Zachary K. Collier

Assistant Professor

Educational Psychology

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Titles:

Assistant Professor

Research Methods, Measurement and Evaluation

Academic Degrees:

Ph.D., Measurement and Statistics, University of Florida, 2018
M.S., Measurement and Statistics, University of Florida, 2015
B.S., Special Education, Winthrop University, 2013

Areas of Expertise:

Causal Machine Learning

Structural Equation Modeling

Biography:

Dr. Collier is a quantitative methodologist whose research brings together educational measurement, data science, and critical perspectives to advance more equitable and credible approaches to data analysis. His work focuses on integrating causal machine learning and structural equation modeling while maintaining a strong emphasis on fairness, data ethics, and methodological clarity. Dr. Collier introduced the concept of CritSEM (Critical Structural Equation Modeling), a framework that reimagines traditional modeling techniques through a justice-centered lens by examining how constructs are shaped by social and structural inequality. With over 30 peer-reviewed publications, substantial external funding exceeding $9 million, and national recognition as an Emerging Scholar and Featured Mathematician, Dr. Collier has built a research program that contributes both methodological depth and real-world relevance across education, public health, and the social sciences. In 2020, he launched the MUDD Lab (Methods for Unstructured and Difficult to use Data).

Vitae

Selected Publications:

Collier, Z. K., Sukumar, J., Cao, Y., & Bell, N. (2024). Signaling Model Misspecification in Latent Class Analysis. Structural Equation Modeling: A Multidisciplinary Journal, 1-8.

Bell, N. S., Collier, Z., Vélez, V. N., & Ford, D. Y. (2024). CritSEM: Advancing QuantCrit to Examine Racialized Resegregation in Special Education. Journal of Research on Educational Effectiveness, 1-33.

Collier, Z., Sukumar, J., & Barmaki, R. (2024). Discovering Educational Data Mining: An Introduction. Practical Assessment, Research, and Evaluation, 29(1).

Collier, Z. K., Chawla, K., & Soyoye, O. (2023). Optimizing Imputation for Educational Data: Exploring Training Partition and Missing Data Ratios. The Journal of Experimental Education, 1-21.

Collier, Z. K., Kong, M., Soyoye, O., Chawla, K., Aviles, A. M., & Payne, Y. (2023). Deep Learning Imputation for Asymmetric and Incomplete Likert-Type Items. Journal of Educational and Behavioral Statistics, 10769986231176014.

Collier, Z. K., & Leite, W. L. (2022). A tutorial on artificial neural networks in propensity score analysis. The Journal of Experimental Education, 90(4), 1003-1020.

Collier, Z. K., Zhang, H., & Liu, L. (2022). Explained: Artificial Intelligence for Propensity Score Estimation in Multilevel Educational Settings. Practical Assessment, Research & Evaluation, 27, 3.

Collier, Z. K., Zhang, H., & Johnson, B. (2021). Finite mixture modeling for program evaluation: Resampling and pre-processing approaches. Evaluation Review, 45(6), 309-333.

Collier, Z. K., & Leite, W. L. (2017). A comparison of three-step approaches for auxiliary variables in latent class and latent profile analysis. Structural Equation Modeling: A Multidisciplinary Journal, 24(6), 819-830.