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Abstract Details
Activity Number:
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176
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Type:
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Contributed
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Date/Time:
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Monday, August 1, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Social Statistics Section
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Abstract - #302541 |
Title:
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Modeling Score-Based Student Achievement Data with Many Ceiling Values
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Author(s):
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Yan Yang*+
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Companies:
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Arizona State University
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Address:
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School of Math and Stats, Tempe, AZ, 85287-1804,
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Keywords:
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Censoring ;
EM algorithm ;
mixed effects ;
Tobit model ;
value-added modeling
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Abstract:
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The competitive federal Race to the Top grant program relies on standards-based state assessments to measure student achievement and school performance. In a standards-based test, high-performing students may achieve the maximum possible score, suggesting lost information on student learning and potentially penalizing their teachers. We develop a multi-membership Tobit mixed model to explicitly account for score ceilings in value-added assessment of teacher and school effects. A Monte Carlo expectation-maximization (EM) algorithm is implemented for model estimation. Simulation and analysis of data from the Arizona standardized assessment are presented to demonstrate the practical utility of the proposed methods.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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