Sarah Quesen is a statistician and psychometrician with experience in educational measurement, applied research, and emerging technologies. She teaches undergraduate courses in the Department of Statistics at the University of Pittsburgh and serves as Director of Assessment Research and Innovation at WestEd, a nonprofit research and development organization focused on improving education and learning outcomes. Her work focuses on test score validity, computational psychometric methods, and the use of machine learning to evaluate measurement invariance and bias. She also leads research on automated scoring and generative AI in test development. Prior to joining WestEd, she was a senior research scientist at Pearson, where she served as lead psychometrician on large-scale assessment programs. Before that, she served on the full-time faculty in the Department of Statistics at West Virginia University for 19 years.
Courses
- STAT 1000: Applied Statistical Methods
- STAT 1100: Statistics and Probability for Business Management
- STAT 0200: Basic Applied Statistics
- PhD, Research Methodology, University of Pittsburgh, 2016
Education & Training
Murphy, D., Quesen, S., Brunetti, M., & Love, Q. (2024). Expected classification accuracy for categorical growth models. Educational Measurement: Issues and Practice, 43(2), 64–73.
Quesen, S., & Lane, S. (2019). Differential item functioning for accommodated students with disabilities: Effect of differences in proficiency distributions. Applied Measurement in Education, 32(4), 337–349.
White, L., Nesbitt, J., Roeters-Solano, H., Quesen, S., et al. (2025). Culturally responsive and sustaining approaches to scoring. In Culturally Responsive Assessment in Classrooms and Large-Scale Contexts.
Brunetti, M., Langi, M., & Quesen, S. (2025). Are We on the Same Page? A Discussion on the Use and Misuse of Early Literacy Assessments. https://osf.io/preprints/osf/ze3qj_v2
Quesen, S., & LeBeau, B. (2025). Pairwise no more: Rethinking bias detection methods for complex intersectional data. Paper presented at NCME, Denver.
- Educational measurement
- Computational psychometric methods
- Novel uses of artificial intelligence in assessment
- Assessment policy and accountability systems
