Person: Willa van Dijk

Professional Titles
Assistant Professor
ORCID
0000-0001-9195-8772
Google Scholar ID
ULLC99kAAAAJ&hl

I am an Assistant Professor in Special Education at Utah State University focusing on preventing reading failure. I am interested in how individual differences in children and teachers influence the effectiveness of reading intervention. I use advanced statistical models and single case research designs to answer my questions and try to share all aspects of my work openly.

Full Name
Wilhelmina van Dijk
Favorite
Thumbnail for Willa van Dijk

Contributions

Dataset: Instructional Coaches Alter level Data (Uploaded on )

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: Instructional Coaches Ego-level data (Uploaded on )

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.

Document: Codebook Instructional Coaches (Uploaded on )

This is the codebook accompanying three data sets for instructional coaches. CoachesEgoClean, CoachesTiesClesn, CoachesAlterClean.

Document: Network Survey Protocol- Instructional Coaches (Uploaded on )

This protocol shows all questions asked during the network survey data collection interviews. The protocol includes notes to proctors, and codebook with labels.

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.

Document: Teacher Survey Data on School RTI Components Codebook (Uploaded on )

This codebook accompanies the data set "Teacher Survey Data on School RTI components, and includes variable name, prompts, data type, and allowable values.

Dataset: Administrator Survey of RTI Implementation (Uploaded on )

These are data from 65 school administrators.

Dataset: Teacher Survey Data on School RTI Components (Uploaded on )

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.

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

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.