Publications – Causal Inference and Quasi-Experimental Designs

BOOK: Leite, W. L. (2016). Practical Propensity Score Methods Using R. Thousand Oaks, CA: Sage Publishing.

Leite, W. L., Jing, X., Kuang, H., Kim, D. & Huggins-Manley, A. C. (2021). Multilevel mixture modeling with propensity score weights for quasi-experimental evaluation of virtual learning environments. Structural Equation Modeling. https://doi.org/10.1080/10705511.2021.1919895

Leite, W. L., Aydin, B., & D. D. Cetin-Berber (2021). Imputation of Missing Covariate Data Prior to Propensity Score Analysis: A Tutorial and Evaluation of Robustness of Practical Approaches. Evaluation Review. https://doi.org/10.1177/0193841X211020245

Code for the paper

Collier, Z. K., & Leite, W. L. (2021). A Tutorial on Artificial Neural Networks in Propensity Score Analysis. Journal of Experimental Education. DOI: 10.1080/00220973.2020.1854158

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, DOI: 10.1080/03610918.2021.1963455

Collier Z. K., Leite W. L., Karpyn A. (2021). Neural Networks to Estimate Generalized Propensity Scores for Continuous Treatment Doses. Evaluation Review. doi:10.1177/0193841X21992199

Leite, W. L., Stapleton, L. M., & Bettini, E. F. (2019). Propensity Score Analysis of Complex Survey Data with Structural Equation Modeling: A Tutorial with Mplus. Structural Equation Modeling: A Multidisciplinary Journal, 26:3, 448-469, DOI: 10.1080/10705511.2018.1522591

Aydin, B., Algina, J., & Leite, W. L. (2019). Comparison of Model and Design-Based Approaches to Detect the Treatment Effect and Covariate by Treatment Interactions in Three-Level Models for Multi-site Cluster Randomized Trials. Behavior Research Methods. 51, 1, 243-357. DOI: 10.3758/s13428-018-1080-1

Bishop, C. D., Leite, W. L., Snyder, P. (2018). Using Propensity Score Weighting to Reduce Selection Bias in Large-Scale Data Sets. Journal of Early Intervention, 40(4), 347-362.

Leite, W. L., Aydin, B. & Gurel, S. (2019) A Comparison of Propensity Score Weighting Methods for Evaluating the Effects of Programs with Multiple Versions. The Journal of Experimental Education, 87(1), 75-88. DOI: 10.1080/00220973.2017.1409179

Gage, N., Leite, W. L., Childs, K. & Kincaid, D. (2017). Average Treatment Effect of School-wide Positive Behavior Supports (SWPBIS) on School-Level Academic Achievement in Florida. Journal of Positive Behavior Interventions. 19(3), 158-167.

Barnes, T., Leite, W. L., & Smith, S. (2017). A Quasi-Experimental Analysis of School-Wide Violence Prevention Programs. Journal of School Violence, 16, 49-67.

Aydin, B., Leite, W. L, & Algina, J. (2016). The effects of including observed means or latent means as covariates in multilevel models for cluster randomized trials. Educational and Psychological Measurement, 76, 803–823.

Leite, W. L., Jimenez, F., Kaya, Y., Stapleton, L. M., MacInnes, J. W., & Sandbach, R. (2015). An evaluation of weighting methods based on propensity scores to reduce selection bias in multilevel observational studies. Multivariate Behavioral Research. 50, 265–284.

Leite, W. L. (2015). Latent growth modeling of longitudinal data with propensity score matched groups In Wei Pan, & Haiyan Bai. Propensity Score Analysis: Fundamentals, Developments, and Extensions, (pp. 191-216.) New York: Guilford.

Leite, W. L., Sandbach, R., Jin, R., MacInnes, J., & Jackman, G. A. (2012). An Evaluation of Latent Growth Models for Propensity Score Matched Groups. Structural Equation Modeling. 19, 437–456.

Shi, Y., Leite, W. L., & Algina, J. (2010). The impact of omitting the interaction between crossed factors in cross-classified random effects modeling. British Journal of Mathematical and Statistical Psychology.63, 1-15.