Models for Value-Added Modeling of Teacher Effects

The use of complex value-added models that attempt to isolate the contributions of teachers or schools to student development is increasing. Several variations on these models are being applied in the research literature, and policy makers have expressed interest in using these models for evaluating...

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Bibliographic Details
Published in:Journal of educational and behavioral statistics 2004, Vol.29 (1), p.67-101
Main Authors: McCaffrey, Daniel F., Lockwood, J. R., Koretz, Daniel, Louis, Thomas A., Hamilton, Laura
Format: Article
Language:English
Subjects:
Charter schools
Correlation
Correlations
Data models
Effectiveness
Elementary schools
Estimates
Learning
Longitudinal Studies
Mathematical Models
Modeling
Multilevel models
Multivariate Analysis
Outcomes of Education
Parametric models
School Effectiveness
Statistical variance
Student Characteristics
Student Development
Students
Studies
Teacher Effectiveness
Teacher Evaluation
Teacher Influence
Teachers
Test scores
Value Added Models
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Summary:The use of complex value-added models that attempt to isolate the contributions of teachers or schools to student development is increasing. Several variations on these models are being applied in the research literature, and policy makers have expressed interest in using these models for evaluating teachers and schools. In this article, we present a general multivariate, longitudinal mixed-model that incorporates the complex grouping structures inherent to longitudinal student data linked to teachers. We summarize the principal existing modeling approaches, show how these approaches are special cases of the proposed model, and discuss possible extensions to model more complex data structures. We present simulation and analytical results that clarify the interplay between estimated teacher effects and repeated outcomes on students over time. We also explore the potential impact of model misspecifications, including missing student covariates and assumptions about the accumulation of teacher effects over time, on key inferences made from the models. We conclude that mixed models that account for student correlation over time are reasonably robust to such misspecifications when all the schools in the sample serve similar student populations. However, student characteristics are likely to confound estimated teacher effects when schools serve distinctly different populations.
ISSN:1076-9986
1935-1054
DOI:10.3102/10769986029001067