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 Data Mining

Missing Data

Biography:

Dr. Collier is an expert in the field of causal data mining, with a particular focus on advancing latent variable modeling and propensity score analysis. He is also deeply committed to ensuring that valid conclusions can be drawn from datasets that contain missing values. Dr. Collier’s expertise in both causal data mining and missing data analysis has made him a valuable contributor to a wide range of research projects in fields such as special education and public health. His work has helped to improve our understanding of complex causal relationships and ensure that data-driven decisions are based on valid and reliable information. In 2020, he launched the MUDD Lab (Methods for Unstructured and Difficult to use Data).

Vitae

Selected Publications:

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.