01959nam a22002057a 4500 20240508071140.0 240508b |||||||| |||| 00| 0 eng d 0022-0973 Lee, Yongseok Propensity Score Matching with Cross-Classified Data Structures Walter : A Comparison of Methods (Journal Article) Philadelphia, USA : Taylor and Francis Group and Routledge ,May 2024 359-376p. The Journal of Experimental Education Volume 92: Number 2, 2024 ***______{For Hard Copy, Please visit Library.}________*** Abstract: In the current study, we compare propensity score (PS) matching methods for data with a cross-classified structure, where each individual is clustered within more than one group, but the groups are not hierarchically organized. Through a Monte Carlo simulation study, we compared sequential cluster matching (SCM), preferential within cluster matching (PWCM), greedy matching (GM), and optimal full matching (OFM), using propensity scores from four different models. The results indicated that the four matching methods performed well when PSs were estimated with logistic regression containing both level-1 and level-2 covariates. When the level-2 covariates were omitted in the logistic regression PS model, matching methods resulted in biased treatment effect estimates. However, omission of level-2 covariates did not result in biased estimates when the PS model was a logistic cross-classified random effects model (CCREM). SCM and PWCM outperformed GM and OFM with a logistic CCREM that included level-1 and level-2 covariates. Cross-classified data| cross-classified random effects model| one-to-many nearest neighbor matching| propensity scores| sequential cluster matching Leite, L. | Leroux, Audrey J. https://doi.org/10.1080/00220973.2023.2164843 PER 0 0 0 0 RIEBPL RIEBPL 2024-05-08 0 2024-05-08 07:11:58 2024-05-08 PER 45756 45755