Methodological Articles.

Collier, Z. K., Chawla, K. & Soyoye, O. (2024) Optimizing Imputation for Educational Data: Exploring Training Partition and Missing Data Ratios, The Journal of Experimental Education, DOI: 10.1080/00220973.2023.2287447

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.

May, H., & Collier, Z. (2023). Nonequivalent comparison group designs. In APA Handbook Of Research Methods In Psychology. (Second Edition).

Collier, Z. K., Zhang, H., & Soyoye, O. (2022). Alternative methods for interpreting Monte Carlo experiments. Communications in Statistics-Simulation and Computation, 1-16.

Collier, Z. K., Zhang, H., & Liu, L. (2022). Explained: Artificial intelligence for propensity score estimation in multilevel educational settings. Practical Assessment, Research, and Evaluation, 27 (1), 3.

Collier, Z. K., Leite, W. L., & Karpyn, A. (2021). Neural networks to estimate generalized propensity scores for continuous treatment doses. Evaluation Review, 0193841X21992199.

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. https://doi.org/10.1177/0193841X211065619

Collier, Z. K., Leite, W. L., & Zhang, H. (2021). Estimating propensity scores using neural networks and traditional methods: a comparative simulation study. Communications in Statistics-Simulation and Computation, 1-16.

Collier, Z. K., & Leite, W. L. (2020). A tutorial on artificial neural networks in propensity score analysis. The Journal of Experimental Education, 1-18.

Leite, W., & Collier, Z. (2018). Data mining. In B. Frey (Ed.), The SAGE encyclopedia of educational research, measurement, and evaluation (Vol. 1, pp. 459-461). SAGE Publications, Inc., https://www.doi.org/10.4135/9781506326139.n181

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.