are data from 65 school administrators.
Dataset
Part of Project: Self-Monitoring Self-Care
Description: These data provide pre and posttest results from the SMSC study.
Dataset
Part of Project: Headsprout Data Analytics
Description: Data from the Headsprout data analytics project. This dataset includes student progress monitoring data (DIBELS, TOWRE, CELF), data analytics from their engagement in Headsprout, and information about their home literacy environment (UFLI Home Literacy survey.
Dataset
Part of Project: Project FOCUS: Exploring RTI Implementation with a Focus on Students Receiving Tier 3 and Special Education
Description: These are data from 1000 teachers and 77 administrators. Data include demographic variables on teachers, and their perceptions of RTI implementation in their school. This is a cross-sectional dataset.
Dataset
Part of Project: Support networks for instructional coaches and special education teachers
Description: These data are ties between alters in the network. These data are needed to visualize the complete network using ego and alter data as well.
Dataset
Part of Project: Support networks for instructional coaches and special education teachers
Description: These data include demographic information on the participating coaches (n = 15) and their coaching context. These data are crosssectional and include specific ego-IDs that can be used to merge with Alter and Tie datasets.
Dataset
Part of Project: Support networks for instructional coaches and special education teachers
Description: These data contain perceived demographic data on named alters (by Ego) and perceived support provision. These data can be combined with Ego level data to make a nested set using key ID variables.
Code
Part of Project: Support networks for instructional coaches and special education teachers
Description: This code includes everything necessary to replicate analyses in the Instructional Coaches Typology paper (see preprint).
Code Type: Analysis
Code
Part of Project: Project KIDS
Description: This code accompanies the preprint Do Student Behavior Ratings Predict Response to Tier 1 Reading Interventions? (10.35542/osf.io/jfxz5). Code is for R version 4.2.1 (2022-06-23) )macOS Montery 12.5
Code Type: Analysis
Code
Part of Project: Project KIDS
Description: This code calculates internal consistency for 6 ISI projects. Including alpha for the total sample, and by project. This code accompanies "Examining differential intervention effects: Do Individualized Student Intervention effects vary by student abilities and characteristics?"
Code Type: Analysis
Code
Part of Project: Project KIDS
Description: This code generates descriptive statistics, Mplus input files for MNLFA models, multiple imputation, and multi-level quantile regression models that accompany the paper "Examining differential intervention effects: Do Individualized Student Intervention effects vary by student abilities and characteristics?"
Code Type: Analysis
Code
Part of Project: Project KIDS
Description: This code takes 4 different datasets generated through the R code (EC_IDA_QR) and runs the MNLFA procedures in Mplus. All aspects of MNLFA are included, CFA, initial measurement variance, calibration, and scoring. Each section should be run separately in Mplus. There are 47 individual Mplus files included.
Code Type: Analysis
Code
Part of Project: Project KIDS
Description: This code takes 4 different datasets generated through the R code (EC_IDA_QR) and runs the MNLFA procedures in Mplus. All aspects of MNLFA are included, CFA, initial measurement variance, calibration, and scoring. Each section should be run separately in Mplus. There are 24 individual Mplus files included.
Code Type: Analysis
Document
Part of Project: Project KIDS
Description: This is the calibration data sample to run the measurement invariance models for PV using MNLFA in Mplus.
Document Type: Other
