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Session Type: Roundtable Session
This session focuses on analyzing observational data when matching of samples or selection of variables are necessary to make sound statistical inferences. Different methods were discussed and compared when various issues occurred in practical settings. Helpful recommendations and pros and cons were discussed as well.
Difference-in-Differences, Propensity Score Matching, and Propensity Score Weighting in School-Level Natural Experiments - Peter Boedeker, Baylor College of Medicine
Propensity Score Analysis With Mismeasured Covariates - Huibin Zhang, University of Tennessee
The Effect of Lack of Measurement Invariance in Propensity Score Analysis With Latent Variables - Yongseok Lee, University of Florida; Walter L. Leite, University of Florida
What's in a Level? Investigating Propensity Score Methods for Effects on Hierarchical Linear Modeling Treatment Estimates - Alexandra Lane Stone, University of Connecticut - Storrs; Carla Evans, Center for Assessment
Statistical Inference After Variable Selection via Penalized Regression: Focusing on Variables Predicting Belonging to School - Jin Eun Yoo, Korea National University of Education; Minjeong Rho, Korea National University of Education