A Monte Carlo Simulation Comparing the Statistical Precision of Two High-Stakes Teacher Evaluation Methods: A Value-Added Model and a Composite Measure.
Year: 2016
Type: dissertation
Abstract: Value-added models are a class of growth models used in education to assign responsibility for student growth to teachers or schools. For value-added models to be used fairly, sufficient statistical precision is necessary for accurate teacher classification. Previous research indicated precision below practical limits. An alternative approach has been explored in which value-added models are incorporated into composite measures of teacher quality alongside subjective indicators. The aim of the current research was to explore the relative precision of the two evaluative approaches using simulated data. It was found that the composite measure produced fewer classification errors than the stand-alone value-added model. The magnitude of the reduction was largest when sample sizes were small, when composite indicators were highly correlated, and when using conservative alpha levels for hypothesis testing. The magnitude of the difference shrank as an increasing number of aggregated evaluation cycles were incorporated into the evaluation, when composite indicators were poorly correlated, and when less conservative alpha levels were used. Implications for this research are mixed, but there is tentative evidence that there is a tradeoff between precision and resource expenditure when comparing stand-alone value-added versus composite evaluative models.
Author: Bryden Michael Spencer
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W2606642843