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Abstract Details
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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 - #301514 |
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Title:
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Exploring Missing Data in Value-Added Models in Education
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Author(s):
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Andrew Karl*+ and Yan Yang and Sharon Lohr
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Companies:
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Arizona State University and Arizona State University and 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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EM algorithm ;
generalized linear mixed model ;
joint model ;
missing not at random ;
multi-membership model
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Abstract:
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Value-added models are used by many states to assess contributions of individual teachers and schools to students' academic growth. There is concern, however, that missing data can bias those assessments. A joint, correlated random effects model is developed that extends the generalized persistence value-added model (Mariano et al., 2010) to include missingness as a function of teacher and student covariates and latent effects. Computational issues are discussed and the model is applied to data from calculus classes at a large public university.
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