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Teacher Turnover and Retention in Los Angeles Urban Schools: Two-Level Discrete-Time Survival Analysis
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| Presenter(s):
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| Xiaoxia Newton, University of California Berkeley, xnewton@berkeley.edu
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| Rosario Rivero, University of California, Berkeley, rosario.rivero.c@gmail.com
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| Abstract:
Teacher retention has attracted increasing attention in the research and policy community. While prior empirical literature has provided valuable insights into the factors that shape teacher turnover, there are conceptual and methodological limitations. Our study examined what individual background characteristics and organizational factors are related to teacher retention in urban schools. We conducted two-level discrete-time survival analysis, using a 7-year panel data from the Los Angeles Unified School District (LAUSD). Our findings on factors related to who, at what career stage, teacher quality, subject assignment, and what school context have important implications for policy formulations on teacher retention and for the policy push of using value-added methods for teacher accountability, especially among teachers in Charter Schools. In addition, our analysis provided some evidence on how effort at the LAUSD to improve school facilities might be associated with teacher stability at those schools.
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Using Nonparametric Survival Analysis in Longitudinal Evaluation of Dynamic Activities
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| Presenter(s):
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| Guili Zhang, East Carolina University, zhangg@ecu.edu
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| Abstract:
Time is an important factor in longitudinal evaluation of dynamic activities. Traditional quantitative data analysis techniques have serious limitations when it comes to addressing the time factor in longitudinal evaluation. This proposal illustrates the usefulness and advantages of using nonparametric survival analysis to evaluate undergraduate engineering student retention in the past two decades in the southeastern United States. A large longitudinal database that includes 100,179 engineering students from nine universities and spans 19 years was used, and the nonparametric survival analysis was adopted to obtain nonparametric estimates of survival and associated hazard functions, and complete rank tests for the association of variables. The results of this evaluation study support using survival analysis to better understand factors that affect student success since student retention is a dynamic problem. Survival analysis allows characteristics such as risk to be evaluated by semester, giving insight to when interventions might be most effective.
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