Propensity Score Analysis with Nested Data: Comparing Single and Multilevel Model Estimates

• Activity Number: 423
• Type: Contributed
• Date/Time: Tuesday, July 31, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Social Statistics Section
• Author(s): Patricia Rodriguez De Gil and Rheta E. Lanehart*+ and Aarti P. Bellara and Jeffrey Kromrey and Eun Sook Kim and Kathryn M. Borman and Reginald S. Lee
• Companies: University of South Florida and University of South Florida and University of South Florida and University of South Florida and University of South Florida and University of South Florida and University of South Florida
• Keywords: Propensity scores ; hierarchical linear modeling ; observational studies

Abstract

Propensity score (PS) methods provide viable strategies for reducing selection bias in nonexperimental(observational) studies. Most research on PS methods model the treatment assignment so that the estimated probability of receiving treatment allows for the identification of comparable individuals based on their individual characteristics. However, in nested data structures selection bias might result not only from differences in the characteristics of the individuals but also from differences in group membership. This study investigated differences in PS results from single-level and multi-level models. Data from an NSF funded project included school transcripts, demographics, enrollment, and achievement data. The impact of special educational programs on advanced mathematics course enrollment was investigated. Data were analyzed by comparing PS distributions, estimating the correla