Person: Willa van Dijk

Professional Titles
Assistant Professor
Google Scholar ID

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
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This code accompanies the preprint Do Student Behavior Ratings Predict Response to Tier 1 Reading Interventions? (10.35542/ 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.

Project: LDbase Integrated Dataset (Uploaded on )

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.

Document: Codebook for Headsprout full data set (Uploaded on )

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.

Dataset: All data from HeadSprout Project (Uploaded on )

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.

Document: Consent form Headsprout study (Uploaded on )

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.

Project: Headsprout Data Analytics (Uploaded on )

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.