Project Activities
People and institutions involved
IES program contact(s)
Products and publications
Journal article, monograph, or newsletter
Dieterle, S., Guarino, C., Reckase, M., and Wooldridge, J. (2015). How Do Principals Assign Students to Teachers? Finding Evidence in Administrative Data and the Implications for Value Added. Journal of Policy Analysis and Management, 34(1): 32-58.
Guarino, C., Maxfield, M., Reckase, M., Thompson, P., and Wooldridge, J. (2015). An Evaluation of Empirical Bayes's Estimation of Value-Added Teacher Performance Measures. Journal of Educational and Behavioral Statistics, 40(2): 190-222.
Guarino, C., Reckase, and Wooldridge, J. (2015). Policy and Research Challenges of Moving Toward Best Practices in Using Student Test Scores to Evaluate Teacher Performance?. Journal of Research on Educational Effectiveness, 8(1): 1-7.
Guarino, C., Reckase, M., and Wooldridge, J. (2015). Can Value-Added Measures of Teacher Performance Be Trusted?. Education Finance and Policy, 10(1): 117-156.
Guarino, C., Reckase, M., Stacy, B., and Wooldridge, J. (2015). Evaluating Specification Tests in the Context of Value-Added Estimation. Journal of Research on Educational Effectiveness, 8(1): 35-59.
Supplemental information
Co-Principal Investigators: Reckase, Mark; Wooldridge, Jeffrey
The project occurred in three phases. Phase 1 included diagnosis, development and demonstration aspects. Under diagnosis, tests of assumptions needed to support inference using VAMs will be developed. These tests address assumptions embedded in both scaling (for example, assumptions of unidimensionality or that year-to-year achievement can be measured through vertical scaling) and VAM estimation strategies (for example, assumptions regarding decay and exogeneity). Under development, the project investigated the use of advanced multidimensional scaling approaches that may more accurately represent the growth in students' achievement and address problems of underestimation of student achievement and teacher effects that have been found with unidimensional item response theory (IRT) models. Under demonstration, evidence of the strengths and weaknesses of different approaches to scaling and VAM estimation were gathered through a series of simulations and the application of the techniques to real data from school districts.
Phase 2 of the project applied the findings from Phase 1 to a state-level data set allowing an investigation of the sensitivity of scaling assumptions and estimates derived from VAMs to various contexts—i.e., across different subpopulations of students. This part of the project operated under the assumption that scaling methods and VAMs differ in their ability to produce causal estimates of performance for teachers and schools serving different types of students. Differences in performance indicators for teachers, schools, and programs were examined across low versus high socioeconomic status students, minority versus white students, urban versus suburban and rural students, and special needs versus general student populations.
Phase 3 of the project focused on the development and dissemination of policy guidelines and recommendations for testing regimes and scaling, data requirements and collection, and estimation methodologies. These recommendations will be adapted to whether teacher, school, or program effects are being considered.
Questions about this project?
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