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A comparison among major value-added models
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A comparison among major value-added models
SubTitle
a general model approach
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ETD_2410
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000052266
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eng
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theses
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Topic
Education
Subject
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(authority = ETD-LCSH)
Topic
School improvement programs--Mathematical models
Subject
(ID = SBJ-3);
(authority = ETD-LCSH)
Topic
School management and organization--Mathematical models
Subject
(ID = SBJ-4);
(authority = ETD-LCSH)
Topic
Effective teaching
Subject
(ID = SBJ-5);
(authority = ETD-LCSH)
Topic
Educational accountability
Abstract
Value-added models (VAMs) are becoming increasingly popular within accountability-based educational policies as they purport to separate out the effects of teacher and schools from student background variables. Given the fact that evaluations based on the inappropriate use of VAMs would significantly impact students, teachers and schools in a high-stake environment, the literature has advocated empirical evaluations of VAM measures before they become formal components of accountability systems. The VAM label is attached to a number of models, which range from simple to highly sophisticated models. However, in practice, educators and policymakers are often being misled into believing that these approaches give nearly identical results, and making decisions without understanding the strengths and limitations of these models. In addition, the empirical evaluations to date have shown that the VAM measures of teacher effects are sensitive to the form of the statistical model and to whether and how student background variables are controlled.
This study proposes a multivariate joint general VAM to investigate the issues raised by the applications of all the currently prominent VAMs, which can be seen as restricted cases of this general model. The general model provides a framework for comparing the restricted models and for evaluating the sensitivity of VAM measures (e.g., teacher and school effects) to the model choice. Markov chain Monte Carlo algorithm is used in a Bayesian context to implement both the general and the restricted models.
A simulation study was conducted to investigate the feasibility and robustness of the general model when the data were generated under varying assumptions. For each condition, three consecutive years of testing scores were generated for 400 students grouped into 16 classes. Real data consisting of three years of longitudinally linked student-level data from a large statewide achievement testing program were also analyzed. The results show that the proposed general model is more robust than other models to different assumptions and the inclusion of the background variable has significant impact on some models when the school/class has an unbalanced mix of advantaged and disadvantaged students.
This study proposes a multivariate joint general VAM to investigate the issues raised by the applications of all the currently prominent VAMs, which can be seen as restricted cases of this general model. The general model provides a framework for comparing the restricted models and for evaluating the sensitivity of VAM measures (e.g., teacher and school effects) to the model choice. Markov chain Monte Carlo algorithm is used in a Bayesian context to implement both the general and the restricted models.
A simulation study was conducted to investigate the feasibility and robustness of the general model when the data were generated under varying assumptions. For each condition, three consecutive years of testing scores were generated for 400 students grouped into 16 classes. Real data consisting of three years of longitudinally linked student-level data from a large statewide achievement testing program were also analyzed. The results show that the proposed general model is more robust than other models to different assumptions and the inclusion of the background variable has significant impact on some models when the school/class has an unbalanced mix of advantaged and disadvantaged students.
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electronic resource
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xiv, 108 p. : ill.
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Ph.D.
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Includes bibliographical references (p. 104-107)
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by Yuan Hong
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Hong
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Yuan
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Yuan Hong
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de la Torre
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Jimmy
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chair
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Advisory Committee
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Jimmy de la Torre
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Camilli
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Gregory
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Gregory Camilli
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Bruce
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Advisory Committee
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Bruce Baker
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Yao
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Lihua
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Lihua Yao
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Rutgers University
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Graduate School - New Brunswick
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2010
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2010-01
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xx
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Rutgers University Electronic Theses and Dissertations
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ETD
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Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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doi:10.7282/T3J38SQ2
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ETD doctoral
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Rights
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The author owns the copyright to this work.
Copyright
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Copyright protected
Notice
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Open
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Permission or license
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Hong
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Yuan
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2010-01-06 11:52:10
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Yuan Hong
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Rutgers University. Graduate School - New Brunswick
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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730 days
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