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Abstract Details
Activity Number:
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423
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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Social Statistics Section
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Abstract - #306528 |
Title:
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Comparing Propensity Score Methods Using Multilevel Level Modeling: A Monte Carlo Study
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Author(s):
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Aarti P. Bellara*+ and Jeffrey Kromrey and Eun Sook Kim and John M. Ferron and Zorka Karanxha
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Companies:
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University of South Florida and University of South Florida and University of South Florida and University of South Florida and University of South Florida
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Address:
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4202 E. Fowler Ave. EDU 105, Tampa, FL, 33620,
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Keywords:
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Propensity Scores ;
Multilevel Modeling ;
Observational Studies ;
Monte Carlo Studies
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Abstract:
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Recently, there has been an increase in the use of propensity score methods to adjust for selection bias in observational research in health and social sciences. Educational research is often situated within a nested framework, and the use of propensity score methods recently has been extended to clustered data. However, the literature lacks a substantial amount of empirical evidence regarding the effectiveness of propensity score methods applied to nested structures. This study simulated data to investigate the effectiveness of propensity score methods in multilevel studies. Specifically, four different propensity score estimation models and conditioning strategies were compared. Model convergence rates, covariate balance achievement, and estimation of treatment effects were examined. Results indicate propensity score methods should be considered under certain conditions when estimating treatment effects with nested observational data.
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