My interests include mixed effects models, survey development and evaluation through Rasch/IRT analysis, and causal inference in observational studies. My second dissertation (December, 2019) looked at a sample of Canadian and U.S. students’ mathematics performance on the Programme for International Student Assessment (PISA) exam. Through the uses of simulated PISA 2015 data, the study compares propensity score techniques with other flexible modeling approaches, such as Bayesian Additive Regression Trees (BART; Hill, 2011) and coarsened exact matching (CEM; Iacus et al., 2012), to determine which methods best reduces bias, balances the pre-treatment variables in the control and treatment groups, and provide robust standard errors. I work at the Center for Decision Research at the University of Chicago’s Booth School of Business.
I have completed a Master's and Ph.D. degree in Measurement, Evaluation, Statistics, and Assessment (MESA) from the University of Illinois at Chicago. Recent research interests include multilevel mediation analysis and deep learning (ANNs, CNNs, RNNs, LSTMs) for image classification, object detection, and textual analysis in Python and Matlab. I also enjoy working with predictive modeling/machine learning techniques.