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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.
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.
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: 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 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.
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: 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.
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: 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 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
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
Document
Part of Project: Headsprout Data Analytics
Description: This is the original consent form used for the study. It includes the text for the parent consent form. The child assent script is included at the bottom of the document.
Document Type: Other
Document
Part of Project: Headsprout Data Analytics
Description: This is the codebook accompanying the dataset "All data from Headsprout project". It includes all variable names, labels, and label values, as well as information about missingness and summary statistics for each variable.
Document Type: Codebook
Document
Part of Project: Project KIDS
Description: This is a useful figure for new users of Project KIDS to see how the project worked. It lists the original projects, dates, and sample sizes of each component of Project KIDS.
Document Type: Figure
Document
Part of Project: Project KIDS
Description: This file contains all Mplus output for the 23 Mplus models to generate scaled factor scores for PV using MNLFA.
Document Type: Other
Document
Part of Project: Project KIDS
Description: This file contains all Mplus output for the 47 Mplus models to generate scaled factor scores for LWID using MNLFA.
Document Type: Other
