Daniel Bolt

dbolt
Daniel Bolt
Nancy C. Hoefs-Bascom Professor of Educational Psychology, School of Education,
University of Wisconsin–Madison

Daniel Bolt is the Nancy C. Hoefs-Bascom Professor of Educational Psychology in the School of Education at the University of Wisconsin–Madison, where he specializes in quantitative methods. His research focuses on the theory and application of psychometric methods in the educational, social, and health sciences, including latent variable models for measurement validation, computational methods, novel item formats, computer-based testing, response styles, and student growth modeling. More recently, his work has explored the intersection of educational data science and psychometrics. Bolt has served as president of the Psychometric Society and is a biostatistician at the Waisman Center. His research has been published in Psychometrika, Psychological Methods, Journal of Educational and Behavioral Statistics, and other leading journals. He is a recipient of the Kellett Mid-Career Award, the Vilas Associates Research Award, and the Chancellor’s Distinguished Teaching Award from the University of Wisconsin–Madison. Bolt earned his PhD in psychology and quantitative methods from the University of Illinois at Urbana–Champaign.

updated 2025

Contributing topics
Supporting students’ social-emotional, mental & physical health
Contributing initiatives
CORE-PACE Research Partnership
Publications by Daniel Bolt
An IRT Mixture Model for Rating Scale Confusion Associated with Negatively Worded Items in Measures of Social-Emotional Learning
An IRT Mixture Model for Rating Scale Confusion Associated with Negatively Worded Items in Measures of Social-Emotional Learning
This article assesses confusion due to negative wording on social-emotional learning (SEL) assessments using mixture IRT models. Results based on student self-reports show confusion biasing scores, especially in Grades 3-5, and correlational…
An IRT Mixture Model for Rating Scale Confusion Associated with Negatively Worded Items in Measures of Social-Emotional Learning
This paper uses mixture IRT models to evaluate whether students experience confusion due to negative wording of items on a social-emotional learning assessment. It examines the consequences of confusion on student- and school-level scores and…